Dave Murphy, Head of Financial Services EMEA & APAC at Publicis Sapient, on why retail banking is at an important crossroads and must react
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Retail banking stands at a pivotal juncture. As digital-first generations reshape customer expectations and competitive pressure from FinTechs and neobanks intensifies, traditional banks face a critical choice: modernise now or risk obsolescence. Publicis Sapient’s latest Global Banking Benchmark Retail Banking Report underscores that “digital by default” is no longer an aspiration. It’s an immediate necessity.
Drawing on insights from 600 retail banking executives across 13 countries, the report highlights a convergence of transformative forces… The accelerated adoption of Gen AI, the decline of legacy IT infrastructure, and an urgent need to reimagine customer engagement for a younger, mobile-first demographic.
Digital or Die: A Defining Moment
Retail banking has been evolving for over two decades, but the stakes have never been higher. In Q1 2025, JPMorgan Chase reported a net income of $14.6 billion, up 9% year-over-year. This was driven by robust trading revenues and investment banking fees. Meanwhile, UK neobanks are making significant strides. Revolut achieved a net profit of $1.0 billion in 2024, marking its first billion-dollar annual profit, with revenues soaring 72% to $4.0 billion. Monzo also reported its first full year of profitability, posting a pre-tax profit of £15.4 million and doubling its revenue to £880 million.
Despite these advancements, 62% of retail banking executives admit their pace of transformation lags behind competitors. This isn’t a minor delay – it’s a strategic disadvantage in a market where 44% of new currents accounts are already being opened with digital banks and FinTechs.
Gen AI: Catalyst and Compulsion
Among all the changes underway, generative AI has emerged as the most powerful and potentially disruptive force. According to the benchmark study, data and AI are the top investment areas for digital transformation over the next three years. Executives are betting big on AI not only to improve customer engagement but also to modernise operations and accelerate core transformation. The impact of Gen AI in banking is tangible. It can:
Personalise customer journeys at scale
Accelerate software development lifecycles
Write code and automate data management
Deliver hyper-relevant product recommendations
Power AI agents with human-like customer service abilities
In short, Gen AI makes what was once prohibitively expensive and time-consuming not only possible but scalable.
The banking customer has changed
The report makes it clear: retail banks must stop building for yesterday’s customer. Gen Z, who will make up one-third of the workforce by 2030, already prefer mobile-first, always-on banking. They value immediacy, customisation, and authenticity. A staggering 83% of Gen Z consumers say they are frustrated with current bank processes.
Compounding this generational shift is the growing irrelevance of traditional customer segmentation. Today’s consumers defy linear categorisation. The same individual can be a small business owner, a parent, and a new homeowner. Yet banks often treat them as three separate customers because of product-centric data silos.
The core problem with legacy thinking
Legacy systems continue to be the biggest barrier to meaningful transformation. 70% of banking executives say their legacy infrastructure is hindering their ability to deliver the digital experiences customers expect. Many core systems are COBOL-based and nearing end-of-life. Yet banks are reluctant to modernise due to perceived risk and complexity.
The irony is clear: the risk of maintaining outdated systems now outweighs the risk of change. With Gen AI, banks finally have the tools to confront the 800-pound gorilla in the room – core modernisation.
Why Core Modernisation is the linchpin
Modernising the core is about more than infrastructure. It’s the key to unlocking the full value of AI, data, and digital transformation. A modern, cloud-native core enables:
Real-time access to first-party and third-party data
Agile delivery through microservices
Better governance and regulatory transparency
Faster go-to-market with new apps and services
Retail banks that modernise their core can stop building costly middleware just to access data. Instead, they gain a unified view of the customer and the agility to respond to banking market shifts in real time.
The virtuous cycle of AI and Core
What’s truly powerful is the feedback loop between Gen AI and a modernised core. Gen AI helps accelerate the core transformation by generating code, automating testing, and streamlining documentation. Once modernised, that core then enhances Gen AI’s capabilities with clean, structured data. This virtuous cycle creates exponential value, making digital transformation faster, cheaper, and more sustainable.
Retail banks are already allocating 35% of their customer experience digital transformation budgets to Gen AI. Furthermore, many are embedding AI across the entire software development lifecycle using tools like Sapient Slingshot to reduce human error, increase test coverage, and ship better code faster.
From Product-Centric to People-Centric banking
Ultimately, the report urges retail banks to shift from a product-centric to a people-centric mindset. That means designing experiences around life moments, not product categories. It means knowing that the mortgage customer is also a small business owner and a parent, and offering solutions that reflect that reality.
With modern core systems and Gen AI, banks can personalise outreach, tailor financial advice, and meet customers where they are. This holistic view is essential not only for growth but also for loyalty.
The era of deferral is over. Banks can no longer afford to delay core transformation. Gen AI has lowered the cost, reduced the complexity, and increased the speed of change. The only question left is whether banks are ready to lead or risk falling behind.
Publicis Sapient is working at the intersection of Gen AI and core modernisation every day… Helping banks link strategy to execution and deliver on the full promise of digital transformation. The future of retail banking isn’t coming – it’s already here. The time to act is now.
Vikas Krishan, Chief Digital Business Officer & Head of EMEA at Altimetrik, on the disruptive power of AI in FinTech
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AI is already disrupting every area of the Financial Services Industry, and is being included in almost every strategic conversation around technology-enabled transformation. This transformation is exemplified by industry leaders like JP Morgan Chase. CEO Jamie Dimon has championed a £12 billion annual investment in data and technology, overseeing over 400 AI use cases. These include fraud detection, customer service improvements and operational efficiencies across the bank. The core platforms underpinning the industry risk buckling under the weight of modernisation. AI is gradually loosening the components of legacy institutions and presenting fresh opportunities. These are scalable, resilient and adaptable to the agile needs of Financial Services. Through this reimagining of core platforms, those who choose to act now can expect to leapfrog their competition. Meanwhile, those who fail to act now risk obscurity, lack of productivity and being disregarded by their consumer base.
The transition to new architectures
For decades, banks have relied on legacy systems to power their core operations. These often ageing platforms are becoming increasingly difficult and expensive to maintain. They have been built both in languages not commonly used and architected with a different business reality in mind. Many frequently lack the flexibility required to meet the demands of today’s digital-first customers. They also struggle to integrate with modern financial technologies. A significant challenge facing organisations is the accumulation of technical debt. There is a cost to additional work or rework caused by choosing quick or limited solutions over more robust, maintainable approaches. Over time, this can lead to significant issues that compound the challenges of legacy systems.
This lack of nimbleness is often the byproduct of a Frankenstein approach to architectural systems. Many financial institutions have traditionally built new features or attempted to fuse together two platforms. This is a delicate balancing act, requiring extensive planning and careful execution. If done with limited oversight, challenges can arise. These include operational disruptions, increased security risks and obvious incompatibility issues. The high risks and cost burdens associated with maintaining legacy platforms has led many banks to reconsider traditional merger approaches. Increasingly opting for modern, cloud-based microservices driven solutions that offer enhanced scalability, security and integration potential.
Meeting the challenge
As the industry establishes governance around this necessary transition, core platforms are being replaced by newer, more adaptable microservice-based architectures. Navigating this evolution requires leveraging an industry partner with a deep understanding of the complexities and risks involved. There are challenges moving from monolithic core systems to flexible, modern frameworks.
If we think back five years or so, many players in the market were already aware of this critical shift. Companies like Misys and Avaloq were acquired by private equity firms and given substantial investment to advance digital initiatives, developing solution suites. The reason for this was clear, everyone understood the market was changing. However, the challenge still remains in managing the migration of large, complex platforms. The key question has always been how to de-risk these migrations when moving to newer architectures. This is an issue across organisations, and it is something that we at Altimetrik actively work with clients in financial services to address.
Data first with AI
If we consider platforms such as core banking or payments systems, the data generated from these transactions should, in theory, hold value. However, gaining insights from legacy platforms is significantly more challenging and the cost of extracting and utilising that data is often prohibitive. It is here that a data-driven approach to AI must be agreed upon.
High-quality, accurate data lies at the core of every successful AI implementation. AI thrives on data; the more precise the data, the better the AI can learn and provide reliable insights. This fundamental truth highlights the importance of data integrity within the AI ecosystem. However, many financial institutions are struggling in this area, both in effectively using internal data and leveraging accurate, timely external data. As companies grow, their data environments become increasingly complex, adding to these challenges.
As financial services organisations expand, they often face the challenge of data silos, declining data quality and scattered, disconnected data repositories. This leads to a fragmented data ecosystem. It can limit AI’s potential to deliver meaningful insights and drive improvements. This transformation requires active leadership from the top. Successful digital transformation depends on executive-level commitment and understanding. Leaders like Charles Scharf of Wells Fargo demonstrates how CEO ownership of data and AI initiatives drives organisation-wide adoption and success. Their hands-on approach ensures these technologies aren’t just IT projects, but core business strategy enablers.
A Single Source of Truth with AI
To overcome this, financial institutions should establish a Single Source of Truth (SSOT) and in doing so move away from older, somewhat clumsy core platforms. An SSOT will provide a unified, consistent view of data across the organisation. This accelerates decision-making with greater confidence. As demonstrated by successful implementations across the industry. For exmple, Bank of America’s AI-powered virtual assistant Erica providing personalised financial advice to Wells Fargo’s modernised data infrastructure. This enables enhanced risk assessment and management. By centralising core data, an SSOT enables the identification of operational inefficiencies, better monitoring of customer behaviours and effective execution of strategies to foster growth.
The key question is how to successfully de-risk this transition from a fixed cost base to a more flexible, agile one. This transition is essential for becoming an outcomes-focused business with greater adaptability. So, how can technology help achieve this?
One approach involves what is often (unfortunately) referred to as a Strangler Pattern. Instead of a wholesale shift from one platform to another, this modulated approach guides clients on a journey that focuses on gradually moving specific functionalities. By decomposing the legacy system function by function, we rebuild each component within the new platform. This allows the old system to run in parallel until fully replaced. Thus shrinking the monolithic structure in a manageable, low-risk way. It is a method preferred by many large financial services players when they move to become digital businesses.
By working within a digital business methodology that prioritises outcomes over technology, we gain significant advantages. The beauty of this function is its flexibility. When implementing a new function, the management of a FS firm may discover it isn’t meeting expectations or fulfilling business needs. And yet these clients still have the security of the old platform to fall back on and can easily revert back to the original system and refine the new function before trying again. This way of working ensures a safety net. It can reduce risk and enable iterative improvements without causing major disruptions to business operations.
The full picture
The transformation of core platforms through AI presents both immense opportunity and significant challenges. Those institutions willing to embrace this change, adopting data-first approaches and modern architectures, are poised to redefine the industry landscape. The transition, whilst complex, can be managed through measured strategies allowing for gradual, low-risk modernisation. As we move forward, the success of financial institutions will increasingly hinge on their ability to harness AI’s potential. They will need to create unified data ecosystems and adapt to the evolving needs of the digital age. Financial services businesses must embrace AI and modernise their core platforms or risk becoming as obsolete as a floppy disk.
Sofia Kyriakopoulou, Chief Data & Analytics Officer at SCOR on how GenAI is driving InsurTech innovation at leading reinsurer SCOR
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Sofia Kyriakopoulou, a Fintech Strategy AI Champion and Group Chief Data & Analytics Officer at SCOR, spoke at InsurTech Insights revealing how GenAI innovation at one of the world’s largest reinsurers is transcending the realm of proof of concepts to become fully productive…
SCOR is as a tier one reinsurer – adaptable and business oriented. We are nimble, deeply technical and very focused on where and when we can play. We have an ambition to grow with our clients and see AI as the differentiator to allow us to innovate, offer new services, and to increase insurability. At SCOR, AI is not the future ambition. It’s here and now in InsurTech.
Delivering business value at scale with AI
In my role as SCOR’s Group Chief Data Analytics Officer I don’t just oversee data and AI initiatives, I aim to ensure they deliver business value at scale. Doing an AI proof of concept is relatively easy. Getting this into the hands of the user, that’s hard. And that’s what we strive to do at SCOR today. I want to give to you a glimpse of our approach and to share with you how we’re building the insurer of the future.
Since GenAI came into our lives in May 2022 we have all been following the frenzy of its unprecedented rise. It has the potential to change the way we work. And there are very few places where this potential is more relevant than insurance. We have ample data that has never been touched by digitalisation… The submissions, the contracts, the statements of accounts and so on. All of us who have been in data science who have tried the traditional models, we have seen the pain. The annotations, the laborious testing validation. And finally, if we could get it to work, it would’ve been so hard to scale it throughout the lines of business from the markets. Then came GenAI. And by now, many of us have figured out what it can do and what we would like to achieve with it.
GenAI can summarise what treaties look like across their addendums in agreements. And it can do more… Take out pieces of information and fill in my template, fill up my IT system, fill up my database. In the end, it’s not about what the technology does, it’s about what we do with it. And at the core we are focusing the opportunities in two places. Number one, the processes where it could create massive efficiencies. Number two, the new data points that could augment our analytics. And once we combine that with deep industry expertise, we believe that’s when we can go beyond pure automation.
Data in the DNA
At SCOR data is in our DNA. We’re a very technical company full of high calibre individuals and we’re growing. We see AI as a differentiator to be able to do more with the most precious piece of capital that we have – our people. So, we are embedding APIs where we believe it matters most. First of all, with our workforce. AI-powered tools are a commodity, but they’re essential. We are equipping our personnel with secure access to third party tools – essential to increase their effectiveness and efficiency. That’s where the value starts to arise – the processes. Identifying those document intensive processes where adding AI could significantly expedite them. Getting the data points for analytics that could make our decisions faster and more efficient. And that’s where it becomes really interesting, as we contemplate the jewels of the crown that we could be building.
AI-Powered InsurTech Underwriting
We believe differentiation comes from AI-powered underwriting and claims solutions. Through SCOR’s digital solutions, we’re coupling our internal knowhow with AI models to create an advantage for us as we use them internally and for our clients. We want to make the AI-powered underwriting process real when an application or submission comes in and triggers the engine to go straight to process. A certain percentage of cases work like that. Currently, if an underwriting repairer is needed the human looks at that. If we don’t have sufficient evidence that will need to be attained for the process to be reviewed again. Then we can decide what’s wrong with it. There are issues we can overcome… Number one – always back and forth with delays. Number two is human judgment – humans are not very consistent. And number three – we’re missing all the insights that we could have brought in from the past evidence. So, could we do better? Yes, we could add AI on all those human steps and augment them. We could do that. But could we look at it differently?
Could we think of this not sequentially, but at once, synthesising all the necessary data points when they’re needed. Looking at it again, we take evidence, we apply AI, we are structuring essential elements out, we’re triggering the underwriting rule engine, and then we’re adding any further information we have available that could support the decision making. Finally, we’re recommending to the underwriter what they should do. And to signify that this is supporting the underwriter, it’s an underwriting system and the small automation that could happen upstream. This is exactly what we’re currently using internally to augment life and health to support our underwriters.
SCOR’s AI Assistant
Our AI-powered underwriting capability is something we can provide to our clients through SCOR’s digital solution; we call it the AI Assistant. And here’s what it does in practice. When applications come in, we select the chain of thought that we’ll apply. For example, we ask it to think like a medical underwriter. It then extracts the essential pieces from the medical reports, joint records and vital family history. And then it creates a digital twin – the standardised pieces of information that the underwriters believe are the essential data points.
We store them and then we go deeper. We are putting the human in the loop so that humans can validate the actual sources of information. And then we complete the decision making. For example, the AI Assistant could detect an impairment and suggest the next course of action. This signifies the direction of sale. That’s the gold standard that we want to strive for.
Scaling AI with InsurTech
We don’t stop at experimentation. Data scientists like me, we love the tip of the iceberg. That’s where it’s exciting, and you can push to get the proof of concept to work. But in fact, under the water lies the very hard work one has to do… The building up of a data foundation; putting all the essential data assets together at the level of data quality that we can trust; establishing the necessary governance and then developing the IT platform in an equally robust way so it can scale. The proof of concept is not just an experiment.
We must plug in the actual IT landscape, the InsurTech tools where the AI is going to be consumed. And then you can go deeper and link the processes with the humans… In order to positively disrupt the process and keep the human in the loop they must be part of the journey from day one. We must educate our teams, demystify what AI is and isn’t. We must listen to their reactions because they’re the ones we will rely on to elevate the model performance.
Meeting the gold standard with InsurTech
Effective change management is for me, the essential element to allow us to go end-to-end. With insurance, and reinsurance, I believe we have come a long way. From the underwriting manuals to the rule engines, to the first AI models, probably now to the first cracking of the notorious submissions… It has been such a journey transforming both technology and the way we work. The shift, however, is beyond technology. It’s about how we operate, how we innovate, and how we create value for us and for our clients. Today, thanks to our ability to be nimble and technical at SCOR, we are in a position to connect all of the new capabilities of this value chain into what is an end-to-end comprehensive risk view. And for me, that’s the gold standard for InsurTech and what we are striving for with this AI revolution.
Arsalan Minhas, AVP Sales Engineering, EMEA & APAC, at Hyland, on how AI revolutionising financial services
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Artificial intelligence (AI) is revolutionising financial services, reshaping how institutions detect fraud, personalise customer experiences, and optimise investment strategies. From AI-powered chatbots assisting customers to machine learning models predicting market trends, the technology is driving unprecedented efficiency and insight.
Yet, alongside these advancements come new challenges. AI-driven scams are evolving in sophistication, algorithmic biases raise ethical concerns, and regulatory scrutiny is increasing. As financial institutions accelerate AI adoption, they’re walking the fine line between harnessing its benefits and mitigating its risks.
AI in fraud detection and prevention – strengthening security measures
One of the most critical areas where AI has transformed financial services is fraud detection and prevention.
Traditional fraud prevention methods relied on static rule-based systems, which were often ineffective at identifying evolving threats. Such systems aren’t necessarily equipped to keep up with the sheer pace of financial service operations today, which has led to a surge of interest in automated alternatives.
AI, particularly machine learning algorithms, offers a dynamic solution by analysing vast datasets in real time to identify anomalies and potential fraud. AI also enhances biometric authentication methods, such as voice and facial recognition. This can ensure secure access to accounts, reducing the reliance on passwords, which are vulnerable to breaches.
According to a recent McKinsey report, AI-driven fraud detection systems can reduce financial fraud losses by up to 50%. Making them a crucial asset for financial institutions. These unprecedented levels of speed and versatility has made AI a priority for even the biggest players.
Of course, fraud detection is not without its challenges. Criminals are also leveraging AI to create sophisticated scams, such as deepfake-based identity fraud. And the introduction of new technologies can challenge cybersecurity initiatives.
With that in mind, financial institutions must constantly update their AI models to stay ahead of emerging threats. Regulatory compliance adds another layer of complexity, as AI’s decision-making much align with consumer protection laws and data privacy regulations like GDPR and CCPA.
The future of Customer Experience
On the customer-facing side of things, Artificial Intelligence is transforming the customer experience through hyper-personalised financial services. Gone are the days of generic banking interactions. AI now enables financial institutions to tailor services based on individual customer behaviours, preferences and financial goals.
Leading UK banks like NatWest and Lloyds Bank have invested heavily in AI-powered virtual assistants. NatWest’s digital assistant, Cora, has handled millions of customer interactions, providing real-time financial insights, bill reminders, and even fraud detection alerts. Similarly, HSBC uses AI-driven tools to analyse spending patterns and offer personalised financial advice. The ability to assess transaction data allows banks to recommend budgeting strategies, suggest tailored loan offers, and predict future financial needs, making banking more intuitive and customer centric.
AI-driven robo-advisors, such as those offered by Nutmeg and Moneyfarm, have revolutionised investment management by providing algorithm-based financial planning. These platforms leverage AI to assess risk tolerance, market trends, and historical data to offer personalised investment strategies with lower fees than traditional financial advisors.
While such tools can be incredibly effective, they do raise concerns about data privacy and algorithmic bias. The more AI knows about an individual’s financial habits, the greater the risk of data misuse or bias in lending and investment recommendations.
Financial institutions must therefore ensure transparency and fairness in AI decision-making to build customer trust and meet regulatory regulations. The basis upon which customers share their personal data, and the protections that it is afforded, are a non-negotiable for any serious financial organisation.
Redefining market strategies in trading and investment
According to Deloitte, Artificial Intelligence is poised to be one of the most disruptive forces in investment management. High-frequency trading (HFT) firms now rely on AI algorithms to process vast amounts of market data within milliseconds. It also enables hedge funds and investment firms to predict market movements by analysing patterns from historical data, social media sentiment, and global economic indicators.
Leading firms like Man Group and XTX Markets have harnessed AI to enhance their trading strategies and portfolio management. Man Group, managing $175 billion in assets, utilises machine learning tools to develop its platform, ManGPT, to analyse trades and optimise investment decisions.
Similarly, XTX Markets, a London-based trading firm, employs advanced AI models to execute millions of trades daily, emphasising AI-driven strategies over sheer speed. Predictive analytics have become an indispensable tool in portfolio management, helping firms adjust their strategies based on real-time market fluctuations.
Naturally, these automated tools require to-the-second oversight from the business itself. The 2010 Flash Crash, in which the stock market plunged nearly 1,000 points within minutes, was exacerbated by algorithmic trading. AI-driven trading models can react unpredictably in volatile markets, amplifying risks if not properly regulated. Humanised AI – the combination of human and AI working in concert, rather than automated systems working in isolation – is crucial.
The future of AI in financial services
As Artificial Intelligence continues to evolve, its integration within financial services will only deepen. Institutions that successfully integrate AI into their operations will gain a significant competitive advantage. Benefiting from enhanced fraud detection, superior customer experiences, and data-driven investment strategies.
These businesses must also navigate the complexities of regulatory compliance, data privacy, and ethical AI deployment. The EU’s AI Act is one of many policies aiming to create the most robust governance structures for AI applications, and finance is no exception.
Striking the right balance between innovation and regulation will be crucial to ensuring AI remains a force for positive transformation rather than disruption. Financial institutions must prioritise transparency, human oversight, and ethical considerations in deployment to fully realise its potential while maintaining consumer trust.
The financial industry is on the brink of an AI-driven revolution. With careful implementation and responsible oversight, the technology has the power to make financial services more secure, efficient, and customer-friendly than ever before. Institutions that embrace this technology while addressing its challenges will shape the future of finance, redefining the way money is managed, invested, and protected in the years to come.
Scott Zoldi, Chief Analytics Officer at FICO, explains why there should be no AI alone in decision making processes
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Many AI models are black boxes and developed without proper consideration for interpretability, ethics, or safety of outputs. To establish trust, organisations should leverage Responsible AI. This defines standards of robust AI, explainable AI, ethical AI, and auditable AI. Under Responsible AI, developers define the conditions that lead to some transactions having less human oversight and others having more. But can we take people out of the decision-making loop entirely? To answer that question, let’s look at some developments in Responsible AI.
Trust in Developing AI Models
One best practice that organisations can adopt is maintaining a corporate AI model development standard. This dictates appropriate AI algorithms and processes to enable roles that keep people in the loop. This will often include the use of interpretable AI, allowing humans to review and understand what AI has learned for palatability, bias, ethical use and safety. Auditable AI will then codify the human-in-the-loop decisions and monitoring guidelines for operational use of the AI.
Responsible AI codifies all the essential human decisions that guide how AI will be built, used and progressed. This includes approving or declining the use of data, removing unethical relationships in data (i.e., illegal or unethical data proxies), and ensuring governance and regulation standards are met. Responsible AI leverages an immutable blockchain that dictates how to monitor the AI in operation. And the decision authority of human operators, which can include conditions where AI decisions are overruled, and operations move to a ‘humble AI model.’ AI Practitioners are keenly aware that even the highest performing AI models generate large number of false positives. So, every output needs to be treated with care and strategies defined to validate, counter, and support the AI.
A Responsible AI framework
There should be a well-defined process to overrule or reverse AI-driven decisions. If built in a Responsible AI framework, these decisions are codified into a crystal-clear set of operating AI blockchain frameworks well before the AI is in production. When there is a crisis you need clear preset guidance, not panicked decision making. This blockchain will define when humans can overrule the AI through alternate models, supporting data, or investigative processes. This AI operating framework is defined in coordination with the model developers, who understand the strengths and weaknesses of the AI. And when it may be operating in ways it wasn’t designed, ensuring there is no gap between development and operation. When auditable AI is employed, there are no nail-biting decisions in times of crisis. You can rely on a framework that pre-defines steps to make these human-driven decisions.
Companies that utilise Responsible AI frameworks enforce usage adherence by auditable AI, which is the operating manual and monitoring system. Embracing Responsible AI standards can help business units attain huge value. At the same time they can appropriately define the criteria where the businesses balance business risks and regulation. Domain experts/analysts will be given a defined span of control on how to use their domain knowledge and the auditable AI will monitor the system to alert and circumvent AI as appropriate.
Drawback prevention begins with transparency
To prevent major pull-back in AI today, we must go beyond aspirational and boastful claims to honest discussions of the risks of this technology. We must define how involved humans need to be. Companies need to empower their data science leadership to define what is high-risk AI, and how they are prepared or not to meet responsible/trustworthy AI. This comes back to governance and AI regulation. Companies must focus on developing a Responsible AI programme, and boost practices that may have atrophied during the GenAI hype cycle.
They should start with a review of how AI regulation is developing, and whether they have the tools to appropriately address and pressure-test their AI applications. If they’re not prepared, they need to understand the business impacts of potentially having AI pulled from their repository of tools. And get prepared by defining AI development/operational corporate standards.
Companies should then determine and classify business problems best suited for traditional AI vs. generative AI. Traditional AI can be constructed and constrained to meet regulation using the right algorithms to meet business objectives. Finally, companies will want to adopt a humble AI approach to have hot backups for their AI deployments. And to tier down to safer tech when auditable AI indicates AI decisioning is not trustworthy.
The vital role of the Data Scientist
Too many organisations are driving AI strategy through business owners or software engineers who often have limited to no knowledge of the specifics of AI algorithms’ mathematics and risks. Stringing together AI is easy. Building AI that is responsible and safe and properly operationalised with controls is a much harder exercise requiring standards, maturity and commitment to responsible AI. Data scientists can help businesses find the right paths to adopt the right types of AI for different business applications, regulatory compliances, and optimal consumer outcomes. In a nutshell: AI + human is the strongest solution. There should be no AI alone in decision-making.
Fouzi Husaini, Chief Technology & AI Officer at Marqeta, answers our questions about Agentic AI and its applications for businesses
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Agentic AI is emerging as the leading AI trend of 2025. Industry figures are hailing Agentic AI as the broadly transformative next step in GenAI development. The year so far has seen multiple businesses release new tools for a wide array of applications.
The technology combines the next generation of AI tech like large language models (LLMs) with more traditional capabilities like machine learning, automation, and enterprise orchestration. The end result could lead to a more autonomous version of AI: Agents. These agents can set their own goals, analyse data sets, and act with less human oversight than previous tools.
We spoke to Fouzi Husaini, Chief Technology & AI Officer at Marqeta about what sets Agentic AI apart whether the technology really is a leap forward in terms of solving AI’s shortcomings, and how Agentic AI could solve business problems.
1. What makes AI “agentic”? How is the technology different from something like Chat-GPT?
“Agentic refers to the type of Artificial Intelligence that can act as agents and on its own. Agentic AI leverages enhanced reasoning capabilities to solve problems without prompts or constant human supervision. It can carry out complex, multi-step tasks autonomously.
“GenAI and by extension Large Language Models, the most famous example being ChatGPT, require human input to solve tasks. For instance, ChatGPT needs user prompts before it can generate content. Then, sers need to input subsequent commands to edit and refine this. Agentic AI has the capability to react and learn without human intervention as it processes data and solves problems. This enables it to adapt and learn much faster than GenAI.”
2. Chat-GPT and other LLMs frequently produce results filled with factual errors, misrepresentations, and “hallucinations”, making them pretty unsuited to working without human supervision – let alone orchestrating important financial deals. What makes Agentic AI any better or more trustworthy?
“All types of AI have the possibility to ‘hallucinate’ and produce factually incorrect information. That being said, Agentic AI is usually less likely to suffer from significant hallucinations in comparison to GenAI.
“Agentic AI’s focus is specifically engineered to operate within clearly defined parameters and follow explicit workflows, making it particularly well-suited for having guardrails in place to keep it on task and from making errors. Its learning capabilities also allow it to recognise and adapt to its mistakes, ensuring it is unlikely to hallucinate multiple times.”
“On the other hand, GenAI occasionally generates factually incorrect content due to the quality of data provided, and sometimes because of mistakes in pattern recognition.”
“In fintech, Agentic AI technology can make it possible to analyse consumer spending data and learn from it, allowing for highly tailored financial offers and services that are more accurate and help to create a personalised finance experience for consumers.”
3. How could agentic AI deployments affect the relationship between financial services companies and their customers? What about their employees?
“The integration of Agentic AI into financial services benefits multiple parties. First,
integrating Agentic AI into their offerings allows financial service companies to provide their customers with bespoke tools and features. For instance, AI can be used to develop ‘predictive cards’. These cards can anticipate a consumer’s spending requirements based on their past behaviour. This means AI can adjust credit limits and offer tailored rewards automatically, creating a personalised experience for each individual.
“The status quo’s days are numbered as consumers crave tailor-made financial experiences. Agentic AI can allow fintechs to provide personalised financial services that help consumers and businesses make their money work better for them. With Agentic AI technology, fintechs can analyse consumer spending data and learn from it. This allows for more tailored financial offers and services.
“As for employees, Agentic AI gives them the ability to focus on more creative and interesting tasks. Agentic AI can handle more routine roles such as data entry and monitoring for fraud, automating repetitive tasks and autonomous decision making based on data. This helps to reduce human error and enables employees to focus more time and energy on the creative and strategic aspects of their roles while allowing AI to focus on more administrative tasks.”
4. How would agentic AI make financial services safer?
“Agentic AI has the capability to make financial services more secure for financial institutions and consumers alike, by bringing consistency and tireless vigilance to critical financial processes. With its ability to analyse vast strings of information, it can rapidly identify anomalies in spending data that indicate potential instances of fraud and can use its enhanced reasoning and ability to act without human prompts to quickly react to suspicious activity.
“While a human operator will be susceptible to decision fatigue, an AI agent could always be vigilant and maintain the same high level of precision and alertness 24/7. This is vital for fields like fraud detection, where a single missed signal could lead to significant consequences.
“Furthermore, its capability to learn without human interaction means that it can improve its ability to detect fraud over time. This gives it the ability to learn how to identify new types of fraud, helping it to adapt as schemes become more sophisticated over time.”
5. What kind of trajectory do you see the technology having over the next year to eighteen months?
“In fintech, Agentic AI integration will likely begin in the operations space. These areas manage complex, but well-defined, processes and are perfect for intelligent automation. For instance, customer call centres where human agents usually follow set standard operating procedures (SOPs) that can be fed into an AI system, which makes automation easier and faster than before.
“In the more distant future, I believe we will see Agentic AI integrated into automated workflows that span entire value chains, including tasks such as risk assessment, customer onboarding and account management.”
Stuart Cheetham, CEO at MPowered Mortgages, on how AI-powered technology allows mortgage lenders to fully underwrite loan applications in minutes
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AI technologies are about to have a huge impact on the mortgage market… In November last year the founders of Revolut announced plans to launch a “fully digital, instant” mortgage in Lithuania and Ireland in 2025. Details were sketchy but the company said that mortgages will be part of a “comprehensive credit offering” it intends to build.
Neobanking progress with AI
Digital only banks, like Revolut and Monzo, are renowned for using the power of technology and data science to create efficiencies and improve customer experience. The reason neobanks have been so successful is because they provide a modern, convenient and cost-effective alternative to traditional banking. This is done a transparent way, through fast onboarding, 24/7 app access and instant notifications. All with a user-friendly interface.
While many financial services sectors have embraced financial technology in the way Revolut and Monzo have for the retail banking sector, the mortgage sector has struggled to make a real breakthrough here. Why hasn’t the mortgage industry caught up one might ask? Mortgages are complex financial products, existing at the intersection of justifiably stringent regulation. They represent the single biggest financial commitment people make in their lifetimes. Financial advisors who source mortgages on behalf of borrowers are hindered at every stage by outdated systems and inadequate or commoditised product offerings.
Disrupting the Mortgage Market
The mortgage industry is one financial services sector that has been yearning to be shaken up by the FinTech industry for some time. While it’s encouraging to see a successful brand like Revolut enter this market, what is less known is that huge progress is being made already by smaller and less well known FinTech disruptors.
For example, the mortgage technology company MQube has developed a “new fast way” of delivering mortgage offers using the cutting edge of AI technology and data science. Today, it still typically takes several weeks to get a confirmed mortgage offer. This is one of the major reasons the homebuying process can be so time consuming and stressful for brokers and borrowers. The mortgage process is characterised by bureaucracy, paperwork, delays and often frustratingly opaque decision-making by lenders. This leads to stress and uncertainty for consumers, and their advisors. And at a time when they have plenty of other property-purchase related challenges to contend with.
Our proprietary research shows us, and this will come as no surprise, that the biggest pain point for borrowers and brokers about the mortgage process is that it is time consuming, paperwork heavy and stressful. Imagine a world where getting a mortgage is as quick and as easy as getting car insurance. This is MQube’s vision.
MQube – AI-powered Mortgages
MQube‘s AI-powered mortgage origination platform allows mortgage lenders to fully underwrite loan applications in minutes. MPowered Mortgages is MQube’s lending arm and competes for residential business alongside the big banks. It uses MQube’s AI-driven mortgage origination platform and is now able to offer a lending decision within one working day to 96% of completed applications.
The platform leverages state-of-the-art artificial intelligence and machine learning to assess around 20,000 data points in real-time. This enables lenders to process mortgage applications in minutes, transforming the industry standard of days or weeks. It automates the entire underwriting journey, from application to completion. This helps to provide a faster service, reduce costs, mitigate risks, and to make strategic adjustments quickly and effectively. By assessing documents and data in real-time during the application, it is able to build a clearer and deeper understanding of a consumers’ circumstances and specific needs. Applicants are never asked questions when MQube can independently source and verify that data, leading to a streamlined and paperless experience. Furthermore, this whole process reduces dependency on human intervention.
The benefits of AI
More and more lenders are seeing the benefits AI and financial technology can bring to their business. They are beginning to adopt such AI-driven financial systems which are scalable and serve to address systemic problems in this industry. The mortgage industry is still some way behind the neobanks, but what’s hugely exciting to see is the progress that has been made so far. Moreover, if FinTechs continue to innovate this sector and if lenders continue to embrace financial technology and use at scale, then getting a mortgage could genuinely become a quick, easy and stress free process. At this point, the mortgage industry could begin to see a shift in consumer perception and change in consumer behaviour. A new frontier for the mortgage industry is upon us.
FICO’s use of Blockchain for AI model governance wins Tech of the Future: Blockchain and Tokenisation award
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Global analytics software leader FICO has won the Tech of the Future – Blockchain and Tokenisation award. The Banking Tech Awards in London recognised FICO for its innovative work using Blockchain technology for AI model governance. FICO’s use of blockchain to advance responsible AI is the first time blockchain has been used to track end-to-end provenance of a machine learning model. This approach can help meet responsible AI and regulatory requirements.
FICO’s AI Innovation and Development team has developed and patented an immutable blockchain ledger. It tracks end-to-end provenance of the development, operationalisation and monitoring of machine learning models. The technology enforces the use of a corporate-wide responsible AI model development standard by organisations. It demonstrates adherence to the standard with specific requirements, people, results, testing, approvals and revisions. In addition to the Banking Tech award, Global Finance recognised FICO’s blockchain for AI technology with The Innovators award last year.
Responsible AI
“The rapid growth of AI use has made Responsible AI an imperative,” commented Dr. Scott Zoldi, chief analytics officer at FICO. “FICO is focused on technologies that ensure AI is used in an ethical way, and governance is absolutely critical. We are proud to receive another award for our groundbreaking work in this area.”
FICO is well-known as a leader in AI for financial services. Its FICO® Falcon® Fraud Manager solution, launched in 1992, was the first fraud solution to use neural networks. Today it manages some four billion payment cards worldwide. FICO has built advanced analytics capabilities into FICO® Platform, an applied intelligence platform for building decision management solutions.
Paul O’Sullivan, Global Head of Banking and Lending at Aryza, on the rise of AI in banking
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The banking sector stands at the crossroads of technological innovation and operational transformation. AI is taking centre stage in reshaping how financial institutions operate. The banking sector is beginning to recognise AI’s potential. It can address challenges, enhance operational efficiency, and deliver more personalised customer experiences.
The Current State of AI in Banking
Research reveals that while a number of banking organisations have yet to fully integrate AI into their operations, key areas such as debt recovery are leading the charge. The slower pace of adoption can be attributed to the highly regulated environment of banking. Because transparency, compliance, and customer trust are non-negotiable. However, despite this cautious approach, banks that have implemented artificial intelligence are already seeing significant benefits, particularly in risk management.
AI’s Role in Risk Management
Effective risk management is a cornerstone of the banking sector. AI is proving to be a powerful tool in this area. By analysing vast amounts of data and providing predictive insights, AI enables banks to mitigate risks early. They can strengthen customer portfolio stability, and make data-driven lending decisions. These capabilities are essential in a landscape where financial risks can escalate rapidly.
Beyond the expected benefits, banks have also reported enhanced customer insights as an unexpected advantage. By leveraging AI to analyse customer behaviours and preferences, banks can tailor their products and services more effectively. Furthermore, they can improve customer satisfaction and experience, whilst fostering long-term loyalty.
Challenges to Adoption
Although organisations are experiencing a multitude of advantages, the integration of AI in banking is not without its hurdles. Legacy IT systems, stringent regulatory requirements, and concerns around data privacy pose significant challenges to widespread adoption. Banks must ensure AI-driven decision-making processes are effective. Moreover, they must also be fully transparent and compliant with industry regulations. Further highlighting the importance of a gradual, strategic approach to AI implementation.
Opportunities Ahead
The potential for AI in banking extends far beyond risk management. From streamlining operational workflows to enhancing customer personalisation and improving decision-making. AI is set to drive innovation across the sector. For example, AI-powered chatbots and virtual assistants transform customer service by providing instant, 24/7 support. They can handle complex interactions, enhancing customer satisfaction. At the same time, advanced analytics enable banks to analyse behaviour patterns, predict trends, and personalise product offerings. Furthermore. enhancing cross-selling opportunities and driving deeper customer engagement. These tools are becoming strategic enablers for innovation in the financial landscape.
A Call to Action
For banks to fully realise the benefits of AI, they must address the digital transformation gap, modernising outdated infrastructures and fostering a culture of innovation. This includes investing in technologies that align with their strategic goals, ensuring robust data security measures alongside maintaining compliance with evolving regulations.
As the banking sector continues its journey towards digital maturity, AI will play a pivotal role in defining its future. By overcoming current barriers and embracing AI-driven solutions, banks can not only enhance operational efficiency but also deliver the seamless, personalised experiences that customers now expect in an increasingly digital world.
About Aryza
At Aryza know that in today’s highly regulated world, there is huge value in quickly guiding your customers through the product that best fit their immediate needs, through a seamless journey that is tailored to their specific circumstances.
We created smart platforms, responsible and compliant products, and a unique system of companies and capabilities so that businesses can optimise their customers’ journey through the right product at the right time.
For our teams across the globe, the growth of Aryza is a good news story and a testament to our clear vision and goals as an international business.
And also front of mind as we build a global footprint is our impact on the environment. Aryza is committed to reducing its carbon impact through the choices it makes and we are pleased to say that we follow an active roadmap.
Jamil Jiva, EVP at Linedata, on compliance in asset management following the EU AI act
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AI’s value-add has shifted from speculative to tangible in recent years. For consumers, it’s brought convenience; for businesses, invaluable timesaving. In the asset management space however, its impact is transformative. It can help assess choice, trust, and risk in seconds. AI isn’t just improving efficiency, it’s fundamentally reshaping decision-making processes.
It’s clear artificial intelligence is achieving widespread adoption among asset managers. Linedata’s recent global survey showed that 36% of asset management companies have already integrated AI into their operations. A further 37% are preparing to introduce it.
However, adopting new and evolving technology can prove to be a long-term challenge. Asset managers have to adapt to regulation as it changes. For example, the newly enacted EU AI Act is designed to regulate high-risk uses. It seeks to ensure safety, transparency, and accountability. With new regulations arriving thick and fast, companies should avoid rushing their implementation or cutting corners. Compliance should be their first and last thought.
AI can bring immediate benefits in optimising efficiency, streamlining operations, and boosting decision-making capabilities. The newly enacted EU AI Act will push firms planning to take a more measured approach to deploying artificial intelligence. This will necessitate a long-term, compliance-driven approach.
The New Compliance Landscape
The EU AI Act marks a turning point for AI governance. For the financial sector, the act will put explainability at the fore of AI-augmented decisions. For asset management firms, which increasingly rely on AI to drive decisions related to market forecasts, risk modelling, and portfolio management, the act mandates a robust approach to accountability.
Asset management firms that use AI must now prioritise governance or risk severe penalties and long-term reputational damage. As firms adjust to the EU AI Act, they must recalibrate their AI strategies and implement future-proof frameworks that blend innovation with security and ethical standards.
Hybrid AI Systems: Creativity and Control
One promising approach to the new regulatory environment is hybrid AI. Hybrid systems marry proprietary data with third-party models. With a blended strategy firms retain full oversight over sensitive tasks – such as decision-making models . Meanwhile, outsourcing less critical functions like data analysis or back-office automation to third-party vendors.
However, hybrid systems bring their own challenges under the EU AI Act. The new regulation imposes strict requirements for transparency. This means firms must ensure that any external solutions they adopt meet the same high standards of risk management and documentation. This necessitates a more in-depth vetting process for third-party providers and ongoing oversight to guarantee compliance. Effective governance, therefore, hinges not just on internal processes but also on the integrity and transparency of external systems and partners.
Despite these complexities, hybrid AI presents an opportunity for asset managers to continue innovating without compromising on compliance. By carefully managing these systems, firms can position themselves to harness the full potential of artificial intelligence while mitigating the risks associated with regulatory breaches.
Building a Sustainable AI Strategy
While the EU AI Act certainly raises the bar for compliance, it also presents an opportunity for firms to create more sustainable, future-proof strategies. Much like how the GDPR transformed data governance, the AI Act could drive a more comprehensive approach to artificial intelligence oversight, encouraging firms to adopt stronger ethical frameworks while staying ahead of regulatory shifts.
For asset managers, investing in adaptable AI infrastructures is one way to navigate these regulatory demands. By focusing on systems that are both flexible and scalable, firms can ensure they remain compliant with evolving regulations without sacrificing the pace of innovation. In particular, areas like predictive analytics, ESG reporting, and portfolio management stand to benefit from such advancements, provided firms integrate transparency and accountability into their strategies.
Asset managers who view regulatory challenges as opportunities – rather than obstacles – will emerge as leaders, showcasing a commitment to ethical AI that can ultimately build trust with clients and regulators alike. While the EU AI Act may seem daunting at first, for those who embrace the changes, it offers a chance to redefine how artificial intelligence can shape the future of asset management.
Scott Zoldi, Chief Analytics Officer at FICO considers whether the current AI bubble is set to burst, the potential repercussions of such an event, and how businesses can prepare
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Since artificial intelligence emerged more than fifty years ago, it has experienced cycles of peaks and troughs. Periods of hype, quickly followed by unmet expectations that lead to bleak periods of AI-winter as users and investment pull back. We are currently in the biggest period of hype yet. Does that mean we are setting ourselves up for the biggest, most catastrophic fall to date?
AI drawback
There is a significant chance of such a drawback occurring in the near future. So, the growing number of businesses relying on AI must take steps to prepare and mitigate the impact a drawback or complete collapse could have. Research from Lloyds recently found adoption has doubled in the last year, with 63% of firms now investing in AI, compared to 32% in 2023. In addition, the same study found 81% of financial institutions now view it as a business opportunity, up from 56% in 2023.
This hype has led organisations to explore AI use for the first time. Often with little understanding of the algorithms’ core limitations. According to Gartner, in 2023 less than 10% of organisations were capable of operationalising AI to enable meaningful execution. This could be leading to the ‘unmet expectations’ stage of the damaging hype/drawback cycle. The all-encompassing FOMO of repeating the narrative of the incredible value of AI does not align with organisations’ ability to scale, manage huge risks, or derive real sustained business value.
Regulatory pressures for AI
There has been a lack of trust in AI by consumers and businsses alike. It has resulted in new AI regulations specifying strong responsibility and transparency requirements for applications. The vast majority of organisations are unable to meet these in traditional AI, let alone newer GenAI applications. Large language models (LLMs) were prematurely released to the public. The resulting succession of fails fuelled substantial pressure on companies to pull back from using such solutions other than for internal applications. It has been reported that 60% of banking businesses are actively limiting AI usage. This shows that the drawback has already begun. Organisations that have gone all-in on GenAI – especially those early adopters – will be the ones to pull back the most, and the fastest.
In financial services, where AI use has matured over decades, analytic technologies exist today that can withstand regulatory scrutiny. Forward-looking companies are ensuring they are prepared. They are moving to interpretable AI and backup traditional analytics on hand while they explore newer technologies with appropriate caution. This is in line with proper business accountability, vs the ‘build fast, break it’, mentality of the hype spinners.
Customer trust with AI
Customer trust has been violated by repeated failures in AI, and a lack of businesses taking customer safety seriously. A pull-back will assuage inherent mistrust in companies’ use of artificial intelligence in customer facing applications and repeated harmful outcomes.
Businesses who want their AI usage to survive the impending winter need to establish corporate standards for building safe, transparent, trustworthy Responsible AI models that focus on the tenets of robust, interpretable, ethical and auditable AI. Concurrently, these practices will demonstrate that regulations are being adhered to – and that their customers can trust AI. Organisations will move from the constant broadcast of a dizzying array of possible applications, to a few well-structured, accountable and meaningful applications that provide value to consumers, built responsibly. Regulation will be the catalyst.
Preparing for the worst
Too many organisations are driving AI strategy through business owners or software engineers who often have limited to no knowledge of the specifics of algorithm mathematics and the very signifiicant risk in using the technology.
Stringing together AI is easy. Building AI that is responsible and safe is a much harder and exhausting exercise requiring model development and deployment corporate standards. Businesses need to start now to define standards for adopting the right types of AI for appropriate business applications, meet regulatory compliances, and achieve optimal consumer outcomes.
Companies need to show true data science leadership by developing a Responsible AI programme or boosting practices that have atrophied during the GenAI hype cycle which for many threw standards to the wind. They should start with a review of how regulation is developing, and whether they have the standards, data science staff and algorithm experience to appropriately address and pressure-test their applications and to establish trust in AI usage. If they’re not prepared, they need to understand the business impacts of potentially having artificial intelligence pulled from their repository of tools.
Next, these companies must determine where to use traditional AI and where they use GenAI, and ensure this is not driven by marketing narrative but meeting both regulation and real business objectives safely. Finally, companies will want to adopt a humble approach to back up their deployments, to tier down to safer tech when the model indicates its decisioning is not trustworthy.
Now is the time to go beyond aspirational and boastful claims, to have honest discussions around the risks of this technology, and to define what mature and immature AI look like. This will help prevent a major drawback.
Alexandra Mousavizadeh, CEO and Co-Founder of Evident, on how global banks are stepping up their AI comms in the face of growing investor scrutiny
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In the big banks’ Q2 earning calls this year, a critical milestone was reached. For the first time, half of the 50 major banks we track in the Evident AI Index fielded questions from equity analysts concerning risks and opportunities specific to artificial intelligence (AI).
External scrutiny of the banks’ AI progress is steadily increasing. This is in line with the huge sums institutions have pumped into originating, developing, rolling out and scaling AI use cases. Banking leaders we’ve spoken to aren’t expecting to register meaningful bottom line business impacts from AI investments for at least another 24-36 months. Meanwhile, investors need satisfying that progress is being made, and that ROI will be forthcoming,
Against this backdrop, the way in which banks communicate around AI is becoming increasingly important.
Just 12 months ago, many banks were making only sporadic, broad-brush or conceptual references to AI. However, our recent AI Leadership Report revealed every bank in the Evident AI Index now has a communications and marketing strategy. Furthermore, the majority are referencing AI across multiple communications channels. These include annual reports, press releases, company LinkedIn posts, and media interviews.
Banks need to ‘talk the walk’
It’s not just the volume of comms, but the substance that is increasing. More banks are now willing to reveal specifics around internal use cases already in production. Moreover, they are sharing the results of these efforts and tangible information about what they are doing to scale artificial intelligence.
Last year, only 6 of 50 Index banks identified AI as a strategic priority in investor relations materials, and clearly described specific use cases in production alongside their ROI. This year, this number increased 2.5x to 15 banks.
These substantive communications help to reassure and placate investors. Furthermore, if a bank is perceived to be at the leading edge of AI adoption, the easier it becomes to attract, retain and inspire the talent needed to make organisation-wide transformation a reality
The C-Suite needs to engage in the AI debate
To achieve cut through in the debate, banks are mobilising their C-level leaders to publicise their ongoing efforts. They are setting out their vision for becoming AI-first organisations.
Of the 50 banks, 45 now have at least one C-Suite executive that has engaged on the topic of AI in external media in the last year. Furthermore, 15 of the 50 banks have two spokespeople on AI, while six banks (CaixaBank, DBS, Goldman Sachs, Intesa Sanpaolo, JPMorgan Chase, and NatWest) are engaging with four or more spokespeople across the Executive team.
As the primary owner of the bank’s strategic vision, the CEO should arguably lead from the front when it comes to market communications around AI. Meanwhile, JPMorgan Chase leads the pack across a host of AI maturity metrics. The efforts of Jamie Dimon to set the agenda and relentlessly beat the drum should not be understated.
Over the past 12 months, Dimon has been quoted in the media on AI topics around 10x more than any other banking chief. He continuously reaffirming his institution’s dominant position in the eyes of investors. This is an intentional, coordinated AI communications strategy that other banks would be well advised to follow.
Communicating tangible AI gains is vital as operational realities bite
Every potentially game-changing new technology follows a well-established hype cycle. In the case of AI, we’re now seeing the inflated expectations of Generative AI – arguably the most significant technology innovation of the past decade – being tempered by the realities (and difficulties) of operationalisation.
A recent memo from leading venture capital firm Sequoia Capital highlighted the elephant in the room. Namely, that the gap between what’s being spent to build out AI (mostly by tech companies) and the actual revenue realised by that investment has risen to $600 billion this year, up from $200 billion in September 2023. Investors are starting to probe for detail on when and where the ROI is coming from and, like Big Tech, the world’s leading banks will find it impossible to duck the difficult questions.
A delicate balance must be struck. Overpromising on AI today and underdelivering further down the line could prove disastrous. And yet, banking leaders know that in the highly contested race for artificial intelligence supremacy, failing to communicate their plans and progress also carries reputational risk.
Of the 50 banks we track, 38 announced at least one AI use case in the last year. Meanwhile, only 21 reported any outcomes associated with those use cases. And of those, only two – JPMorgan Chase and DBS – went so far as to specify their total actual realised $ return on AI spend last year.
With investor scrutiny only likely to intensify in the year ahead, individuals at the top of every bank must set forth a clear vision. They must establish frameworks for measuring the effectiveness of their AI efforts and the ROI being realised. And, crucially, provide consistent and clear communication every step of the way.
Nicholas Holt, Head of Solutions and Delivery, Europe, Marqeta on how AI has the potential to revolutionise payments
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The financial services sector has witnessed a profound transformation over the past two decades. It has been propelled by technological advancements. From online banking to mobile-first platforms like Revolut and Monzo, the industry is continuously evolving. The integration of Artificial Intelligence (AI) into financial services is set to push the boundaries even further. Offering enhanced convenience and changing how we manage our money.
AI offers the ability to process and analyse vast amounts of data in real-time. It promises to make financial services intuitive, intelligent, and personalised to individual needs. And it can also help to make it more secure.
AI-Powered Personalisation
AI can interpret a consumer’s transaction history and spending patterns to create tailored financial recommendations. These include optimising payment methods, choosing better reward programmes, or suggesting savings opportunities. This degree of personalisation is far more sophisticated than the broad, one-size-fits-all approach currently offered by banks.
The technology can enable ‘predictive cards’ to leverage machine learning algorithms to set personalised credit limits and rewards based on an individual’s financial behaviour. By predicting future needs, AI-powered tools can offer a more holistic view of one’s finances. They can improve financial literacy and promote better financial decision-making.
Consumers are increasingly warming to the idea of AI in financial services. According to Marqeta’s 2023Consumer Pulse Report, 36% of consumers in the US and the UK expressed interest in using AI tools to help manage their finances. This figure rose to over 50% for consumers under 50, indicating a clear demand for personalised AI-driven solutions.
Unlocking Access to Credit
Access to credit is a significant factor in financial inclusion. AI has the potential to expand this access by transforming how creditworthiness is assessed. Traditionally, credit approval processes have relied heavily on limited data points, such as a person’s credit score and income. However, AI can analyse a broader range of data, from spending patterns to social media behaviour. This can provide a more nuanced assessment of an individual’s creditworthiness.
By using advanced machine learning models, AI can process this data at incredible speeds. This allows more people to be approved for credit faster and with greater accuracy. It can be particularly beneficial for individuals who may have struggled to secure credit through traditional methods, such as younger consumers or those without a lengthy credit history.
Generative AI (GenAI), which builds upon traditional AI by predicting and creating entirely new behaviours and patterns, also holds promise in this area. As the use of GenAI tools grows, we can expect more tailored financial products that respond to each consumer’s unique needs. Moreover, this could include personalised loan offerings or dynamic credit options that adapt in real-time to a person’s financial situation.
Fighting Fraud
While personalisation is one of AI’s most exciting applications, its ability to detect and prevent fraud is another crucial benefit. Fraud detection is a near constant battle across financial services, with millions of transactions processed every minute across the globe. Identifying suspicious activities quickly and accurately is essential for maintaining trust and security.
Machine learning algorithms are adept at spotting irregularities that might be missed by human analysts or even traditional software. Additionally, these systems can identify patterns that indicate potential fraud and alert financial institutions instantly, allowing them to take swift action.
Furthermore, as fraud techniques evolve, AI systems will continuously learn and adapt, staying one step ahead of cybercriminals. This capacity to evolve will make AI an invaluable asset in the fight against fraud.
AI and Embedded Finance
Embedded Finance, the process of integrating financial services into non-financial platforms, has already begun reshaping how consumers and businesses interact with money. AI is set to accelerate this trend, enhancing the capabilities of embedded financial tools with real-time data processing and hyper-personalisation.
For instance, businesses could use AI-powered embedded finance solutions to offer tailored payment options at checkout based on a customer’s purchasing behaviour. This could include personalised financing options, such as Buy Now, Pay Later (BNPL) services, or optimised rewards based on previous transactions. Companies like Marqeta are already exploring AI’s potential to elevate embedded finance, making these interactions seamless and highly personalised.
The Future of Finance
Financial services in 20 or just 10 years from now will likely be unrecognisable compared to today. AI will play a central role in shaping this evolution. Consumers and businesses can expect a future where financial products are deeply integrated into everyday life. However, not as separate, standalone services, but as seamless, invisible enablers of transactions and financial management.
GenAI will become increasingly sophisticated, offering predictive insights that can help consumers manage finances with greater precision. For businesses, AI-driven solutions will enable more efficient operations, cost reductions, and enhanced customer engagement through personalised offerings.
In this future, consumers will enjoy unparalleled convenience and flexibility. Payments, credit, and financial planning will be customised to fit the individual, with AI continuously learning and adapting to offer better recommendations and insights. This will lead to greater financial literacy, broader access to credit, and improved financial security. Additionally, financial service providers will gain much greater control over fraud and other security challenges.
Fred Fuller, Global Head of Banking at Endava, on how banks can effectively communicate AI advancements and demonstrate ROI to investors
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There is no single solution, AI or otherwise, that can prepare financial institutions for the modern world. To build a bank capable of successfully navigating the challenges of the future, a long-term digital transformation strategy is required. Especially relevant in the wake of recent IT outages,
At present, according to Endava’s Retail Banking Report 2024, 67% of banks are still heavily reliant on legacy systems. This leads to wasted budget and decreased efficiency. With limited resources available to modernise their tech stack, company leaders are often forced to choose which technology-type to prioritise. When doing this, 50% have chosen artificial intelligence (AI).
Is AI alone enough?
Can AI overhaul archaic processes or are there too many hurdles in the way? The first hurdle to successful digital transformation in financial services is overcoming the employees’ perception of the process. Time and time again, corporations have failed in the goal to integrate solutions that successfully feed into a long-term tech strategy. Often, this is due to wide-spread change fatigue. When exhausted by continuous efforts to change their day-to-day, workers become resistant to transformation. The best way to overcome change fatigue, and drive digital transformation in financial institutions, is through overhauling legacy systems. And adopting solutions that will stand the test of time.
Legacy Systems
Across the world, outdated legacy systems are holding financial institutions back and costing them billions. From 2022 to 2028, this expense is expected to grow at a rate of 7.8%. Not only do these archaic processes cost money, but they force banks to contend with a multitude of siloes. From departments to data. We live in a world where neobanks are growing in popularity. They are able to provide a frictionless customer experience using their modern tech stack. Traditional organisations must rid themselves of siloes to enable all areas of the business to leverage AI. In turn, this will provide them with strong data collection and support from departments who are agreed on next steps.
At present, three quarters of financial institutions feel they need to modernise their core. Without this change, they lack the secure, data-driven foundation necessary to utilise AI and see return on their technical investments.
The benefits of AI integration
Once a strong foundation has been laid, it becomes easier to see the practical benefits of integrating AI. For example, when data is no longer siloed by legacy systems, using chat bots to support customers with simple queries creates an efficient consumer experience. There are internal benefits too. AI can spot potentially suspicious activity, flagging it before it is too late. Or analysing data to ensure risk management and process automation. Despite its wide-reaching capabilities, AI alone is not the only option for financial institutions…
Routes to the future
Endava’s Retail Banking Report also showcased the variety of solutions that banks are using to improve their tech stack. 45% of respondents recognised data analytics, in and of themselves, as a top area for investment. Meanwhile 30% flagged IoT, and 14% the Metaverse.
There’s a reason for the emphasis on strong data. It not only supports the integration and use of AI-fuelled capabilities, but it is the driving force behind numerous functions of the bank itself. Of those surveyed, 37% aimed to use data to improve customer service. 34% to strengthen security, and 33% to personalise products and improve the customer experience.
As well as attracting and retaining consumers, business leaders can benefit from their access to strong data by attracting and retaining talent. With 39% of failed digital transformations viewing lack of employee buy-in as a factor, financial institutions are encouraged to educate workers on their technology integration plans, and ensure solutions are user-friendly. Fortunately, looking ahead, 20% of banks surveyed seek to use data to improve the workplace.
A bank’s priority – looking ahead
More than ever, banks are reliant on data to keep operations running smoothly. From providing customers with a personalised experience to improving the workplace in the competition for talent, there are a multitude of reasons to ensure the foundations of your tech stack are strong.
Doing so makes integration of new technology a smoother experience for all. To this end, it’s no shock that 50% of banks are keen to embrace AI, using it to benefit customers and speed up processes. However, with many hampered by the legacy technology and the ever-looming threat of change fatigue, integration of any technology should be carefully planned, customer focused and data led.
Sejal Mehta and Andrew Rodgers from Odgers Berndtson’s Global FinTech Centre of Excellence and Randy Bean, a Senior Advisor to Odgers Berndtson and industry author, explore the dynamics shaping leadership in the UK fintech sector
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The UK FinTech sector is undergoing a significant transformation, marked by maturation, consolidation, and a more selective investment landscape. Funding is increasingly funnelled towards profit-generating scale-ups, and away from newer entrants.
At the same time, the sector is shaped by a multi-generational workforce with varied perspectives. Meanwhile rapid advancements in AI foster apprehension and excitement. These converging factors make FinTech one of the most dynamic and competitive spaces to work in today. This presents both challenges and opportunities for its leaders.
From our perspective as global FinTech executive search and leadership advisors at Odgers Berndtson these shifts are reshaping the demands placed on leadership. They are also influencing what it takes to lead effectively in this fast-changing sector. Here, we explore the leadership trends that are emerging as a result.
Ethical FinTech leadership
Venture capital funding is now more selective and private equity investors are increasingly targeting fintechs with solid exposure. This is creating a difficult environment for new start-ups. Those attracting funding are typically cash-positive scale-ups.
Amidst these challenges, more FinTech firms are opting to list on the NASDAQ rather than the London Stock Exchange, as the UK navigates more stringent regulation. The need for payments licences, extensive reporting, and compliance demands weigh heavily on FinTech leaders.
In this landscape, we’re seeing leaders with experience in regulated financial services bring a valuable skillset. The ability to operate within defined regulatory frameworks while generating growth. FinTech boards are looking for leaders with high authenticity and who can make ethical decisions. And while balancing ambition and growth with the realities of working in a highly regulated space.
Founder replacements
We are in the midst of the FinTech sector’s maturation. Start-ups are transitioning into scale-ups, requiring different leadership competencies. For many, this requires the founder to step down or step into a board role and appoint a CEO who can take the business through its next stage of growth.
This requires leaders who are commercially driven, capable of shaping market strategies, and adept at understanding customer needs and product-market fit. Navigating risk and regulation becomes crucial, while the founder’s creative, opportunity-led approach typically no longer dominates the new operational and strategic demands.
Boards and investors are looking for CEOs with a broader skillset and deep regulatory expertise. These leaders must also be able to attract and retain the type of talent that can sustain growth and innovation, while maintaining the ‘DNA’ that made the business so attractive in the first place.
A multi-generational workforce
Intergenerational divides are becoming more pronounced for all businesses and noticeably in sectors like FinTech. Here, younger generations with fresh perspectives are working alongside older, more experienced professionals – often from traditional financial services backgrounds.
This diversity in age, experience, and approach can be a powerful asset, but only if integrated effectively. Typically, Gen Z and Millennials prioritise flexibility, technological integration and experimentation. Meanwhile, Boomers bring valuable expertise in regulatory environments and operational effectiveness, but may be more accustomed to traditional structures and leadership styles.
Increasingly, we see FinTech leaders attempt to bridge these divides by emphasising open communication, promoting mentorship opportunities, and encouraging cross-generational collaboration. With less funding and more regulation, FinTech leaders recognise the need to identify and capitalise on the strengths of a multigenerational workforce if they are to succeed.
Leadership team dynamics
As FinTech companies scale, leadership is no longer just about the capabilities of individual leaders but about the dynamics of the entire executive team. Successful scale-ups understand the importance of assembling a leadership team that brings a diverse mix of skills, and generational perspectives to the table.
We are starting to see FinTech companies think about leadership team dynamics as they scale up. Boards are looking for a blend of strategic, operational and ethical considerations. As well as how well team members work together. Do they solve problems cohesively? Are there any unresolved tensions or conflict? Are they aligned and equipped to collectively deliver on the leadership mandate?
Many leadership teams are not optimising their potential due to misalignment of strengths. For example, we recently worked with a FinTech creating an executive team profile to identify the leadership competencies needed to deliver their mandate. This exercise enabled the team to reallocate executive responsibilities for strategic initiatives based on the required strengths, regardless of traditional job roles.
Polarising views on Gen AI
Leading organisations are experiencing a transformational moment due to accelerated interest in AI and Generative AI. 89.6% are increasing their investments in AI, while 64.2% of companies have indicated that AI will be the most transformational technology in a generation. In response, organisations are hiring for the data and AI leadership roles required to prepare their companies for an AI future.
However, this integration of Gen AI has sparked both excitement and nervousness, particularly around issues of data protection and privacy. Generational differences are especially noticeable. Younger professionals are often less concerned about data privacy, while older generations remain cautious about the security implications.
This divergence in attitudes can create tension within the organisation, as leaders grapple with how best to leverage Gen AI while ensuring compliance with stringent data protection regulations. For some FinTechs, AI is seen as a specialised area requiring dedicated focus. Meanwhile, others believe AI represents a fundamental shift in how business can be conducted and AI strategy should be woven into the fabric of every leader’s responsibilities.
This divide in attitudes reflects the broader challenges we see FinTech companies face in incorporating AI. Leaders must now navigate the balance between embracing innovation and safeguarding sensitive information. They must also ensure AI is not seen as a siloed function. It must be an integral part of their commercial and strategic vision. Given the fundamental changes in the sector, the emphasis on leadership capabilities is changing for both the individual and executive team.
As businesses increasingly turn to AI to drive efficiencies in customer service operations, James Towner, Chief Growth Officer at ArvatoConnect, explores how businesses can strike the right balance of using digital technologies that empower successful human interactions.
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Generative AI continues to transform how businesses engage with their customers. Buy-now-pay-later-giant Klarna is the latest to grab headlines for integrating an AI customer service chatbot that manages the equivalent workload of 700 employees. Klarna’s bosses have hailed AI as delivering superior experiences for their customers, saying its chatbot has a customer satisfaction score similar to human agents. However, studies find AI is no panacea for customer service success just yet.
AI vs the human touch
AI can undoubtedly play a major role in automating more routine queries. It provides a dynamic augmentation to the agent’s role by providing consistent, relevant information to the agent’s fingertips. But in many instances human interaction is an invaluable part of the customer experience.
In addition, customers have a variety of needs not least when it comes to those with vulnerabilities. The latest report from ArvatoConnect found how consumers that self-identify as being vulnerable said they prefer some level of human interaction when seeking help from a business. AI tools are unlikely to fully understand their unique needs.
A separate study by Smart Money People also highlights that nearly half of financial services customers (48%) are frustrated by a lack of access to human support. And an over-reliance on chatbots (24%) from firms. This epitomises the challenge facing customer services transformation projects in financial services and other categories. How can businesses get the right balance between AI and the human touch to optimise the customer experience? And what are the risks to getting the balance wrong?
Humanising the digital, digitising the human
Undoubtedly businesses can drive efficiencies in customer service operations with the help of technologies. These include AI, machine learning for analysing customer data, and robotic process automation (RPA) for handling repetitive tasks like extracting data from financial documents and using next generation chatbots. They allow human agents to focus on more complex issues, bringing empathy and creativity to their interactions.
Combining AI and human agents can then enable what we call ‘humanising the digital and digitising the human’. It represents a hybrid approach. For example, live speech AI analytics can provide helpful prompts or insights for agents, during conversations with customers, while freeing up their time.
Automating quality assurance and using generative AI to summarise customer interactions is helping to boost agents’ productivity while driving upskilling and training. Sentiment analysis and conversation analytics can also help agents to identify triggers for vulnerability. This can help them to provide the right level of support customers need and identify the next best action to take.
Developments of these customer service technologies will continue to drive transformation. Advanced tools can assess past and present customer data, suggest personalised next steps and guide agents through complex interactions. This helps ensure they deliver the right outcomes quickly and effectively to all customers.
Addressing the imbalance
Encouragingly, addressing the balance of AI and human agents is on the radar of businesses. Nearly a third (29%) of financial services businesses told us in a separate study that they planned to move the focus away from AI to human contact.
However, this compares to 51% in our study saying they planned to introduce more technology, such as AI and automation, to support the customer experience.
Understandably, many businesses see such technology as a route to saving money. But cost savings can still be reaped by empowering human agents with the right digital tools.
Companies can set clear goals for which processes need improvement, design solutions that meet those specific needs, and take a people-first approach. What this means, is using technology at the right times, in the right places – what we call ‘digital orchestration’ – and always knowing why it’s being used and what it’s expected to deliver.
Supporting vulnerable customers with AI
This is even more important when it comes to vulnerable customers, tailoring options like access to a human, to avoid the risk of alienating a large customer base
Nearly half (47%) of people in the UK identify as vulnerable, according to the Financial Conduct Authority. These individuals may face one or more of a wide range of unique challenges like mental or physical health issues, or have experienced difficult life events like bereavement.
Our study, which polled 250 individuals who self-identify as vulnerable, found that more than three-quarters (78%) of vulnerable consumers said that they prefer some level of human contact when seeking help, as many feel AI tools fail to fully understand their unique needs, leading to delays and frustration.
Nearly half (48%) of those who identify as vulnerable also admitted to avoiding businesses entirely when they do not provide adequate support tailored to their needs: largely in the form of inadequate human interaction.
However, 56% of those surveyed felt that AI and technology could meet their needs just as well as a human could. This reflects a growing acceptance of digital solutions, indicating that while many still prefer human contact, there is an openness among some vulnerable customers to engage with AI-driven assistance, as the impact of this advancing technology continues to permeate all in society.
Critically, in striking the right balance between humans and AI, businesses need to understand the preferences of their customers and how they want to interact with the organisation.
Looking ahead
Many business leaders will be turning to their IT and customer experience directors to see how they can replicate the apparent success of businesses like Klarna in adopting AI while reducing agent capacity. Yet any customer service transformation project must consider the risks of failing to balance AI and the human touch and what impact it might have on customers.
Businesses have the most to gain by using technology in a way that supports and enhances the human experience, for both the agents and the customer – creating personalised and genuine interactions that solve customer issues in the shortest amount of time.
Hugo Farinha, Co-founder and CTO at Virtuoso QA on why AI is driving organisational change across financial services
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We’ve seen an enormous amount of discussion concerning all aspects of AI since the emergence of Chat GPT made it headline news. However, most articles and conversations focusing on its business impact seem to wilfully ignore the ‘elephant in the room’. Namely, the inevitable organisational change AI will usher in, especially for employees.
AI technology driving change
To ignore change is folly, and likely to have the exact opposite effect that businesses and AI technology vendors want. We can’t pretend workforces won’t be disrupted by such a seismic technological advance. Certain job roles will become obsolete. Business leaders can’t run the risk of creating a culture of fear and uncertainty among employees who are unlikely to be fooled.
It’s true AI could lead to leaner operations, particularly in insurance and finance companies, with fewer employees needed for routine tasks, but only half the story. Smart businesses will almost certainly reinvest cost savings into new growth areas that require specific human talent. Companies that maintain a strong human element in customer service and personalised offerings will differentiate themselves in a crowded market. The rise in AI-driven, agile companies will create faster market shifts and greater competition.
While AI has the potential for productivity and efficiency gains, and even to do the same with less if needed, I actually don’t predict major job culls in the next few years. AI is particularly good at data processing and data analytics, in insurance for example. So, when more data can be processed and analysed, human intervention can make better informed decisions as a result. In the short to medium term, data analysis and decision making will remain firmly in the human realm. But powered by AI.
The Future for Artificial Intelligence
Meanwhile, the technology is still evolving, and organisations need to build a model that layers over the top of AI – powered by it, rather than replaced by it. Despite the hype, we are still a long way from AI becoming an entity that can lead, implement and operate itself to a purposeful end. But it will increasingly power applications overlaid by strategic, human-led frameworks.
To achieve this, leaders must bring their teams with them on the journey. In the field of testing for example, developers have traditionally written code as part of their role. This is a very time consuming and laborious task. Historically skills gaps have led to delays in progress. But the ability to ‘outsource’ to AI has freed up the time of those developers to focus on the purpose of that code in relation to the product. And, ultimately, the customer. Similarly, leaders in all fields need a broader understanding of AI use cases such as these to make effective strategic decisions. For example, on hiring. Understanding when to bring in more people and when to bring in new technology to complement the skills of your existing team means understanding AI’s strategic implications, technical capabilities and limitations.
An Evolving Job Market
From the perspective of the employee, the job market will continuously evolve alongside AI advancements. It will require ongoing adaptation and learning to stay relevant. Skills such as empathy, communication, and negotiation will remain vital. These are differentiators and difficult for AI to replicate. Understanding AI tools and data analysis will be increasingly important, even for non-technical roles. The ability to adapt to new technologies and continuously learn will be essential. Moreover, as AI becomes more integrated, the need for professionals who understand the ethical implications and regulatory requirements will grow exponentially.
Driving growth and job creation in this new world will require a different mindset to the current received wisdom. From both employees and leaders. In addition to the advances and changes already discussed, AI also has the potential to level the playing field, enabling smaller or newer companies to compete more effectively with, and even seriously threaten, established players. With many traditional barriers to entry such as burdensome start-up costs removed, new business models are likely to emerge. In much the same way as they did in the early days of the internet. Investors will be on the lookout for the next ‘giant killer’.
This will create opportunities for those with the foresight to upskill, as well as for those looking to start their careers. Although those opportunities and the jobs of tomorrow may not yet be completely clear. What is clear, however, is that established businesses cannot afford to be complacent. Change is inevitable and empires can be toppled overnight by technology as disruptive as AI. By embracing it early, leaders in those businesses will have the opportunity to spot and fix the gaps and redundancies in their business models that the technology and its capabilities exposes before the market does so more painfully and publicly.
Our mission is to enable and lead the world’s quality-first revolution. QA tools haven’t kept up with the demands of the testing world. Virtuoso is here to deliver with AI-powered, low-code/no-code test automation to support the modern business.
“Virtuoso technology represents the foundation for software quality in the digital world, and we are proud to be a critical, guiding force in the era of AI.”
Cullen Zandstra, CTO at FloQast on mitigating the risks of AI to deliver benefits to financial services
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There’s a lot of buzz around Generative AI (GenAI). What’s not always heard beneath the noise are the very real and serious risks of this fast-developing AI tech. Let alone ways to mitigate these emerging threats.
Currently, one quarter (26%) of accounting and bookkeeping practices in the UK have now adopted GenAI in some capacity. That figure is predicted to grow for many years to come.
With this in mind, and as we hit the crest of the GenAI hype cycle, it’s critically important that leaders focus closely on the potential risks of AI deployment. They need to proactively prepare to mitigate them, rather than picking up the pieces after an incident.
Navigating the risky transition to AI
The benefits of AI are well-proven. For finance teams, AI is a powerup that unlocks major performance and efficiency boosts. It significantly enhances their ability to generate actionable insights swiftly and accurately, facilitating faster decision-making. AI isn’t here to take over but to augment the employees’ capabilities. Ultimately improving leaders’ trust in the reliability of financial reporting.
One of the most exciting aspects of AI is its potential to enable organisations to do more with less. Which, in the context of an ongoing talent shortage in accounting, is what all finance leaders are seeking to do right now. By automating routine tasks, AI empowers accountants to focus on higher-level analysis and strategic initiative, whilst drawing on fewer resources. GenAI models can help to perform routine, but important tasks. These include producing reports for key stakeholders and ensuring critical information is effectively and quickly communicated. It enables timely and precise access to business information, helping leaders to make better decisions.
However, GenAI also represents a new source of risk that is not always well understood. We know that threat actors are using GenAI to produce exploits and malware. Simultaneously levelling up their capabilities and lowering the barrier of entry for lower-skilled hackers. The GenAI models that power chatbots are vulnerable to a growing range of threats. These include prompt injection attacks, which trick AI into handing over sensitive data or generating malicious outputs.
Unfortunately, it’s not just the bad guys who can do damage to (and with) AI models. With great productivity comes great responsibility. Even an ambitious, forward-thinking, and well-meaning finance team could innocently deploy the technology. They could inadvertently make mistakes that cause major damage to their organisation. Poorly managed AI tools can expose sensitive company and customer financial data, increasing the risk of data breaches.
De-risking AI implementation
There is no technical solution you can buy to eliminate doubt and achieve 100% trust in sources of data with one press of a button. Neither is there a prompt you can enter into a large language model (LLM).
The integrity, accuracy, and availability of financial data are of paramount importance during the close and other core accountancy processes. Hallucinations (another word for “mistakes”) cannot be tolerated. Tech can solve some of the challenges around data needed to eliminate hallucinations – but we’ll always need humans in the loop.
True human oversight is required to make sure AI systems are making the right decisions. We must balance effectiveness with an ethical approach. As a result, the judgment of skilled employees is irreplaceable and is likely to remain so for the foreseeable future. Unless there is a sudden, unpredicted quantum leap in the power of AI models. It’s crucial that AI complements our work, enhancing rather than compromising the trust in financial reporting.
A new era of collaboration
As finance teams enhance their operations with AI, they will need to reach across their organisations to forge new connections and collaborate closely with security teams. Traditionally viewed as number-crunchers, accountants are now poised to drive strategic value by integrating advanced technologies securely. The accelerating adoption of GenAI is an opportunity to forge links between departments which may not always have worked closely together in the past.
By fostering a collaborative environment between finance and security teams, businesses can develop robust AI solutions. They can boost efficiency and deliver strategic benefits while safeguarding against potential threats. This partnership is essential for creating a secure foundation for growth.
AI in accountancy: The road forward
The accounting profession stands on the threshold of an era of AI-driven growth. Professionals who embrace and understand this technology will find themselves indispensable.
However, as we incorporate AI into our workflows, it is crucial to ensure GenAI is implemented safely and does not introduce security risks. By establishing robust safeguards and adhering to best practices in AI deployment, we can protect sensitive financial information and uphold the integrity of our profession. Embracing AI responsibly ensures we harness its full potential while guarding against vulnerabilities, leading our organisations confidently into the future.
Founded in 2013, FloQast is the leading cloud-based accounting transformation platform created by accountants, for accountants. FloQast brings AI and automation innovation into everyday accounting workflows, empowering accountants to work better together and perform their tasks with greater efficiency and accuracy. Now controllers and accountants can spend more time delivering greater strategic value while enjoying a better work-life balance.
Russ Rawlings, RVP, Enterprise, UK&I at Databricks, on the future of AI in FinTech
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Strict regulation, along with time and cost restraints, means financial services must take a measured approach to technological advancements. However, with the emergence of GenAI, particularly large language models (LLMs), organisations have an opportunity to maximise the value of their data to streamline internal operations and enhance efficiencies.
Embracing GenAI has never been more important for organisations looking to stay ahead of the curve. 40-60% of the global workforce will be impacted by the growth of AI. Moreover, global adoption of GenAI could add the equivalent of $2.6tn to $4.4tn in value annually to global industries. The banking sector stands to gain between $200-340 billion.
But whilst the financial services industry can gain incredible benefits from GenAI, adoption is not without its challenges. Financial organisations must prioritise responsible data management. They must also navigate strict privacy regulations and carefully curate the information they use to train their models. But, for companies that persevere through these obstacles, the benefits will be substantial.
Building customised LLMs for financial services
Consumer chatbots have brought GenAI to the mainstream. Meanwhile, the true potential of this transformative technology lies in its ability to be tailored to the unique needs of any organisation, in any industry. Including the financial sector.
Risk assessment, fraud prevention, and delivering personalised customer experiences are some of the use cases of custom open source models. Created using a company’s proprietary data, these models ensure relevant and accurate results. And are more cost-effective due to their smaller datasets. For instance, banks can use a customised model to seamlessly analyse customer behaviour and flag up any suspicious or fraudulent activities. Or, a model can leverage sophisticated algorithms to assess an individual’s eligibility for a loan.
Another huge benefit of these tailored systems is trust and security. Deploying a custom open-source model eliminates the need to share sensitive information with third parties. This is crucial for organisations operating within such a highly regulated industry. This approach also democratises the training of custom models. Furthermore, it allows organisations to harness the power of GenAI whilst retaining control and compliance.
Using data intelligence to boost AI’s impact
To truly harness the power of GenAI, organisations must cultivate a deep understanding of data across the entire workforce. Every employee, regardless of how technical they are, must grasp the importance of proper data storage. Also how data can be used to improve decision-making.
Organisations can use a data intelligence platform to help implement this. Built on a lakehouse architecture, a data intelligence platform provides an open, unified foundation for all data and governance. It operates as a secure end-to-end solution tailored to the specific needs of the financial services industry. By adopting such a platform, businesses can eliminate their reliance on third party solutions for data analysis. They can create a streamlined approach to data governance and accelerate data-driven outcomes. Users across all levels of the business can navigate their organisation’s data, using GenAI to uncover important insights.
The future of AI in the financial sector
The path to success lies in embracing GenAI as a canvas for crafting bespoke solutions. Whilst no two financial institutions are exactly the same, the industry’s tools must strike a delicate balance between supporting specific use cases and addressing broader requirements, Customised, open source LLMs and data intelligence platforms hold the key, sparking transformative change across the sector. These tailored solutions will empower financial businesses to integrate cutting-edge innovations and ensure security, governance and customer satisfaction. Organisations that embrace this change will not only gain a competitive edge, but also pave the way for larger transformations, re-shaping the financial landscape and setting new standards for the industry.
Databricks is the data and AI company with origins in academia and the open source community. Databricks was founded in 2013 by the original creators of Apache Spark™, Delta Lake and MLflow. As the world’s first and only lakehouse platform in the cloud, Databricks combines the best of data warehouses and data lakes to offer an open and unified platform for data and AI.
Pat Bermingham, CEO of B2B digital payment specialist Adflex, asks what impact will Artificial Intelligence really have on B2B payments?
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Visit any social media newsfeed and countless posts will tell you AI means “nothing will ever be the same again”. Or even that “you’re doing AI wrong”. The volume of hyperbolic opinions being pushed makes it almost impossible for businesses to decipher between hype and reality.
This is an issue the European Union’s ‘AI Act’ (the Act), which came into force on 1 August 2024, aims to address. The Act is the world’s first regulation on artificial intelligence. It sets out how to govern the deployment and use of AI systems. The Act recognises the transformative potential AI can have for financial services, while also acknowledging its limitations and risks.
Within the debate about AI in financial services, B2B payments are an area where AI has huge potential to accelerate digital innovation. Let’s go beyond the hype to provide a true perspective on what AI really means for B2B payments specifically.
Understanding what AI is, and what it isn’t
AI is a system or systems that can perform tasks that normally require human intelligence. It incorporates machine learning (ML). ML has been used by developers for years to give computers the ability to learn without being explicitly programmed. In other words, the system can look at data and analyse it to refine functions and outcomes.
A newer part of this is ‘deep learning’, which leverages multi-layered neural networks. This simulates the complex decision-making power of our brains. The deep learning benefits outlined later in this article are based on Large Language Models (LLMs). LLMs are pre-trained on representative data (such as payment/transaction/tender data). Deep learning AI does not just look at and learn patterns of behaviour from the data. It is becoming capable of making informed decisions based on this data.
Before we explore what this means for B2B payments, let’s make one caveat clear: human supervision is still needed to ensure the smooth running of operations. AI is a supporting tool, not a single answer to every question. The technology is still maturing. You cannot hand over the keys to your B2B payments process quite yet. Manual processes will retain their place in B2B payments. AI tools will help you learn, adapt and improve more quickly and at scale.
The AI Act – what you need to know
The Act attempts to categorise different AI systems based on potential impact and risk. The two key risk categories include:
Unacceptable risk – AI systems deemed a threat to people, which will be banned. This includes systems involved in cognitive behavioural manipulation, social scoring, and real-time biometric identification.
High risk – AI systems that negatively affect safety or fundamental rights. High-risk AI systems will undergo rigorous assessment and must adhere to stringent regulatory standards before being put on the market. These high risk systems will be divided into two further categories:
AI systems that are used in products falling under the EU’s product safety legislation, including toys, aviation, cars, medical devices and lifts.
AI systems falling into specific areas that will have to be registered in an EU database.
The most widely used form of AI currently, ‘generative AI’ (think ChatGPT, Copilot and Gemini), won’t be classified as high-risk. However, it will have to comply with transparency requirements and EU copyright law.
High-impact general-purpose AI models that might pose systemic risk, such as GPT-4o, will have to undergo thorough evaluations. Any serious incidents would have to be reported to the European Commission.
The Act aims to become fully applicable by May 2026. Following consultations, amendments and the creation of ‘oversight agencies’ in each EU member state. Though, as early as November 2024, the EU will start banning ‘unacceptable risk’ AI systems. And by February 2025 the ‘codes of practice’ will be applied.
So, with the Act in mind, how can AI be used in a risk-free manner to optimise B2B payments?
Today’s B2B payment platforms are not one-size-fits-all solutions; instead, they provide a toolkit for businesses to customise their payment interactions.
AI-based LLMs and ML can be used by payment providers to rapidly understand and interpret the extensive data they have access to (such as invoices or receipts). By doing this, we gain insights into trends, buyer behaviour, risk analysis and anomaly detection. Without AI, this is a manual, time consuming task.
One tangible benefit of this data analysis for businesses comes from combining payment data with knowledge of a wide range of vendors’ skills, products and/or services. AI could then, for example, identify when an existing supplier is able to supply something currently being sourced elsewhere. By using one supplier for both products/services, the business saves through economies of scale.
Another benefit of data analysis comes from payment technology experts. Ours have been training one service to extract data from a purchase order or invoice, to flow level 3 data, which is tax evident in some territories. This automatically provides the buyer with more details of the transaction, including relevant tax information, invoice number, cost centre, and a breakdown of the products or service supplied. This makes it easy and straightforward to manage tax reporting and remittance, purchase control and reconciliation.
AI-driven data analysis isn’t just a time and money-saver, however. It also adds new value by enabling providers to use the data to create hyper-personalised payment experiences for each buyer or supplier. For example, AI and ML tools could look out for buying and selling opportunities, and perform a ‘matchmaking supplier enablement service’ that recommends the best payment methods – and the best rates – for different accounts or transactions. The more personalised a payment experience is, the happier the buyer and more likely they are to (re)purchase.
Efficient data flows mean stronger cash flows
Another practical application of AI is to help optimise cash management for buyers. This is done by using the data to determine who is strategically important and when to pay them. It could even recommend grouping certain invoices together for the same supplier, consolidating them into one payment per supplier, reducing interchange fees and driving down the cost of card acceptance.
AI can also perform predictive analysis for cash flow management, rapidly analysing historical payment data to predict cash flow trends, allowing businesses to anticipate and address potential challenges proactively. This is particularly valuable in the current economic climate where cashflow is utterly vital.
By extracting value-added, tax evident data from a purchase order or invoice, AI can rapidly analyse invoices and receipts to enable efficient, accurate automation of the VAT reclaims process. Imagine: the time comes for your finance team to reclaim VAT on recent invoices and receipts, but they don’t have to manually go through every receipt or invoices and categorise them into a reclaim pile or not reclaimable. It sounds like a dream but it will be the reality for business everywhere: AI does the heavy lifting and humans verify it, saving significant time and resources.
The third significant benefit of AI is automated invoice reconciliation. By identifying key information from an invoice and recognising regular payees, AI can streamline and automate the review process. This has the potential to significantly speed up transactions and enable more efficient payment orchestration.
Binding together all supporting paperwork, such as shipping, customs, routes, and JIT (just-in-time) requirements can also be done by AI, and it’s likely to be less prone to human error.
This provides an amazing opportunity to make B2B payments faster, reduce costs and increase efficiency. Businesses know this: 44% of mid-sized firms anticipate cost savings and enhanced cash flow as a direct result of implementing further automation within the next three years. According to American Express, 48% of mid-sized firms expect to see payment processes accelerate, with more reliable payments and a broader range of payment options emerging.
When. Not if.
There are significant opportunities to leverage AI in B2B payment processes, making it do the heavy lifting. It is, however, essential to view these opportunities with a balanced understanding of the limitations of AI.
While all the opportunities for AI in B2B payments outlined here are based on relatively low-risk AI systems, human oversight of these systems is still essential. However, with all the freed-up time and resource achieved through the implementation of AI, this issue can be avoided.
AI in B2B payments is not an if, but a when. The question is, when will you make the jump, hand in hand with technology, rather than fearing it or passing full control over to it.
In order to grow, it is essential for users to see the tangible benefits. For example, by enhancing efficiencies in account payable (AP), businesses can reallocate time and resource previously spent in AP to other areas. Early adopters are starting to test the water but only time will tell how much of an impact AI will make.
Most businesses will likely wait for the early adopters to fail, learn and progress. If something goes wrong in B2B payments, it can have a huge impact on individuals, businesses and economises. Only when the risk is clearly defined and manageable will AI truly become the gamechanger in B2B payments that all the hype claims.
“Adflexhas been at the heart of the B2B fintech revolution from the beginning. We are known for fostering innovation and helping companies harness the power of digital payments. Our technology and expertise bring together buyers and suppliers to make transactions fast, cost-effective and straightforward to manage. We take the pain out of the supply chain by delivering seamless and secure payment integration that adds value to both buyers and merchants.”
Michael Donnelly, Head of Client Success at BlueFlame AI, on how to prepare your firm to attract and retain the next generation of AI talent
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In the fast-paced world of financial services, a new generation is stepping in with high expectations for generative artificial intelligence (AI) in the workplace. Recently, BlueFlame AI conducted a specialised training session for one of our private equity clients, aimed at their newly hired summer intern class. The experience was eye-opening for us. Furthermore, it also provided a great lesson in the growing importance of AI in the industry and the expectations today’s young professionals have as they enter the workforce
AI & LLMs
The comprehensive training session covered vital areas such as AI and Large Language Models (LLMs), a review of the most popular use cases the industry has adopted, and hands-on practical training in prompt engineering. Moreover, our goal was to show this next generation the skills they’ll need to leverage these tools effectively. New roles could revolutionise alternative investment management processes like due diligence, market analysis, and portfolio management.
We also used this as an opportunity to survey the group about their experience of and expectations for AI use in the workplace – and it yielded some striking insights. A significant 50% of the interns reported using ChatGPT daily, with 83% utilising it at least weekly. Furthermore, these numbers suggest young professionals expect these tools to be available to them in their professional lives. In the same way they are available in their personal lives and set to become as commonplace as traditional software in the workplace. The interns’ expectations regarding AI’s impact on their work efficiency are even more telling. An overwhelming 94% believe these tools will enhance their productivity, indicating strong faith in the technology’s potential to streamline tasks and boost performance.
These high expectations have key implications for employers. A significant 89% of interns expect their employers to provide enterprise-grade AI/LLM access. This statistic is a wake-up call for companies that have yet to invest in AI technologies, highlighting the need to stay competitive not just in terms of products and services but also in workplace technology provision.
Talent Acquisition & Retention
Perhaps most important is AI’s potential impact on talent acquisition and retention. One-third (33%) of interns surveyed indicated they would reconsider their choice of employer if they didn’t offer access to enterprise-grade AI/LLM tools. A response that could throw a serious wrench into any Financial Services firm’s hiring plans.
The message is clear for businesses looking to stay ahead of the curve when it comes to supporting their employees. Investing in AI technologies and training is no longer optional. Firms must be ready to meet the expectations of the incoming workforce. They need to provide them with the best technology to maintain a competitive edge in an increasingly AI-driven business landscape. Companies that embrace AI and provide their employees with the tools and training to harness its power will likely see significant productivity, innovation, and talent retention advantages.
AI Revolution
Private and public investment firms stand to benefit greatly from this AI revolution. As this new generation brings its enthusiasm and expectations for technology tools into the workplace, firms that are prepared to meet these expectations will be better positioned to tap into fresh perspectives, drive innovation and reap significant efficiency and productivity gains. And if firms can take a proactive approach to training and commit to developing a forward-thinking, AI-enabled workforce, they will be able to enhance their teams’ capabilities and shape the future of work in the financial sector.
Generative AI and the workplace expectations it has created mark a new paradigm in the market. The next generation of professionals is not just ready for AI – they’re demanding it. Firms that recognize and act on this trend will be well-positioned to lead the pack when it comes to innovation, efficiency and talent acquisition.
Founded in 2023 BlueFlame AI is the only AI-native, purpose built, LLM-agnostic solution for Alternative Investment Managers.
Financial institutions are increasingly turning to artificial intelligence (AI) to gain a competitive edge. AI tools streamline operations, improve customer…
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Financial institutions are increasingly turning to artificial intelligence (AI) to gain a competitive edge. AI tools streamline operations, improve customer support, and automate processes, making banks more efficient and customer-focused.
Research by McKinsey shows that over 20 percent of an organisation’s digital budget goes towards AI. The study links significant investments in AI to a 10-20 percent increase in sales. AI will play a central role in boosting efficiency, customer service, and overall banking productivity.
Introduction to AI in Personalised Banking
Delivering personalised experiences is crucial for customer satisfaction and retention. AI helps banks achieve this by collecting and analysing customer data. This data is then used to create recommendations, product offerings, and even financial advice tailored to each customer’s needs.
AI tools can optimise workflows through a technique called prescriptive personalisation, using past data to predict future behaviour. Real-time personalisation takes this further, incorporating current information alongside historical data.
This allows banks to deliver highly customised virtual assistants and real-time recommendations powered by natural language processing (NLP) models. These AI-powered assistants not only build trust and user engagement but also simplify interactions with the bank.
Tool 1: Predictive Analytics
Predictive analytics, powered by AI tools, unlock a new level of customer personalisation in banking. These tools analyse data to uncover hidden patterns and trends that traditional methods might miss. This knowledge reveals sales opportunities, possibilities for cross-selling, and ways to improve efficiency.
Predictive analytics use past data to forecast customer behaviour and market trends. This foresight allows banks to tailor marketing strategies and sales approaches to meet changing customer needs and capitalise on emerging opportunities.
Tool 2: Chatbots and Virtual Assistants
One key advantage of chatbots is their constant availability. This is especially helpful for customers who need assistance outside of regular operating hours.
AI chatbots learn from every interaction, improving their ability to understand and meet individual customer needs. By integrating chatbots into banking apps, banks can provide personalised banking experiences and recommend financial products and services that fit a customer’s specific situation.
Erica, a virtual assistant developed by Bank of America, handles tasks like managing credit card debt and updating security information. With over 50 million requests handled in 2019 alone, Erica demonstrates the potential of chatbots as efficient assistants for customers.
Tool 3: Recommendation Engines
Banks use AI tools to analyse vast amounts of customer data, including purchases, browsing habits, and background information. This deep understanding helps banks recommend products that truly fit each customer’s needs.
These personalised recommendations extend beyond credit card suggestions. AI can identify potential investments or loans that align with a customer’s financial goals. By providing customers with relevant information, banks allow them to make informed financial decisions.
Tool 4: Sentiment Analysis with AI
AI sentiment analysis translates written text into valuable insights. AI uses NLP to understand emotions and opinions in written communication. By examining things like customer feedback, emails, and social media conversations, banks gain a much clearer picture of customer sentiment.
Tool 5: Voice Recognition
AI-powered voice assistants offer a convenient way to handle everyday banking tasks. From checking balances to paying bills, all a customer needs are simple voice commands.
These assistants use NLP to understand customer requests and respond accurately. Voice authentication adds another layer of security by verifying customer identity during transactions.
Tool 6: Process Automation
Robotic Process Automation (RPA) automates repetitive tasks, boosting operational efficiency. It tackles up to 80 percent of routine work and frees up workers for more valuable tasks requiring human judgement.
RPA bots can handle tasks like issuing and scheduling invoices, reviewing payments, securing billing, and streamlining collections – all at once. NLP empowers these bots to extract information from documents, simplifying application processing and decision-making.
Tool 7: Facial Recognition with AI
Facial recognition helps banks verify customer identities during tasks like opening accounts, accessing information, and making transactions. Compared to traditional passwords, facial recognition offers stronger security and greater convenience. It eliminates the need for remembering complex passwords or worrying about stolen credentials, making banking interactions smoother and less error-prone. This technology also helps prevent fraud by identifying attempts to impersonate real customers.
Capital One AI Case Study
Capital One demonstrates how AI can personalise banking. Their AI assistant uses NLP to understand customer questions and provide immediate answers. Capital One also incorporates AI into fraud detection. Machine learning and predictive analytics help pinpoint suspicious credit card activity to strengthen security measures.
Conclusion
AI tools offer a significant opportunity for banks to improve customer experiences and achieve long-term success. By personalising banking services with AI, banks can better meet individual customer needs. This leads to higher satisfaction and loyalty, which enhances the bank/customer relationship.
AI has the potential for an even greater impact. As banks integrate more advanced AI capabilities, they can create even more engaging and personalised interactions. This focus on ‘hyper-personalisation’ could be the next big step for financial institutions to set them apart in a competitive market.
Banks are adopting artificial intelligence (AI) technology to provide more personalised experiences. A study by the AI Development Company projects…
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Banks are adopting artificial intelligence (AI) technology to provide more personalised experiences. A study by the AI Development Company projects that 75 percent of financial institutions will invest $31 billion in integrating AI into their existing systems by 2025. The trend is driven by customer demand for faster and more convenient banking options.
AI excels at analysing enormous amounts of data. This lets banks find patterns and trends to personalise customer service and boost efficiency. For example, AI-powered chatbots offer 24/7 help with basic questions, freeing up customer service staff for trickier issues. AI can also analyse customer behaviour to predict their needs and suggest relevant services or support, from personalised investment options to flagging suspicious account activity.
Benefit 1: Increased Efficiency
Long wait times and impersonal interactions often leave customers frustrated with traditional bank customer service. Fortunately, AI streamlines the experience by providing quick and accurate answers. It eliminates the need to navigate complex phone menus.
AI personalises interactions and saves customers from endless button-pressing and long hold times. AI in customer service can also analyse vast amounts of customer data. The data helps banks anticipate customer needs and recommend tailored solutions, preventing problems before they arise. This results in higher customer satisfaction and a smoother banking experience.
Benefit 2: Personalisation
AI can analyse vast amounts of customer data, including purchases and browsing habits, to create detailed customer profiles. These profiles help banks recommend relevant products and services that fit individual needs.
For instance, a customer who often pays bills online might be recommended a new budgeting tool. Similarly, someone who regularly saves for travel could receive information about travel insurance or currency exchange. These personalised suggestions can come through various channels, like the bank’s website, email alerts, or chatbots.
Benefit 3: Cost Savings
Cost savings are a major advantage of AI-powered customer service in banking. One key way AI achieves this is through automation. Chatbots powered by AI can handle many routine customer inquiries, freeing up human agents for complex issues. This reduces labour costs while also improving response times.
AI also helps with better staffing management. It can analyse past data to predict how many calls are coming in. Banks can then ensure they have the right number of agents available, avoiding overstaffing or understaffing that can significantly impact costs.
Benefit 4: 24/7 Support
Traditionally, reaching a support agent often meant waiting on hold during peak hours. However, AI in customer service is transforming the industry by offering immediate assistance through chatbots. These virtual assistants provide instant support the moment a customer reaches out.
Unlike human agents with limited working hours, chatbots are available 24/7. This ensures customers get help whenever they need it, regardless of location or time zone. This is especially valuable in the globalised world, where customers might need support outside of regular business hours.
A great example of this success is Photobucket, a media hosting service. After implementing a chatbot, they offered 24/7 support to international customers. This results in a three percent increase in customer satisfaction scores along with a 17 percent improvement in resolving issues on the first try.
Benefit 5: Multilingual Support
AI-powered chatbots offer multilingual support, breaking down language barriers and creating a positive banking experience. These chatbots can figure out a customer’s preferred language at the start of a conversation. This ensures clear communication, no matter what language the customer speaks.
Conclusion
A study by Global Market Insights predicts the conversational AI market will reach $57.2 billion by 2032. This technology is making big strides in banking, particularly by automating routine tasks and inquiries. By taking care of these repetitive tasks, AI frees up human agents to focus on more complex customer issues. This improves efficiency and helps banks manage their operating costs. A streamlined customer service experience builds trust and loyalty, which can lead to business growth for financial institutions.
McKinsey & Co. is seeing an increase in the number of clients seeking artificial intelligence-linked projects, reports Bloomberg. Faster adoption…
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McKinsey & Co. is seeing an increase in the number of clients seeking artificial intelligence-linked projects, reports Bloomberg. Faster adoption of the technology is helping the consulting titan and its peers boost revenue, across industries like Insurtech, following a period of tumult.
About 40 per cent of the New York-based firm’s client projects involve the technology. The number of AI-related customers in the past 12 months is approaching 500, Rodney Zemmel, senior partner and head of the firm’s digital business, said in an interview.
“We believe the long- or the medium-term economic implications are very real,” Zemmel said. He was a final candidate in the recent global managing partner leadership elections at the firm. According to people familiar with the matter, who asked not to be identified discussing confidential information.
Though there’s some degree of hype around AI, “we’re seeing the organisations that are doing that are getting value from it,” Zemmel said. “It’ll be a little longer, and maybe, a little harder than people think, but we’ve got no doubt that the value is there,” he added.
AI adoption across Insurtech
Among those deploying automation rapidly are the traditional and regulated industries such as banking and insurance, Zemmel said. In a June report, Citigroup Inc. said AI is poised to upend consumer finance and make workers more productive. Additionally, with a high potential for 54 per cent of jobs across banking to be automated. Citi also said that the technology could add $170 billion to the industry’s coffers by 2028.
JPMorgan Chase & Co. Chief Executive Officer Jamie Dimon has called AI “critical” to his company’s future success. He also noted the technology can be used to help the firm develop new products, drive customer engagement, improve productivity and enhance risk management.
The surge in automation has come as a relief for the broader consulting industry. It has been battling a slowdown in demand for its traditional services. McKinsey, Ernst & Young and PricewaterhouseCoopers have been cutting jobs to weather the slump. Furthermore, Accenture Plc shares tumbled in March after the company warned it’s seen financial-services customers, including Insurtech, rein in spending on its software.
AI’s rise is also diverting some budgets toward specialist consultancies. Although AI-focused units like McKinsey’s QuantumBlack are growing rapidly, according to Zemmel.
McKinsey – QuantumBlack
McKinsey, which has advised everyone from the U.S.’ Pentagon to China’s Ping An Insurance Group Co., currently has about 2,000 people working across QuantumBlack. It has 7,000 staff in total in tech-related fields, according to Zemmel’s estimates. McKinsey’s headcount stood at about 45,000 globally as of 2023 and revenues were at a record $16 billion.
Zemmel said that the firm is still evaluating how the use of AI will impact its own headcount over the longer run. McKinsey had earlier warned about 3,000 of its consultants that their performance was unsatisfactory and will need to improve.
“We’re certainly planning on being agile about it,” Zemmel said. “One thing that’s clear is everybody in our organization’s going to need to know how to use AI and incorporate in their day-to-day work if they’re going to remain relevant to their clients.”
AI-powered threat detection, automation, and data analysis are empowering fintech cybersecurity teams to more effectively meet the challenges of an evolving world.
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Artificial intelligence (AI) is driving a new generation of modern cybersecurity solutions. The technology is transforming how organisations protect against evolving digital threats, as predictive and big data analytics bring new benefits to the sector.
How is AI transforming cybersecurity for fintech teams?
AI’s importance in cybersecurity lies in its ability to provide advanced threat detection, automate responses, and adapt to evolving threats. It can also handle large amounts of data, making monitoring networks and detecting issues easier without increasing risks.
AI learns from past experiences, recognising patterns and improving over time. This makes it good at spotting weak passwords and alerting the right people. AI can also block harmful bots that try to overload websites. AI automates large amounts of tasks, allowing for 24/7 monitoring and quicker responses to security threats.
Its machine learning algorithms analyse vast datasets in real-time, identifying patterns and anomalies to detect emerging threats. As AI excels in behavioural analytics, it establishes a baseline of normal behaviour to spot deviations that indicate security threats.
Unlike traditional methods that rely on predefined signatures, AI can identify zero-day threats—new and previously unknown vulnerabilities—promptly. This proactive approach allows organisations to respond swiftly, preventing potential breaches before they occur.
AI also enhances threat intelligence by automating the analysis of code and network traffic, freeing up human analysts for more complex tasks. It, in turn, facilitates automated incident responses, rapidly mitigating attacks and minimising damage.
Predictive AI in Fraud Detection
AI is revolutionising fraud prevention by using predictive and behavioural analysis to detect and prevent fraudulent activities. By analysing historical data, AI identifies patterns that often precede fraud. This approach not only enhances detection accuracy but also reduces false alarms, distinguishing between normal and suspicious behaviours with greater precision.
In real-time, AI monitors multiple transactions simultaneously, flagging suspicious activities as they happen to mitigate risks promptly. It learns individual customer behaviours to detect anomalies, such as large transactions or unusual patterns. These triggers prompt alerts for investigation or automated protective measures, such as account freezing.
Despite challenges such as data privacy and the need for extensive datasets, AI’s advancements in machine learning promise increasingly effective solutions for protecting financial systems.
Industry case studies: Vectra and Kasisto
Fintech companies like Vectra use AI-powered technologies such as Cognito to automate threat detection and response. These systems analyse vast datasets to detect and pursue cyber threats swiftly, ensuring comprehensive security measures against malicious activities.
Tools like Kasisto’s KAI enhance customer experiences by providing personalised financial advice through AI-driven chatbots. This demonstrates AI’s versatile applications in improving both security and service delivery within the fintech sector.
AI’s use cases in cybersecurity are expected to increase. AI will revolutionise how users are authenticated. It will use advanced biometric analysis and behaviour tracking to make it harder for unauthorised users to gain access while ensuring a smooth experience for legitimate users.
This approach strengthens security by verifying identities with methods like fingerprints or facial recognition and detects unusual behaviours for added protection. AI’s ability to learn continuously from new data means cybersecurity systems will become smarter and more effective over time, adapting quickly to new threats.
The growing complexity of financial markets presents new challenges for asset and wealth managers. Therefore, to navigate this evolving environment,…
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The growing complexity of financial markets presents new challenges for asset and wealth managers. Therefore, to navigate this evolving environment, many are embracing artificial intelligence (AI) for assistance with investment decisions. AI acts as a powerful tool, improving efficiency and effectiveness across various aspects of asset management.
From analysing market trends to building diversified portfolios, AI’s strength lies in processing massive amounts of data. Furthermore, it uncovers hidden patterns empowering managers to make data-driven investment choices across financial services.
Introduction to AI in Asset Management
Asset management involves managing investment portfolios for individuals, institutions, and businesses. This includes stocks, bonds, real estate, and other financial assets. The main goal is to grow value over time while minimising risk and meeting client goals.
AI is transforming asset management with its data processing and analytics capabilities. Additionally, AI algorithms can quickly analyse massive amounts of financial data, market trends, and economic indicators. This helps uncover hidden patterns and connections that human analysts might miss. A data-driven approach empowers asset managers to make better investment decisions and develop more accurate market forecasts.
Portfolio Management
AI is transforming asset management by offering powerful tools for better decision-making. Moreover, machine learning (ML), AI analyses vast amounts of historical market data to identify patterns and predict future trends, providing valuable insights for building portfolios.
Natural language processing (NLP) lets computers understand human language. NLP can unlock information from unstructured sources like news articles, social media, and analyst reports. The algorithms then analyse sentiment and extract key information that feeds into portfolio decisions.
AI optimisation algorithms help construct optimal portfolios. These algorithms consider risk tolerance, return goals, and investment limitations. By using these tools, portfolio managers can create portfolios designed to maximise returns while minimising risk.
Risk Management
AI is changing how investment decisions are made. The AI algorithms can analyse massive amounts of historical market data and complex risk models.
The analysis provides a deeper understanding of individual asset risk and the overall portfolio’s exposure. With this knowledge, investment managers can proactively identify potential risks and develop strategies to lessen them.
AI offers real-time risk monitoring. An AI-powered system continuously tracks portfolio performance, alerting managers to any significant changes in risk. This allows for swift adjustments as market conditions evolve.
Automated Trading
Traditional automated trading tools execute trades based on pre-programmed instructions from human traders. These tools function within the parameters set by the user and can’t analyse markets on their own.
AI offers truly independent systems with tools that can analyse markets using technical and fundamental analysis with minimal human input.
AI uses sentiment analysis, ML, and complex algorithms to process vast amounts of information and identify trends. This data-driven approach removes the emotional bias that can affect human traders.
Case Studies
The asset management industry is seeing a rise in firms using AI to improve performance. A recent example isDeutsche Bank’s collaboration with NVIDIA. This multi-year project aims to integrate AI across their financial services. This includes virtual assistants for easier communication and AI-powered fraud detection. The bank expects faster risk assessments and improved portfolio optimisation.
Morgan Stanley is also making strides in AI adoption. Partnering with OpenAI, their financial advisors now have access to a massive research library at high speed. Advisors can explore client portfolio strategies and find relevant information in seconds, leading to better-informed advice.
Future Prospects
APwC report predicts artificial intelligence will significantly boost global GDP, contributing up to $15.7 trillion in 2030. This advancement could reshape asset management in the coming years, leading to entirely new business models and investment strategies.
One future possibility involves fully automated investment platforms powered by AI. These platforms would manage investment portfolios with minimal human involvement and use real-time data analysis to create personalised investment plans.
Moreover, AI could pave the way for more dynamic investment strategies that respond to market changes. By constantly analysing market conditions, AI can automatically adjust investment portfolios to optimise returns and minimise risks. This could lead to more resilient and adaptable investment systems that are better equipped to navigate various market environments.
Customer service significantly influences the overall customer experience and brand reputation. Artificial intelligence (AI) has taken customer service to new…
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Customer service significantly influences the overall customer experience and brand reputation. Artificial intelligence (AI) has taken customer service to new heights, including in the insurance industry.
Financial technology development has offered a better customer experience with enhanced accessibility and convenience. Mobile banks and digital wallets make it possible to contact the customer service team through online platforms. With the help of AI, FinTech companies escalate their services by offering more personalised, prompt, and efficient service.
AI Chatbots and Virtual Assistants
Conversational AI, which focuses on creating human-like interactions like chatbots and virtual assistants, improves customer service efficiency.
Chatbots are automated programmes that promptly address customer service queries. They can assist customers with inquiries and provide support for product information, account balances, or transaction details. AI-powered chatbots can give an immediate response and handle multiple customers at the same time.
Meanwhile, virtual assistants are voice-activated apps that can comprehend and carry out tasks based on users’ commands. These assistants offer personalised support by understanding the customers’ needs. For instance, they can deliver investment guidance tailored to customers’ risk tolerance and financial objectives.
These AI solutions can also assist human assistants by handling routine tasks, allowing them to focus on more complex work. Thus, the employment of AI assistants can reduce operational costs and effectively allocate resources to more important tasks.
Personalised interactions with AI
This approach can provide more personalised interactions by using algorithms and predictive tools to understand and respond to each customer’s preferences. AI algorithms can analyse large datasets of customers’ past interactions, browsing behaviour, and demographic information.
Meanwhile, predictive analytics tools can be used to anticipate customer needs and offer relevant financial products or services. These recommendations are constantly updated based on real-time client interactions and feedback.
24/7 Support
AI-powered customer service has the benefit of around-the-clock availability. It can operate continuously without being bound by office working hours like human-based customer service. Faster response times and enhanced availability help FinTech companies improve overall customer satisfaction.
Case Studies
Paypal, a digital wallet company, is one of the FinTech companies that has successfully used AI to improve its customer service. After implementing chatbots, PayPal experienced a 20 percent decrease in customer support costs and a 25 percent increase in user engagement. These chatbots can handle routine inquiries, resolve issues, and make personalised product recommendations.
Another example is Citi, a US retail bank that developed an AI-powered Customer Analytic Record (CAR). This programme can consolidate customer data, including financial records, used products, and interactions across online banking. The data is linked to automated decision-making AI software for analysis. The system can then recommend personalised offers to customers via text and mobile banking.
Future prospects
According to David Griffiths, Citigroup’s chief technology officer, AI has the potential to revolutionise the banking industry and improve profitability. With the continuous development of AI technology, the fintech industry can further improve its customer service.
Ronit Ghose, another executive at Citigroup, predicts that in the future, every client will have an AI-powered device in their pocket. This innovation will improve their financial lives with enhanced AI in customer service.
However, there are still concerns about AI’s scalability limitations in handling vast amounts of tasks. In addition, AI’s access to customers’ data makes security an important area to ensure its credibility. FinTech companies should ensure digital compliance to earn customers’ trust.
Financial service sectors are undergoing significant transformation driven by the adoption of AI.
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From established institutions to innovative FinTech startups, financial organisations are embracing AI technology to improve their offerings and operations.
A report by Statista projects global investment in AI for financial services to reach a staggering $26.5 billion by 2025, highlighting the growing importance of AI in finance. Additionally, given the significant impact of AI technology, this article will explore the top 10 AI applications transforming the financial services sector.
Introduction to AI in Financial Services
The financial sector is grappling with a growing tide of data and intricate market dynamics. Furthermore, AI technology has emerged as a powerful tool to navigate this complexity. ML models, for instance, can analyse vast amounts of transaction data in real-time, identify unusual patterns, and flag potential fraudulent activities.
Moreover, AI’s impact extends to automating manual tasks that burden financial institutions. AI tools can efficiently process large datasets, generate reports, and handle administrative duties. Also, this shift towards automation allows financial institutions to focus their resources on higher-value and strategic endeavours.
1. Signifyd
Signifyd offers a comprehensive Commerce Protection Platform designed to empower businesses with a holistic approach to fraud and abuse prevention.
By using machine learning (ML) models, Signifyd’s Fraud Protection ensures exceptional accuracy in eliminating fraudulent transactions while automating order approvals. Additionally, this is further bolstered by Abuse Prevention, a feature that addresses customer abuse behaviours and simultaneously rewards legitimate customers.
2. KAI
Kasisto offers a conversational AI platform, KAI, designed to enhance customer experiences within the financial sector. KAI tackles two key challenges for banks: reducing call centre volume and empowering customers. Equally important, it achieves this by providing self-service options and solutions through AI-powered chatbots.
If a customer inquiry extends beyond the chatbot’s capabilities, KAI seamlessly transfers the conversation to a human customer service representative, ensuring a smooth handover and comprehensive resolution.
3. Entera
Entera, an AI application designed for residential real estate investors, streamlines the entire investment lifecycle. Combining SaaS tools and expert services, Entera empowers investors to buy, sell, and manage single-family homes. Furthermore, the platform grants access to a comprehensive database of on-market and off-market properties, simplifies transaction processes, and facilitates market trend discovery.
4. Range
Aimed at simplifying wealth management, Range offers a unique blend of AI technology and human expertise. This unique approach integrates investment management, tax planning, and estate planning services, all accessible through a user-friendly interface. Tailored to meet individual goals through a unified view of all financial activities, Range also offers clients the guidance of certified financial planners when needed.
5. Zest AI
Zest AI uses ML and artificial intelligence to address challenges in credit risk assessment for financial institutions. Their platform analyses vast datasets to identify patterns missed by traditional models, addressing longstanding challenges faced by financial institutions. Also, this AI technology aims to reduce lending bias, improve risk prediction, and expand access to credit for borrowers.
6. Upstart
Upstart is a fintech company using AI technology to improve credit accessibility. Their AI-powered lending platform assists financial institutions in making informed lending decisions by analysing a broader spectrum of data beyond traditional credit scores. This approach aims to expand credit inclusion, allowing borrowers with limited credit history to qualify for loans.
7. Proofpoint
Proofpoint offers a suite of cybersecurity solutions designed to shield organisations from sophisticated cyberattacks and compliance concerns. This AI application addresses people, data, and brand protection, encompassing areas like email security, data loss prevention, and threat intelligence. Recognizing people as the most susceptible targets, Proofpoint prioritises a human-centric approach to ensure the very foundation of an organisation’s security posture is fortified.
8. Brighterion
Brighterion tackles complex decision-making across industries like finance and healthcare with its unique model-based AI technology This model-based system utilises Smart Agents, enabling it to personalise, adapt, and continuously learn.
After analysing and observing data, the platform creates virtual profiles that update in real-time. This allows for a holistic one-on-one analysis, granting organisations a comprehensive 360-degree view of each entity’s behaviour.
9. Kavout
Kavout stands out in the industry by harnessing the power of ML and quantitative analysis. This approach allows them to process vast amounts of unstructured data and identify real-time patterns within the financial markets.
One of Kavout’s core solutions is the K Score, an AI-powered stock ranking system. Furthermore, by analysing this massive data pool, the K Score condenses the information into a single numerical ranking for each stock.
10. Trumid
In the fixed-income trading space, Trumid is a company using advanced analytics and AI to optimise the credit trading experience. Their suite of data-driven tools and proprietary Fair Value Model Price offers real-time pricing intelligence for over 20,000 USD-denominated corporate bonds. In addition, this engine analyses and adapts to market fluctuations, equipping traders with valuable insights to guide data-driven trading decisions.
FinTech Strategy and Interface joined Publicis Sapient at Money20/20 in Amsterdam for the launch of its third annual Global Banking Benchmark Survey and spoke with Head of Financial Services Dave Murphy about its findings
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The third annual Global Banking Benchmark Study from Publicis Sapient draws on insights from 1000+ senior executives in financial services across global markets. The study focuses on the goals, obstacles, and drivers of digital transformation in banking.
Global Banking Benchmark Study
The study was launched during Money20/20 Europe in Amsterdam last month. Eoghan Sheehy, Associate MD, and Grace Ge, Senior Principal, highlighted the banking industry is focused on improving existing processes rather than introducing new ones. Data Analytics and AI are identified as key priorities for digital transformation. Additionally, there is a focus on internal use cases and efficiency.
Eoghan and Grace also discussed the challenges faced by the banking industry. These include regulation, competition from companies like Amazon, and the need to attract talent. They emphasised the importance for financial institutions of modernising core infrastructure. Also, building cloud infrastructure to support ongoing digital transformation. Moreover, the study notes the prevalence of the development of custom-made tools and internal use cases for AI implementation. Furthermore, Eoghan and Grace provided examples of repeatable use cases and discussed the success factors for Data Analytics and AI.
Four key takeaways from Publicis Sapient
Four key tracks came out of the study…
Modernising the core will always be important. But modernising the core for its own sake and also building the cloud infrastructure that supports it or allows for it to be modern. A decent chunk of the survey responders are still very focused on this. Executives are stating they want to make sure their people can make the best use of the beautiful core they’ve now built.
GenAI is an area of thoughtful experimentation for the Neobanks. We’re talking about scaled microservices here. Instances where, across Neobanks, you’ll have the same machine learning model and the same GenAI text generator facilitating retail and SMEs. That’s pretty sophisticated and something everyone has to contend with.
Data Analytics transformation is a key priority using GenAI to do so along with bringing new talent into the game.
Payments has been a big theme at Money20/20… We’re seeing lots of activity around ancillary individual product areas.
“The study focuses on how to think about solving problems end-to-end. Banks are dealing with legacy issues and taking a customer first view into solving the challenges. The practical application of AI across the banks is a significant theme as they look to automate decision-making and deliver better credit risk models. AI is finally delivering a set of use cases that truly can impact the way banks operate and build their own technology.” Dave Murphy, Head of Financial Services, EMEA & APAC
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Traditional evaluation processes for credit scoring and analysis for risk management are being elevated with AI.
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This innovation is driving financial inclusion for people around the globe who don’t have traditional access to financial institutions. Equipped with the correct algorithm and capability to assess big data sets accurately, AI is the ideal assistant.
Using a machine learning model, AI in credit scoring will continue to develop and upgrade itself the more we use it. New advanced algorithms can be expected. AI will be able to process bigger sets of data and produce more accurate results. This means a bigger scope of potential borrowers can be accessed, while making the lenders’ work lighter.
As has been seen, this function of AI is used in real-time by several US-based finance companies, such as Ocrolus that provides financial documents review services. They’re using AI to achieve 99% accuracy in their results.
The next step to further AI’s advances is by putting more effort in training it, making it a sharper tool.
How AI is becoming essential to credit scoring
Credit scoring is one of the main ways to assess potential borrowers and help decide whether they’re eligible for mortgages, business loans, or even credit cards. It also helps determine the terms they are offered, and the amount they can borrow.
AI is essential in this area because much of credit scoring is dependent on providing financial evidence as a guarantee, usually in the form of employment payslips or assets. New potential borrowers are less likely to have assets and are in an economy where self-employed, contract, and gig work is increasingly the norm.
Then there are those who are ‘unbanked’, who don’t have any savings – that includes 1.5 billion people.
New technology means data sourcing can become broader and more inclusive. This creates new borrower categories to consider, making it possible for financial institutions to reach more borrowers who previously could not be assessed.
AI Boosts Accuracy and Efficiency
Credit scoring must be done thoroughly, and that is a process that takes time and effort when done manually.
Once the process is established, it can follow protocol and move much faster. AI’s power makes it much easier to go from identifying a new model for credit scoring to being able to roll it out reliably at scale
Machine learning means all data AI analyses feeds into the processing system. AI is trained by analysing a bulk of data consisting of transaction history, debt history, and payment history. All of which are the main points of traditional data scoring.
But, instead of only training to do this repeatedly and accurately, AI will detect previously unseen patterns. This will help predict future behaviours of potential borrowers, such as their probability of repaying on time, from groups that do not have good access to credit.
AI in risk management and assessment
When it comes to risk management, the more accurate the analysis, the better. With AI evaluating larger sets of data with more data sources, the results can be more personalised.
The model also helps the system to monitor the activities in real time using advanced and adjusted tools. Therefore, the outcome itself will always be the most up to date and precise. In a more advanced scenario, the tools can even predict based on previous patterns, giving them a function to prevent.
Real-life, real-time examples
Aside from risk assessment and data analysis, AI also contributes to many other factors. It can be used for fraud detection based on patterns that it recognises. It can also create personalised offers based on an individual’s data analysis.
The usage of this type of AI and the tools it creates is already being applied. Enova, a US-based financial technology company, uses AI to complete its credit assessment. With more advanced updates every year, we can expect even more companies in different industries utilising AI.
Future trends that must be nurtured
The biggest challenge moving forward is how much effort we want to put in to evolve the AI we have now, as the complexity grows and bigger effort is needed. Evidently, AI banking solutions help bring huge impacts, so attention is now shifted to updating them furtther.
The assistance AI brings to overall credit scoring and risk management in general will easily outweigh the complexity of its introduction. The more patterns and data AI consumes, the more accurate the results and powerful its feedback loop. Credit scoring is possibly the most impactful application of AI in financial services for the future of consumers.
Artificial intelligence is fundamentally changing how businesses operate, and the banking and finance sector is no exception.
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Furthermore, the integration of AIinto banking apps and services has driven a shift towards a more customer-centric and technologically advanced industry.
AI-powered systems improve efficiency and decision-making within banks – but they also offer significant cost reductions. A 2023 McKinsey report on banking highlighted the potential for AI to increase productivity by 5% and generate global cost savings of up to $300 billion.
Introduction to AI in Banking
Automation in banking has evolved rapidly… Starting from basic work and Robotic Process Automation (RPA), to deploying AI in data analysis and eventually to sophisticated applications that impact core areas like risk management and fraud prevention.
AI’s deployment in advanced data analytics helps combat fraud and improve compliance. Meanwhile, AI models can streamline anti-money laundering measures, completing tasks in seconds that previously took hours or days.
AI’s data processing speed allows banks to uncover valuable insights that fuel AI development in chatbots, payment advisors, and fraud detection. This translates to a better customer experience for a wider audience, potentially boosting revenue, lowering costs, and improving bank profitability.
Understanding Customer Behaviour
Successful applications in functions that represent relatively “easy wins” have helped shift the focus to customers.
AI unlocked a new level of customer understanding. By analysing everything from spending habits to online behaviour, AI usesd machine learning to predict customer behaviour and tailor services accordingly.
This deep insight helps banks with AI strategies to be proactive. For instance, AI can identify patterns that indicate a customer may soon switch banks. Armed with this knowledge, banks retain customers by offering personalised incentives or targeted offers.
AI analysis of customer data to gain insights into spending habits, savings patterns, and investment preferences. Banks can use these insights to tailor marketing campaigns, enhance customer service interactions, and create new products and services that directly address the evolving needs of their customers.
A rising demand for more personalised customer experiences has dovetailed with the development of generative AI. The latter’s ability to learn, create, predict – and then communicate, promises a further revolution in banking technology and strategies. It also offers a method of automating delivery of better customer experiences at scale.
Personalised Product Recommendations
By implementing AI models, banks can now offer products and services that are tailored to each customer’s unique financial situation and future needs. This shift towards personalised product recommendations fosters deeper customer relationships and loyalty.
Personalised product recommendations ensure customers are only approached with offers that are likely to interest them, optimising the cross-selling and up-selling of financial products. This targeted approach not only increases the success rate of product offers but also reduces the inefficiency of blanket marketing campaigns.
Better Customer Service
AI-driven chatbots are revolutionising customer interactions in the banking sector. These virtual assistants provide personalised, round-the-clock experiences. Powered by natural language processing (NLP), chatbots understand and respond to customer queries in a manner akin to human communication.
This AI strategy allows customers to receive immediate assistance with any banking matter, eliminating the need for long queues or frustrating phone calls. Customers can get instant assistance with various banking matters – from checking account balances and transferring funds to even applying for loans – all through a simple conversation.
Case Studies
Facial and voice recognition are becoming increasingly sophisticated thanks to AI’s ability to analyse vast amounts of data and refine authentication processes. These advancements not only enhance security but also contribute to personalised customer experiences.
A recent example is NatWest, the first major U.K. bank to leverage AI-powered biometrics for remote account opening. Developed with HooYu, the system uses real-time biometric matching to verify a customer’s selfie against official identification documents.
Another example comes from JPMorgan Chase, where researchers use AI and deep learning techniques to develop an early warning system for malware, trojans, and phishing campaigns. This system can identify threats before they occur, providing crucial time for the bank’s cybersecurity team to take preventative measures. These approaches show how AI strategies are shaping the future of banking tech.
Future Outlook
AI has the potential to revolutionise how financial institutions operate and interact with customers.
There is a major security challenge that comes with it. Banks have to prioritise cybersecurity measures to keep sensitive data protected from unauthorised access or accidental disclosures. There are also serious privacy concerns over the use of customer data.
Financial institutions have their own unique vocabulary and styles of communication. While this may seem a disadvantage, these emerged for ease of communication and specificity – and that means AI will be able to both learn and use the same methods finance workers are versed in. AI will likely become a companion tool for individuals within the industry, just as it will be for customers of it. Each will empower and improve the other.
The financial services industry has always been racing to implement the newest technologies. Back in the 1960s, various financial institutions…
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The financial services industry has always been racing to implement the newest technologies. Back in the 1960s, various financial institutions competed to introduce ATMs. In the 2020s, it’s AI’s turn to deliver the utmost value to fintech customers.
Modern finance infrastructure relies on AI-based fintech trends and solutions. Applications such as Venmo, Paypal, Wise, Apple Wallet, and other apps are the primary examples. With them, users can purchase insurance, apply for loans, or buy cryptocurrency without leaving their homes.
With the growing demand for fintech services, the rise of AI is rapidly reshaping the future of fintech itself. According to NVIDIA’s State of AI in Financial Services: 2024 Trends Survey Report, 43 per cent of global financial services professionals already use generative AI in their organization. Forty-six per cent of them are already using large language models (LLMs), too.
Catching up with AI trends is mandatory in maintaining a competitive edge. The NVIDIA report reveals that 97 percent of surveyed companies plan to quickly invest in more AI tools. By next year, projections suggest that the globalAI in finance technology market will rise to $26.6B.
Here are ten of the top AI trends expected to influence the fintech industry:
1. Customer Insights
Many AI tools enable analysts to crack customer behaviour and preferences. From the data, fintech companies can craft even more personalised experiences.
Customer insights can be inferred from various sources. For example,HSBC’s AI tool analyses a customer’s transaction history, coupled with their social media activity, to provide investment advice and product offerings. The approach has been said to improve customer satisfaction and retention rates.
Another digital banking company, Revolut, uses machine learning algorithms to perform similar tasks. It provides AI-based budgeting and investment advice, as well as financial planning strategies.
2. Robo-advising
More financial institutions are exploring chatbots and virtual assistants with the ability to provide recommendations. According to research by Polaris, the Robo-advisor market is anticipated to grow from $7.39B in 2023 to $9.5B in 2024.
NVIDIA’s report also reveals that 34 per cent of financial services professionals sought AI’s help to enhance the experience of their customers. For instance, Bank of America’s virtual assistant, Erica, is equipped with AI insights to provide customers with real-time assistance.
3. Customer Onboarding with AI
It is commonly known that customer onboarding processes, especially in financial services, are often time-consuming. Many companies are looking to counter this by using AI tools that can automate compliance checks and document processing.
For example, the Oxford startup Onfido uses its proprietary AI, Atlas, to automate identity verification during customer onboarding. Atlas’s method include cross-referencing documents like passports and driver’s licenses with facial biometrics.
4. Robotic Process Automation (RPA)
Robotic Process Automation, as the name suggest, is a way to automate repetitive tasks. In various companies across the world, this technology has been transforming back-office operations.
By increasing effectivity, RPA allows companies to focus on value-added activities. JPMorgan Chase, for example, is able to cut the time to analyse legal documents through its COIN (Contract Intelligence) platform. The bank claims that COIN allows it to reallocate its resources to more strategic business endeavours.
5. Investment Management
More often than not, companies that use artificial intelligence systems to manage their investment benefit from better portfolio diversification. Independent investors who have converted to AI-driven services, too, seek ways to maximise the returns on their investment.
Its platform formulates personalised investment plans based on risk tolerance and financial goals. With Wealthfront, investors also gain access to continuous portfolio optimization and tax-efficient investing.
6. Credit Scoring with AI
Traditionally, scoring models only process limited data. This can often lead to biases, especially for outdated models. In comparison, AI-based credit scoring that analyze broader data sources can assess creditworthiness in a more accurate manner.
This means improved access for underserved populations, on top of reducing default rates for lenders. California-based Zest AI, for instance, offers an AI-powered credit scoring platform that uses a tool called FairBoost to give a more holistic view of a borrower’s creditworthiness.
Regulatory technology, whichdemand jumped last year, is a resource-intensive area for financial institutions. Therefore, AI automation has been a huge help in streamlining its processes.
In the field, artificial intelligence helps to guarantee financial institutions adhere to regulatory standards more efficiently and effectively. For example, De Nederlandsche Bank uses AI data analytics to detect networks of related entities. The process assesses the exposure of financial institutions to networks of suspicious transactions.
8. Payment Processing with AI
A lot of fintech companies are looking into AI to perfect their payment processes in terms of speed and security. The integration results in increased customer satisfaction, both for B2B and B2C companies.
The multinational finance company Stripe, Inc., for example, use AI tools to empower its digital payments processing. Now, customers can manage recurring billing effortlessly thanks to its advanced AI agents.
Stripe has also collaborated with Microsoft’s Azure OpenAI team to integrate GPT-3 for its support services.
9. Blockchain and Cryoto-related Services
AI improves the security and efficiency of blockchain and cryptocurrency transactions drastically. Some tools can perform difficult tasks such as predicting price movements, and optimise trading strategies.
A standout example is the American blockchain firm Chainalysis. For some time, the company has been helping prevent fraud and other illicit activities in the crypto space.
10. AML Compliance
Created to prevent financial crimes, Anti-Money Laundering (AML) regulations can benefit from the use of artificial intelligence. When integrated into the system, AI tools can efficiently detect malicious activities, which results in expedited AML processes.
For example, the financial crime detection company AyasdiAI creates AI application Sensa to help institutions with anti-money laundering (AML) compliance. AyasdiAI’s platform identifies suspicious activity patterns that traditional methods might miss. Its method reduces false positives in AML compliance efforts and increases overall accuracy.
AI in Fintech’s future
The trends outlined in this article represent the future of the fintech industry.
AI’s role in fintech will only continue to grow with more companies investing in its development. Soon, artificial intelligence will take on more sophisticated tasks that add to the value of fintech products and services.
N-SIDE VPs Amaury Jeandrain and Charlotte Tannier discuss their organisation’s partnership with Sanofi and look ahead to a brighter future.
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Transparency. Good partnerships need it to survive.
For N-SIDE and Sanofi, it has been a key ingredient to what has made the partnership successful for the past eight years.
Since late 2015, N-SIDE has established and built on a strategic partnership with France-based pharmaceutical company Sanofi, aimed at optimising the firm’s clinical trial supply chain. The partnership helped digitalise Sanofi’s clinical supply chain while driving greater performance and waste reduction.
Harnessing efficiency
N-SIDE is a global leader in increasing the efficiency of life sciences and energy industries by providing software and services that optimise the use of natural resources, facilitating the transition to a more sustainable world. Founded in 2000, N-SIDE has built deep industry knowledge and technical expertise to help global pharmaceutical and energy companies anticipate, adapt, and optimise their decisions. In the life sciences industry, N-SIDE reduces waste in clinical trials, leading to more efficient, faster, and more sustainable clinical trials.
Amaury Jeandrain, Vice President Strategy of Life Sciences at N-SIDE, has witnessed first-hand the development of the partnership since he joined the company in January 2016. “Very quickly, the value of risk management and waste reduction was perceived internally and this partnership ended up growing to become one of our largest. Today, Sanofi is the company at the forefront of a lot of the innovation co-created with N-SIDE.”
Amaury Jeandrain, Vice President Strategy of Life Sciences at N-SIDE
Pharmaceutical companies of varying sizes use N-SIDE solutions to avoid supply chain bottlenecks in their clinical trials, decrease risks and waste, control costs, reduce time-to-market and speed up the launch of new trials. N-SIDE’s focus is on four key pillars to bring high levels of efficiency into Sanofi’s clinical supply chain: best-in-class supply chain, people, analytics and innovation.
Charlotte Tannier, Vice President of Life Sciences Services at N-SIDE, adds that the key differentiator is the transparency between her organisation and Sanofi. “We trust each other and know that we can be fully open with them,” she explains. “We like to build new things together and co-develop innovative solutions.”
Charlotte Tannier, Vice President of Life Sciences Services at N-SIDE
Teaming with Sanofi
Having defined a clear route to success through the Sanofi partnership, Amaury is keen to point out that the relationship has acted as something of a catalyst for future business collaborations with other companies. “There are a lot of good practices that were initiated with Sanofi that now became a standard in our industry,” he discusses.
Looking ahead, the future of the partnership looks bright and is showing no signs of slowing down. Charlotte explains that the next step is all about “integration.” “For the moment, we have multiple teams and departments that are using the N-SIDE solutions, and many other software are used as well within the organisation. The focus in the short term will be to enable a unified IT landscape and environment,” she reveals. “The objective will be to be fully integrated and to increase the impact of the data they own. Because we believe, with Sanofi, that the way forward is through data. We are also planning to help Sanofi leverage more of the data that we’re generating together to increase its impact.”
As technology continues to evolve and organisations become even more digitally mature, partnerships built on transparency and trust will be in demand. N-SIDE and Sanofi already have that head start.
In this innovative partnership, the whole is greater than the sum of its parts as the two companies focus on taming tail-spend with an on-demand platform with embedded change management.
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Businesses have been leaving money on the table for years. For most organisations, (indirect) tail spend flies under the radar because of the large number of lower-value transactions, a fragmented supply base, and a poor user experience. This results in process inefficiencies and lost savings opportunities that can be eight to 13 percent higher than with more competitive sourcing.
Simfoni and Kearney set out to solve this problem, joining forces on solutioning tail spend management. The partnership pairs Kearney’s rich heritage and expertise in procurement transformation and change management with Simfoni’s composable analytics and spend automation technology. The result is a comprehensive global delivery model that significantly improves tail spend management, which until now has been a major problem for large and smaller organisations alike.
“We started our journey over three years ago,” says Stefan Dent, co-founder of Simfoni. “It takes some time to form a bond. You get to know one another working together on client engagements and then you realise that the relationship is really working, so you double down on the commitment.”
Simfoni helps businesses “see spend differently” leveraging data analytics to gain a deep understanding of user needs across everyday ‘tail spend’. Founded in 2015, Simfoni is a leading provider of tail spend, spend analytics, and e-sourcing solutions for large and midsize businesses around the globe. Simfoni’s platform uses machine learning and AI to accelerate and automate tail spend management, saving time and money. Its solution quickly ingests and organises complex data to uncover opportunities to optimise tail to higher value spend. Simfoni emphasises rapid value delivery through on-demand spend automation solutions that are operational in weeks rather than months.
Remko de Bruijn, senior partner at Kearney
The Kearney–Simfoni partnership delivers a unique and powerful proposition, combining Simfoni’s digital tail spend solution with Kearney’s know-how and ability to launch a transformation and unlock the promised value, says Remko de Bruijn, a senior partner at Kearney. “There are many digital procurement solutions around, but frankly, many of them aren’t delivering the promised value, typically because of challenges with user adoption and change,” he says. “Kearney continuously assesses solutions in the market, with one of our other partners, ProcureTech, and together, we concluded that Simfoni is leading in tail spend. This is how we found each other.”
Kearney is a leading global strategy consulting firm founded in 1926, with more than 5,700 people working in more than 40 countries. The company works with more than three-quarters of the Fortune Global 500 as well as with the most influential governmental and nonprofit organisations. Kearney is a partner-owned firm with a distinctive, collegial culture that transcends organizational and geographic boundaries—and it shows. Regardless of location or rank, the firm’s consultants are down-to-earth and approachable, with a shared passion for doing innovative client work that realises tangible benefits for their clients, in both the short and long term.
“We see Simfoni as a powerful solution to realise savings in indirect tail spend. It’s about not only data and spend automation, but also the customer experience,” De Bruijn says. “This is crucial when dealing with everyday spend as most users are non-procurement professionals.”
Kearney aids businesses in implementing Simfoni’s solution quickly, mitigating risks associated with unmanaged spend and vendors. “The attractive thing about Simfoni is that the solution manages tail spend—optimising both spend and vendors—with the savings funding the digitisation. It’s a tail spend solution that delivers a comprehensive service,” De Bruijn says. “Simfoni will even pay the tail suppliers with Simfoni becoming the ‘One Vendor’ for the tail, which creates additional benefits in accounts payables and working capital.”
Simfoni and Kearney both operate globally, which is important since their customers often operate in multiple regions around the world. “It’s a very interesting and powerful proposition,” De Bruijn says.
Stefan Dent, co-founder of Simfoni
Simfoni designed its tail spend platform from the ground up. The company founders came from the procurement domain, having worked in a variety of procurement leadership roles and at other procurement technology providers. “Let’s face it, existing solutions never solved tail spend, which accounts for around 80 percent of your vendors and transactions and around 20 percent of spend value,” Dent says. “Until now, the only options were BPOs, where you effectively outsource your tail to be managed by humans in a lower-cost country, or you use self-service bidding platforms. These solutions deliver some value, but it’s like putting a plaster on a wound. You never properly cure the problem.”
Simfoni’s platform is unique in that it is first and foremost a software-as-a-service (SaaS) solution with integrated buying services and digital procurement content components that connect with a client’s existing systems, or Simfoni can operate autonomously. Dent says that’s not even the best part. “The user experience is the most important element because, as Remko pointed out, most tail spend users are not procurement professionals,” he says. “Our users are in R&D, IT, plant operations, or marketing. They want an intuitive, easy-to-use solution to source and buy goods and services to support the everyday needs of their business. This is where traditional eProcurement systems fail.”
Dent says Kearney is an ideal partner being a trusted advisor to many of the world’s largest organisations. Kearney’s expert knowledge of procurement and transformation are a vital part of the offering. “Kearney’s input and expertise is crucial as Kearney helps our clients scope their tail spend program and update their procurement operating model while Simfoni frees up resources, allowing the client to focus on higher-value activities,” he explains. “At the end of the day, technology alone doesn’t solve tail spend. It’s about change. Kearney helps our clients make that digital shift. That’s why our partnership is so powerful because together we provide a comprehensive change and a digital solution as a package. The opportunity for our clients to finally control and optimise tail-spend is huge.”
Our cover story this month focuses on the work of Arianne Gallagher-Welcher. As the Executive Director for the USDA Digital…
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Our cover story this month focuses on the work of Arianne Gallagher-Welcher. As the Executive Director for the USDA Digital Service, in the Office of the OCIO, her team’s mission is to drive a tech transformation at the USDA. The goal is to better serve the American people across all of its 50 states.
Welcome to the latest issue of Interface magazine!
Welcome to a new year of possibility where technology meets business at the interface of change…
“We knew that in order for us to deliver what we needed for our stakeholders, we needed to be flexible – and that has trickled down from our senior leaders.” Arianne Gallagher-Welcher, Executive Director for the USDA Digital Service reveals the strategic plan’s first goal. Above all, the aim is to deliver customer-centric IT so farmers, producers, and families can find dealing with USDA as easy as using an ATM.
BCX: Delivering insights & intelligence across the Data & AI value chain
We also sat down with Stefan Steffen,Executive Leader for Data Insights & Intelligence at BCX. He revealed how BCX is leveraging AI to strategically transform businesses and drive their growth. “Our commitment to leveraging data and AI to drive innovation harnesses the power of technology to unlock new opportunities, drive efficiency, and enhance competitiveness for our clients.”
Momentum Multiply: A culture-driven digital transformation for wellness
Multiply Inspire & Engage is a new offering from leading South African insurance provider Momentum Health Solutions. Furthermore, it is the first digital wellness rewards program in South Africa to balance mental health and physical health in pursuing holistic wellness. CIO, Ndibulele Mqoboli, discusses re-platforming, cloud migrations, and building a culture of ownership, responsibility, and continuous improvement.
Clark County: Creating collaboration for the benefit of residents
Navigating the world of local government can be a minefield of red tape, both for citizens and those working within it. Al Pitts, Deputy CIO of Clark County, talks to us about the organisation’s IT transformation. He explains why collaboration is key to support residents. “We have found our new Clark County – ‘Together for Better’ – is a great way to collaborate on new solutions.”
Also in this issue, we hear from Alibaba’s European GM Jijay Shen on why digitalisation can be a driving force for SMEs. We learn how businesses can get cybersecurity right with KnowBe4 and analyse the rise of ‘The Mobility Society’.
Could generative AI be the answer to procurement’s problems: fewer workers, more work, and a rising bar for digital literacy.
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It’s news to no one that the nature of the procurement industry has changed.
Spurred by the COVID-19 pandemic, an industry-wide surge in digital transformation, and the rising immediacy of the climate crisis, procurement has never been more important, or more complicated. However, as the industry’s demands grow and evolve, many procurement teams find themselves in need of skilled individuals that simply aren’t there.
A recent study conducted by Gartner found that just one in six procurement teams believe they have “adequate talent” to meet their future needs. That means just 15% of CPOs were confident in their future talent pools and ability to recruit skilled individuals, even if they believed their current staffing was sufficient to meet demand today.
Concerns over “having sufficient talent to meet transformative goals based around technology, as well as the ability to serve as a strategic advisor to the business,” were the primary cause of skill shortage stress, according to Fareen Mehrzai, a Senior Director Analyst in Gartner’s Supply Chain Practice. Essentially, the changing nature of procurement means not only that today’s procurement teams are unprepared for the discipline’s continued transformation from back office buyer to “orchestrators of value” in the executive team, but face an increasingly sparse hiring market as the requirements for a new procurement recruit become increasingly complex to satisfy.
Generative AI: Making digital accessible
Generative AI exploded into the public consciousness in 2023 with the launch of image generation tools like Midjourney and DALL.E, as well as chat-bots like Chat-GPT, powered by large language models. Investment has been immediate and almost unthinkably massive. In late 2023, it was estimated that generative AI startups were attracting 40% of all new investment in SIlicon Valley, and Bloomberg Intelligence estimates that the market for generative AI, valued at $40 billion in 2022, will be worth as much as $1.3 trillion in the next decade.
Now, whether or not generative AI has the society-spanning, epoch-disrupting economic and social impact people are predicting (personally, I remain unconvinced, and anyone who disagrees can either fight me in the metaverse or try to run me over with a self-driving car) actually manifests, there’s no denying generative AI’s potential as a useful tool if adopted correctly.
Especially in an underskilled, rapidly digitalising procurement sector.
How can generative AI help procurement?
While Generative AI will never write a (good) movie script or create a piece of art that anyone with any taste would find genuinely moving, there are some things it does very well. Namely, it is very good at not only taking in and processing huge (and I mean huuuuge) amounts of chaotic, poorly structured information and answering questions about it, but most importantly, it can understand prompts and give results in simple, conversational language. There are still limitations and kinks to work out, however.
Generative AI still deals with hallucinations. However, the ability to input huge amounts of data and analyse that data in a conversational format could alleviate a lot of the technological literacy related teething problems that appear to be at the heart of the procurement skills shortage.
An EY report notes that, in the Supply Chain and Procurement space, generative AI has massive potential to: “Classify and categorise information based on visual, numerical or textual data; quickly analyse and modify strategies, plans and resource allocations based on real-time data; automatically generate content in various forms that enables faster response times; summarise large volumes of data, extracting key insights and trends; and assist in retrieving relevant information quickly and providing instant responses by voice or text.”
The future of Gen AI
Generative AI can be a source of simplicity for procurement teams at a time when new technologies often add complexity and necessitate upskilling or new hires. EY notes that a biotech company using a generative AI’s chat function has had positive results when using it as a way to inform its demand forecasting. “For example, the company can run what-if scenarios on getting specific chemicals for its products and what might happen if certain global shocks occur that disrupt daily operations. Today’s GenAI tools can even suggest several courses of action if things go awry,” write authors Glenn Steinberg and Matthew Burton.
Adopted correctly, generative AI could not only empower procurement teams to handle the pain points of today, but also tackling the looming threat of the skills shortage in an industry facing a relentless demand for skills that may not be in adequate supply for years to come.
Luke Abbott, Co-Founder and CEO at Equipoise, discusses the art of accelerating sustainable procurement with artificial intelligence.
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In today’s rapidly evolving business landscape, sustainability is not just a buzzword; it’s a necessity. As organisations strive to reduce their environmental footprint and drive social improvements in their supply chains, sustainable procurement emerges as a pivotal strategy. With the advent of artificial intelligence (AI), the potential to revolutionise sustainable procurement practices has never been more promising.
Understanding sustainable procurement
Sustainable procurement is the integration of environmental, social, and economic considerations into procurement decisions, to reduce adverse impacts upon society, the economy, and the environment1. As businesses grapple with the repercussions of climate change, dwindling resources, and increasing stakeholder demands, sustainable procurement offers a pathway to not only mitigate risks but also seize new opportunities.
The AI advantage in sustainable procurement
AI, with its ability to process vast amounts of data, automate tasks, and identify intricate patterns, is poised to be a game-changer for sustainable procurement. By leveraging AI, organisations can:
Enhance sustainability data collection
Scope 3 is the hottest topic in sustainable procurement and many organisations are grappling with the question of how to measure the greenhouse gas emissions of their suppliers. Understanding this, especially beyond the first tier, requires extensive data collection. If you were to focus on your top 100 suppliers and ask your tier n-1 suppliers to do the same, when you get to tier 3 (which is probably nowhere near the end of the supply chain) you need to engage a staggering one million companies. At this point, manual data collection and analysis is out of the question for time-strapped organisations. AI tools, such as Avarni2, streamline this process, ensuring comprehensive and accurate data acquisition.
Predictive analytics for sustainability risk management
Managing sustainability risks in today’s intricate global supply chains presents challenges such as monitoring vast supplier networks, handling overwhelming sustainability data and rapidly adapting to sanctions, media reports and regulations, all while maintaining a pristine reputation. AI offers a solution by providing real-time monitoring of supply chains, predictive analysis of potential disruptions, seamless data integration for a comprehensive view, automated reporting for enhanced transparency, and scenario analysis for strategic planning. AI tools, like Versed AI3, continuously monitor vast amounts of supply chain data, ensuring real-time tracking of sustainability factors. This real-time monitoring allows companies to identify potential risks before they escalate, enabling procurement teams to proactively address disruptions and uphold sustainability standards.
Automation
According to Deloitte’s 2023 Global Chief Procurement Officer Survey4, over 70% of CPOs have seen an increase in procurement-related risks, and only a quarter feel equipped to predict supply disruptions timely. Furthermore, internal challenges like talent loss and organisational complexities add to the burden. By automating routine tasks, AI not only alleviates these pressures but also empowers procurement professionals to focus on high-value initiatives, such as supplier education on sustainability priorities. Generative AI tools like ChatGPT can expedite market research, strategy formulation, and contracting processes, allowing teams to be more agile and responsive in this volatile environment.
AI in action
Unilever’s Sustainable Living Plan5 has been at the forefront of leveraging AI to drive innovation in sustainable procurement. In 2023, Unilever highlighted how they have been using AI and digital technologies, from the launch of their first digital tool to the recent formulation of the world’s first green carbon detergent6.
“We’re using AI to help identify alternative ingredients that can strengthen the resilience of our supply chain, making our formulations more sustainable and cost-efficient, and simplifying them by reducing the number of ingredients without impacting a product’s quality or effectiveness.” – Alberto Prado, Unilever R&D’s Head of Digital & Partnerships.
Through a data-driven approach, Unilever has been making smarter, faster, and sharper decisions to optimise its portfolio of brands and products. Their commitment to sustainability is further emphasised by their ambitious goals, which include climate action to achieve net zero, reducing plastic usage, regenerating agriculture, and raising living standards within their value chain7.
Limitations and due diligence
While AI offers transformative potential, it’s crucial to recognise its limitations. The accuracy of AI predictions and recommendations hinges on the quality of data fed into the system. In the realm of sustainable procurement, this means ensuring that the data sources are reliable and comprehensive. Regular audits, cross-referencing with trusted databases, and continuous training of AI models are essential to maintain the integrity of AI-driven insights.
The 2023 Gartner Hype Cycle for artificial intelligence8 underscores the significance of addressing the limitations and risks of fallible AI systems. It emphasises the need for AI strategies to consider which innovations offer the most credible cases for investment, ensuring that AI’s transformative benefits are realised while mitigating potential pitfalls.
The future of AI in sustainable procurement
As we gaze into the future, the synergy between AI and sustainable procurement is poised to grow stronger. With advancements in machine learning algorithms, natural language processing, and predictive analytics, AI’s potential to drive sustainability will only amplify. The Gartner report highlights the rise of generative AI, which is reshaping business processes and redefining the value of human resources. Such innovations, including generative AI and decision intelligence, are expected to offer significant competitive advantages and address challenges associated with integrating AI models into business processes.
However, a conservative outlook suggests that while AI will be a significant enabler, the onus remains on organisations to embed sustainability into their ethos and operations.
In conclusion, as the business landscape becomes increasingly complex, the fusion of AI and sustainable procurement offers a beacon of hope. By harnessing the power of AI, organisations can not only navigate the challenges of today but also pave the way for a sustainable and prosperous future.
From cost-containment to carbon emissions, here are the 10 things that should be top of mind for every chief procurement officer in 2024.
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In the year to come, procurement will continue to transition from a back office function to a boardroom value-driver. Chief Procurement Officers and other leaders will need to increasingly reevaluate their relationships to the rest of the business as procurement not only becomes an increasingly vital source of business wins, but also a central piece of the puzzle when it comes to emissions reduction and resilience throughout the supply chain.
From generative AI to the skills shortage, there’s a lot that CPOs could be focusing on in the year ahead. We’re kicking off the new year with our list of the top ten things CPOs should be prioritising in 2024.
1. Drive significant value for the business
That’s why the first priority of all CPOs in 2024 is to apply technology, new operational organisation, hiring practices, sustainable strategy, cost containment, and every other trick and technique in order to create value for the business. Increasingly, CPOs are transitioning from logistical and cost-cutting functionaries to “orchestrators of value” and that will only become more apparent as the year (and decade) wears on.
2. Drive digital transformation
As mentioned before, procurement is a process that’s reinventing itself before our very eyes, embracing new digital technologies and ways of working that increase efficiency and drive value for the business. CPOs are increasingly important integrators of technology into the business, and should all be prioritising ways to implement technology over the coming year. However, it’s important to beware that technology for technology’s sake is even more dangerous than sticking it out with a legacy system…
3. Reduce environmental impact
Knowing may be half the battle, but once CPOs have an understanding of the environmental impact their S2P process has, they must prioritise finding ways to mitigate that impact. From a stricter regulatory landscape to a more perceptive and angry public, a meaningful environmental sustainability strategy is no longer “nice to have” or even necessary: it’s long overdue.
4. Understand your Scope 3 emissions
More than 60% of procurement leaders in the US, UK, and Europe surveyed in a recent report say that their Scope 3 emissions reporting process is more of a “take your best-guess” approach than a process of gathering concrete, reliable information.
The S2P process is one of, if not the, biggest source of greenhouse gas emissions for every company on earth, and understanding the consequences of working with one supplier or another (and then accurately reporting that information) is a huge part of the journey to net zero. CPOs who fail to prioritise transparency in their S2P process will find themselves actively hindering their organisations’ environmental ambitions at a time when procurement has the potential to be the biggest driver of positive environmental impact in many organisations.
5. Cultivate your supplier ecosystem
As much as technology is playing a bigger and bigger role in the procurement process, no CPO should discount the importance of building genuine, strategic relationships within their supplier ecosystem. Obviously, some industries are doing better than others, but in many areas (like the fashion industry, where “Those in charge of contracting suppliers for fashion brands say they are investing in longer-term strategic partnerships,” but their suppliers “tell a different story”) there’s still need for improvement.
6. Don’t buy into the hype (too soon)
In 2021, it was self-driving cars. In 2022 it was the metaverse. And last year saw the world get absolutely bent out of shape over the promise of generative artificial intelligence. However, much like NFTs and blockchain (another thing everyone was spending a lot of money trying to figure out how to make money from for a while), the promised trillions of dollars of economic impact from these technologies has yet to translate into meaningful business applications. Even the hyperloop was abandoned this year.
Procurement is an area with a huge amount of potential for digital transformation, and adopting the right technologies for the right reasons is what’s going to separate industry-defining success stories from all those dudes who went blind at the Bored Ape Yacht Club convention.
7. Mitigate risk to the supply chain
In the wake of the COVID-19 pandemic, the global source to pay (S2P) process has transitioned from a “just in time” approach to a “just in case” one. As climate change disrupts agriculture and manufacturing across the global south, and events like the Yemeni blockade of the Suez canal in order to hinder Israel’s occupation of Palestine hinder the movement of goods between regions, CPOs should prioritise diverse buying strategies that mitigate risk to their S2P processes.
8. Be a source of cost-containment
Inflation was a defining characteristic of the economy in 2023, as corporate price gouging (amid other factors) caused cost-of-living to spike. In a world of rising prices, and supply chain unpredictability, controlling costs will fall increasingly to CPOs in 2024. Cost reduction targets have been hit less consistently across the industry in the last few years, thanks largely to inflation and the pandemic’s disruption of global supply chains. Going into the year ahead, CPOs who can find a way to successfully meet their cost containment targets will find themselves with a serious leg up over their competition.
9. Don’t lose existing talent
The world is in the midst of a growing resurgence in the power of labour, as class consciousness and anti-capitalist sentiment rise. The old propaganda about loyalty to companies that would replace that employee in a heartbeat doesn’t work anymore, and workers are increasingly understanding (and demanding) their true worth, and it sent shockwaves through the service, autoworker, and entertainment industries in the US last year alone.
With the tech sector still leading the world in brutal mass Q4 firing and rehiring strategies, and labour movements within massive logistics firms like Amazon growing stronger by the day, 2024 promises to be defined by more strikes and other examples of direct action, not less. CPOs in the middle of a talent shortage should prioritise giving their employees reasons to stay beyond gym memberships and company pizza parties.
10. Hire top talent
The nature of procurement is changing. As the discipline becomes increasingly digitalised, not to mention plays a more strategic role within the modern enterprise as a whole, the skills that make for a good procurement professional aren’t the same skills that were on job listings ten, or even five, years ago.
In 2024, CPOs should constantly reevaluate the skills necessary not only to do the job now, but to tackle the procurement challenges of the next few years when hiring.
Data is the key to unlocking new opportunities and managing risk, but capitalising on the opportunities of data in procurement is not without challenges.
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Over the past few years, the procurement sector has been thrust into the limelight, as CPOs are increasingly being identified as drivers of value creation, cost containment, and risk management.
In addition to business and process innovations, a lot of the changes in the role of procurement are due to a wave of digital transformation sweeping the industry. If digital transformation is the engine driving this elevation of the procurement function, then data is the fuel powering it.
Effectively capturing, organising, and utilising data to generate meaningful insights can produce significant benefits for the procurement process. However, costly investment into data analytics, flawed adoption strategies, and oceans of bad data can turn all the potential for wins into a whole new source of risk for the business. This week, we’ve gathered our top 3 challenges CPOs face when incorporating big data into their operations.
1. Bad data
No, I don’t mean Lore from Star Trek: TNG. Bad Data is a fundamental and pervasive risk to procurement professionals looking to empower their analytics. It’s also a far more widespread problem than many executives would like to believe. Last year, a report by SpendHQ found that 75% of procurement professionals doubted the accuracy of their procurement data, leading to almost 80% of executives outside the procurement function lacking confidence when it comes to making decisions based on that data.
In order for it to make any meaningful contribution to reducing costs, mitigating risk, promoting sustainability and driving meaningful change within the business as a whole, the data used by procurement has to be accurate. Pierre Laprée, chief product officer of SpendHQ, noted in the report that “procurement teams must do more to build and maintain influence within their organisations, including removing the dependency on spreadsheets to become more efficient.”
2. Choosing the right technology
Collecting, managing, and drawing insights from your procurement data is a matter of using the right digital tools. However, choosing the right digital tools—especially with CPOs often facing pressure from stakeholders to transform their operations digitally—can be a complicated prospect with potentially severe negative consequences ranging from sub-par outcomes and wasted budgets to catastrophic data breaches.
A report by Productiv found recently that, while “procurement and IT are being inundated with software access, vendor intake and renewal requests,” the number of applications and subscription services being managed by the average business has risen by more than 30% in the past two years. Combined with growing workloads, skill shortages, and an unclear vision for handling these growing technology stacks, Productiv’s report notes that “this patchwork of tools across various steps of the vendor management lifecycle has created technology, team and data silos. Instead of increasing efficiency, these tech stacks start adding up to a lot of manual work to bring everything together.”
3. Creating spend data visibility
Dark purchasing refers to the phenomenon of procurement expenses incurred outside a business’ defined procurement process. It’s uncontrolled spending that procurement can’t see, but that still gets added to their numbers at the end of the quarter.
Big data and procurement is often thought of in terms of its ability to help understand the world outside the business’ walls—logistics, pricing, supplier behaviour throughout the market in response to market changes—but effectively deploying data analytics to understand why dark purchasing is happening, when, and by whom is a vital step in figuring out how to reduce its impact on the company.
Unfortunately, this presents a serious challenge, as many procurement departments lack a cohesive data organisational strategy; data is often scattered throughout multiple silos in the organisation, hidden from procurement in much the same way that unapproved purchasing hides until quarterly expense reports. Overcoming this challenge and creating a holistic, accurate view of company spend—both within the procurement function and outside it—is one of the greatest opportunities and challenges presented by the infusion of big data into procurement.
The assistant will use natural language processes and AI to perform “thousands of procurement tasks”.
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The latest in a small flurry of generative AI-powered virtual procurement assistants is hitting the market. Earlier this month, Relish, a B2B app developer based in Ohio, announced the release of its new procurement assistant—a virtual assistant product powered by generative artificial intelligence and designed to intuitively interact with users while performing “thousands of procurement tasks”.
“What we’re offering is a solution that truly frees users from the menial to engage in the meaningful,” said Ryan Walicki, Relish CEO, in a statement to the press. He added that the Relish Procurement Assistant would revolutionise the way businesses handle their procurement systems and processes, claiming: “By leveraging large language models, this single interface spans all procurement systems and platforms and can be custom fit to any enterprise solution ensuring workflows are never interrupted.”
The rise of generative AI
Relish isn’t the first company to utilise a combination of generative AI and large language models, like ChatGPT, to create a more naturalistic interface between users and complex systems for managing data. In November, Californian tech firm Ivalua released an Intelligent Virtual Assistant powered by generative AI as part of its platform, making similar claims that the technology would eliminate busy work, freeing up employees for more strategic activities.
Relish works in a similar way, plugging into an existing procurement management platform, and using artificial intelligence and natural language processing to “intuitively interact” with users in a conversational way, giving them detailed insight into their workflows.
According to Relish, the technology can perform numerous tasks, including supplier management, sourcing, contract management, supply chain, and purchasing.
Where Relish differs from other offerings on the market is in its alleged ability to “[adapt] to any platform and workflow preference.”
According to Jeremy Reeves, Relish Senior Vice President of Product: “The adaptability helps users get the most out of their procurement enterprise software, maximising their return on the investment… It brings a new dimension to how users will go from being taskmasters to being conductors of their enterprise systems.”
AI and Machine Learning-powered analytics could help security teams flag and prevent fraud in their procurement functions.
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Procurement fraud is costly and hard to prevent, but with the right tools, organisations could see red flags earlier and respond in time rather than too late.
According to the Association of Certified Fraud Examiners (CFE), organisations lose 5% of their annual revenue to fraud, with the median loss per case totalling $117,000, and the average being $1.7 million.
Supply chains and procurement functions are especially vulnerable to fraud—often comprising long and winding networks, intricate webs of relationships, vast inventory assets, and multiple transactions along the S2P journey. The procurement and supply chain functions of retailers and manufacturers are especially vulnerable.
Frequently, procurement fraud is the result of a malicious individual within the organisation, although vendors and partners can also be responsible. Bid rigging, intellectual property infringement, inventory theft, and product counterfeiting are all examples of occupational fraud within the procurement process.
To address these challenges, companies must implement proactive measures. The CFE report noted that nearly half of fraud cases occurred due to a lack of internal controls, or an overriding of insufficient existing controls. It also found that anti-fraud controls were effective, resulting in lower losses and quicker fraud detection.
Fraud is prone to thrive in the procurement process, and can have devastating consequences, but the fight against the threat isn’t hopeless, and new technologies are proving especially effective in stamping out the issue.
In addition to traditional anti-fraud measures like strengthening internal controls, performing due diligence, and conducting regular quality checks, organisations can fight fraud in their procurement and supply chain functions by harnessing the power of AI and Big Data.
Fighting fraud with Big Data
AI analytics of Big Data sets can do more than improve efficiencies and predict trends in the movements of goods; these types of analytics excel at pattern recognition and, once correctly trained, can identify subtle changes in activity within the procurement function and supply chain that could point to fraud.
According to Isabelle Adam, an analyst at the Government Transparency Institute in Budapest, and Mihály Fazekas, founder of the Institute and assistant professor in the School of Public Policy at Central European University, “With the increasing use of electronic and online administrative tools — such as e-procurement platforms — making administrative records readily and extensively available in structured databases, public procurement has become a data-rich area.”
This wealth of data, if improperly handled, can become a place for fraud to hide, but if big data analytics are applied, they argue, it “can serve as a tool for auditors to identify and prevent fraud and corruption.”
Blockchain promises added transparency and security for the procurement process, but are the benefits worth the price of admission?
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Blockchain—the decentralised ledger technology that powers cryptocurrencies and NFTs—could be an immensely disruptive force in the procurement and supply chain management sectors. We’re going to take a look at how blockchain might impact procurement, and whether it represents a meaningful innovation or if the costs outweigh the benefits.
Blockchain: the hype
Using a combination of different technologies, including distributed digital ledgers, encryption, asset tokenization, and immutable record management, blockchain creates an unbroken and tamper-proof (in theory) chain of information.
For example, storing the entire service history of a vehicle, the transaction history of a house, or the provenance of a piece of art on a blockchain theoretically renders it trustworthy and incorruptible. A potential buyer could review the timestamped information included on the blockchain and be confident in its accuracy. In principle, blockchain could reduce or remove the need for intermediaries in highly regulated and complex transactions—like real estate, for example.
“Have you bought a house lately? Imagine if you could have transacted with the seller directly, even though you had never met, confident that the deal would be recorded in a way that neither of you could change or rescind later,” write Gartner analysts David Furlonger and Christophe Uzureau, suggesting that “You wouldn’t have to reconcile rafts of personal information with a real-estate agent, mortgage broker, insurance agent, property inspector and title company” if you were making a transaction using the blockchain.
Furlonger and Uzureau suggest that record keeping and verification is just the beginning and, once developed and combined with other technologies (characterised by lots of hyper and limited real world applications) like artificial intelligence (AI), the Internet of Things, and the Metaverse, the real potential of the technology will be unleashed, creating “whole new social and economic constructs in the peer-to-peer age of Web3.”
Blockchain: the reality
In actuality, Blockchain outside of applications for cryptocurrency isn’t actually… very interesting? It’s certainly not new. Blockchain technology not used to underpin a cryptocurrency is just a distributed append-only data structure. Often there are some users that are allowed to make additions to the structure. In the real estate example used Furlonger and Uzureau, that might include the homeowner, a surveyor conducting an appraisal of the property, the utility company providing electricity and water to the house, and professionals hired to perform maintenance on the property. A private blockchain could collect and verify the history of a property like rings on a tree, and provide an authoritative account that is, in theory, free from tampering. The thing is, that sort of verification is called a consensus protocol, and they’ve been around since before the 1960s—as have append-only data structures.
The reality is that the new, shiny applications for blockchain aren’t actually very useful. Supposedly, Blockchain technology offers up a way to verify information (or conduct a transaction) without relying on an intermediary, or blindly trusting a third party. “Trust-less” is the phrase that gets thrown around a lot. However, the result is often that you’re just trusting the technology underpinning the blockchain over a human or a public institution.
Building trust
As Bruce Schneier pointed out in an article for WIRED, “When that trust turns out to be misplaced, there is no recourse. If your bitcoin exchange gets hacked, you lose all of your money. Your bitcoin wallet gets hacked, you lose all of your money. If you forget your login credentials, you lose all of your money. If there’s a bug in the code of your smart contract, you lose all of your money. And if someone successfully hacks the blockchain security, you lose all of your money.”
One glaring example was the 2019 case of cryptocurrency exchange CEO Gerald Cotten, who died while being the only person with the password necessary to access US$145 million worth of other people’s Bitcoin. Far from being trustless, it would seem the people who lost access to their money were placing their trust in a single individual who died, leaving them no physical or legal recourse to get their money back.
There’s also the very valid criticism of blockchain-based technology that it’s an environmental disaster. NFTs caught most of the heat for this over the past few years, but all blockchain-based technology needs to be stored somewhere in a constantly active server. As noted by the NASDAQ in a report from earlier this year, “The energy consumption of blockchain technology results in significant greenhouse gas emissions, which contribute to climate change.”
So, blockchain is bad?
Not necessarily. I, personally, will stake what reputation I have on the fact NFTs and cryptocurrencies are misguided and valueless gimmicks at best and insidious, cynical techno-cults (that burn fossil fuels more enthusiastically than the UV lights at the Bored Ape convention burned out crypto bros’ retinas) at worst.
However, remember the boring version of blockchain technology? The append-only data sets we talked about before may not be new or especially sexy, but they’re an element of blockchain technology that could be incredibly useful for the procurement sector.
Blockchains in procurement
The procurement sector has traditionally struggled with opacity. Sourcing goods—especially from overseas markets—through networks of distributors and middlemen can muddy the waters and conceal vital steps in the source-to-pay process. The origin of goods, labour practices, contact with modern slavery or deforestation, can all be concealed in a murky supply chain.
Tracing the progress of an item from its raw materials through to a finished product is “often a challenge for today’s supply chains due to outdated paper processes and disjointed data systems that slow down communication. The lack of data compatibility exposes supply chains to problems like visibility gaps, inaccurate supply and demand predictions, manual errors, counterfeiting, and compliance violations,” notes an AWS report. However, with blockchain, procurement and supply chain management organisations can “document production updates to a single shared ledger, which provides complete data visibility and a single source of truth. Because transactions are always time-stamped and up to date, companies can query a product’s status and location at any point in time. This helps to combat issues like counterfeit goods, compliance violations, delays, and waste.”
Global network
If the documentation of, say, a shipment of EV batteries, can trace a direct line from a lithium mine in Australia to a factory in China through a global network of suppliers, all the way to their arrival at a factory in Ohio, the procurement department sourcing those batteries can scrutinise every piece of the value chain much more effectively for quality control, potential counterfeiting, and ESG compliance.
It’s not as flashy as Dogecoin, but it’s actually useful, especially as corporations make efforts to divest major polluters or other parties with poor ESG practices from their supply chains in an effort to reduce Scope 3 emissions and stop propping up reprehensible practices like modern slavery and deforestation.
Keith Hartley, CEO of LevaData, discusses why procurement’s golden age is now amid the rise of transformative tech solutions.
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“This is the golden age to be in procurement.”
Keith Hartley, CEO of LevaData, doesn’t hold back.
Similar to his passion for surfing, he is constantly on the lookout for the next challenge to tackle. The company he leads is an integrated, AI-powered supply management software platform that is transforming direct material sourcing by helping companies reduce costs, mitigate risk, and accelerate new product development.
Given the trajectory of the procurement function’s journey over the past 10 years, few could doubt the change the space has seen. Indeed, procurement was once a back-office function siloed out of sight, but today it stands front and centre in business operations as a key cog in the machine. Hartley recognises that while it is an exciting time, procurement is still a laggard and restrained. “I would say we’re woefully behind in procurement,” he admits.
“The function’s teams are typically not ones to raise their hand and demand the tools they need to do their job. If you’re a salesperson and you work in a Customer Relationship Management system, it’s a given you need a system to do your job, and if you’re in finance, it’s a given you need an ERP system. When you turn to procurement, there’s not widespread acknowledgement that you need a tool like LevaData to do your job.”
LevaData powers the smartest supply chains in the world by constantly analysing business objectives against real-time market activity and community intelligence. The company is trusted to deliver improved margins, control risks, generate new product velocity, and achieve multi-tier supplier engagement with purpose-built tools for quick collaboration and decisive actions. LevaData creates a competitive advantage with transformational and predictive insights. “What we are replacing are spreadsheets and emails, but some major companies are still 100% reliant on them,” discusses Hartley. “It’s an antiquated way of doing business. Macroeconomic shocks aren’t new, and obviously Covid was a significant one. With these shocks in the global supply chain, you must understand the impact on your specific business.”
Hartley speaks to how at the end of the day, companies still need to make a profit. “It’s about finding alternative sources of supply and buying the parts at the right price. These are challenges that don’t go away; in fact, they were heightened during Covid and have continued with ongoing geopolitical tensions. The reality is there are always macroeconomic shocks that cause supply to be constrained and prices and lead times to be variable. This has a direct impact on how organisations deliver results and drive revenue growth. Covid really heightened the need for companies to get this workflow in order, and that’s what LevaData has been addressing. The procurement people have been thrust into the light. If they don’t have the tools they need, then they’re stuck. The job is incredibly complex, and procurement needs all the help it can get in today’s world.”
The arrival of generative AI
As generative AI continues to emerge in conversations in procurement and beyond, its rise has caused much excitement within organisational structures. Indeed, OpenAI’s ChatGPT’s launch in late 2022 has only amplified this conversation, with many eager to harness the benefit of efficiency and cost savings as quickly as possible. But just because it’s new, does it make it right?
“It’s early days. It’s mostly hype so far in terms of how it’s being adopted and brought forward, but I’ve never seen a faster accelerated hype cycle than gen AI [has] right now,” explains Hartley. “LevaData is a leader in AI and is using it in two areas of our product. We’re still in the early infancy of AI and what it can do. We use AI to help us contextualise all the different data sources. We take over 154 data sources and blend them. This is data that doesn’t make sense together. Most data-heavy people tap out at about 12 or 14 data sources because the mathematics gets so complex. The complexity has kept the indirect procurement providers away from this space.
“The second part where we use AI is where we identify parts based on savings potential. There’s a lot of potential for the generative piece incorporating an even larger number of data sources. This is huge. AI is going to change a lot and will take some time, but I’ve never seen such a rapid hype around AI before.”
Procurement’s golden age
Looking ahead, Hartley is full of optimism and enthusiasm for procurement’s future and believes we are entering the “golden age.” “The best part is that we’re just at the very start,” he explains. “If you’ve been in indirect procurement for the past 50 years, you’ve been wowed by Coupa, JAGGAER and Ariba, as they have sold the world on the benefits of source-to-contract and procure-to-pay workflows. That works well for indirect procurement, when you are buying pencils, chairs and laptops in volume. But the more complex workflow of sourcing direct materials, the very materials that you turn into products to sell in the market, has largely gone unnoticed. Fortunately, companies have realised the direct sourcing opportunity, and started investing in AI-powered tools like LevaData.
“Legacy spreadsheets and email should no longer be the de facto standard for direct material sourcing. With the convergence of AI, big data, and analytics platforms, procurement professionals can be the heroes they and their company deserve. The next decade is going to be a wild ride in procurement.”
At DPW Amsterdam 2023, we chat with procurement leaders to find out why the conference is regarded as one of the most influential tech events in procurement today…
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Koray Köse, Chief Industry Officer, Everstream Analytics
“When you go to events that are this disruptive that are actually giving you an environment like a concert where people have a very positive vibe, that’s when the best experiences are shared and people open up. If you listen, you now understand what the real challenges are. If you’re at a conference that is very formal, then you get a very different feeling. It is the casualness of DPW that helps the authenticity of every company and its challenges.
“It’s a unique environment where you get very authentic, bold, blunt, but truthful statements of perception of actuals, desires, future vision, and also conversations about how can we as a community do things differently? How can we as potential future partners do things differently? And how can tech concatenate value and how can we actually now do that in a partnership with companies that we don’t even consider clients at this point? They’re not clients, but they share exactly what they want and those are benefits.
“I think it’s almost like an incubator environment because a lot of ideas are formed here. Lots of connections are made and a lot of deals for vendors are done too. You look at the floor and there are about 120 vendors all here for the same reason, it’s amazing. To get that concentrated over 48 hours, a lot of people will walk away and need to process what happened and the conversations they had. Then we look forward to next year.”
Koray Köse, Chief Industry Officer, Everstream Analytics
Ashwin Kumar, Vice President, GEP
“DPW has given me some insight into what kind of options there are. Sometimes I go through the booths and I see two solutions and question how they’re different. At first, I think they’re doing the same thing. And then once they start explaining, you find out the nuance. Now I understand this may not be applicable for this client of mine that I’m working with maybe this is for a company that’s growing at 30%, not for someone who is already there and growing at 2% or 3%.
“I think that way DPW has helped me understand how do you stitch different things together and then take it to a client and say, ‘this is the ecosystem you need at this point in time. It could change in six months, or three months, we don’t know. Go with it for now and you don’t have to worry about being married to that solution for too long.’”
Ashwin Kumar, Vice President, GEP
Kathryn Thompson, Partner, Deloitte
“I think DPW shows us the art of the possible in digital procurement. It shows us if you were unconstrained and you could do anything, what would you choose and build? You don’t have that in some of the other tech conferences that are a bit tied into an infrastructure they need to build. I love this what if idea we have here. I think it’s fabulous we have this confluence of organisations that need these tools, all the different startups and solutions to bounce ideas off and work out the future. DPW has real energy and passion like no other. You must get your message across in three minutes or it’s gone, that passion is brilliant because there’s nothing similar.”
Kathryn Thompson, Partner, Deloitte
Scott Mars, Global Vice President of Sales, Pactum
“This to me, especially for Europe, is the premier procurement technology event. All the main vendors, our competition as well as our peers are here. There’s many CPOs in attendance alongside procurement and digital transformation leaders so for us as a vendor, it really is a great audience. We love having the ability to network with our peers or other vendors, potential partners and these procurement leaders and visionaries so it’s definitely a great opportunity to do that. It is certainly one of the best procurement events I’ve ever been to. They do a great job here at DPW.”
Scott Mars, Global Vice President of Sales, Pactum
Karin Hagen-Gierer, Chief Procurement Officer, Scoutbee
“Whenever I go to conferences, I get to see the latest technology exhibited. I can have conversations with many people in a very short period of time. Number two, for me as a CPO, I come here as well to meet my peers and have good conversations. Amsterdam is always a good place to come and maybe combine business with pleasure.”
Karin Hagen-Gierer, Chief Procurement Officer, Scoutbee
Gregor Stühler, CEO, Scoutbee
“Procurement people are incredibly busy and getting a hold of them is quite difficult. Having them all in one spot is super helpful. One key challenge for procurement software providers is that the buying centre is not the same. If you sell sales software or whatsoever, it’s usually the same buying centre. You approach the Chief Revenue Officer or something like that. In procurement, it’s not always the CPO that decides on the tech. But DPW is filtering out and attracting the talent that is making those tech decisions and it’s extremely valuable for the startups and for the tech companies as well.”
Gregor Stühler, CEO, Scoutbee
Alan Holland, CEO, Keelvar
“This event has actually been a catalyst for some of the transformation we’re seeing in procurement. Matthias and his team have grown together best-of-breed vendors and they realised early on that change is afoot and legacy systems are going to become part of the history of the space. He embraced these vendors which are coming up with exciting new developments and provided us with a venue to put our best foot forward and present ourselves to other large enterprises with an appetite for understanding what innovation was required. We’re very grateful to Matthias, we’ve worked with him from day one and we think he’s done fantastic work here.”
Alan Holland, CEO, Keelvar
Prerna Dhawan, Digital Lead, Procurement, The Smart Cube
“I think DPW raises the profile of procurement. DPW has elevated the function because procurement is no longer seen as the industry that thinks of digital at the end. It’s not a laggard anymore. I attended the first DPW event pre-Covid and thought it was brilliant then but it’s got bigger and better since. We talk about this in procurement, you get innovation from your suppliers but if you think about innovation when it comes to technology you have to be open to talk to vendors and that doesn’t happen in other conferences the way it does here. I think DPW has created that platform for learning from each other to happen.”
Prerna Dhawan, Digital Lead, Procurement, The Smart Cube
CPOstrategy explores this issue’s big question and uncovers what the impact of gen AI is in procurement.
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The true possibilities of what can be achieved via AI is still being unearthed.
Indeed, the influence of new technology will only grow from here and new digital tools are being introduced all the time.
When it comes to generative AI, there is perhaps a misunderstanding that it is a new innovation. But the history of gen AI actually dates back to the 1960s. Among the first functioning examples was the ELIZA chatbot which was created in 1961 by British scientist Joseph Weizenbaum. It was the first talking computer program that could communicate with a human through natural language. It worked by recognising keywords in a user’s statement and then answering back through simple phrases or questions, in likeness to a conversation a human would have with a therapist. While ELIZA was seen as a parody and largely non-intelligent, its introduction has paved the way for later advancements in Natural Language Processing (NLP) and the future of generative AI.
Fast forward to today and the gen AI conversation and wider tech landscape looks very different. In late 2022, OpenAI launched ChatGPT – technology which has shaken the procurement function and beyond. ChatGPT interacts in a conversational way with its dialogue format making it possible for users to answer follow-up questions, admit mistakes, challenge incorrect answers and reject unsuitable requests. As such, the chatbot has created quite a buzz which has been felt across the globe.
Generative AI’s misconception
Speaking to us exclusively at DPW Amsterdam, Gregor Stühler, CEO at Scoutbee, believes there are some misconceptions around ChatGPT and the nature of how accurate the data it provides actually is. As is the case with any new technology, these things take time. “It’s always the same. It happened with electric cars, nobody thought that would solve the battery issue,” he discusses. “I think we are right at the peak of the hype cycle when it comes to those things and people have figured out what they can use it for. With wave one of gen AI, it is fine to have hallucinations of the model and if something is spat out that is not supported by the input.
Gregor Stühler, CEO at Scoutbee
“But by the second use case, hallucinations are not okay anymore because it’s working with accurate data and should not come up with some imaginary creative answers. It should be always supported by the data that is put in. This is very important that people understand that if you train the model and if you have the right setting, those hallucinations will go away and you can actually have a setting where the output of the model is 100% accurate.”
Data security
Michael van Keulen, Chief Procurement Officer at Coupa, agrees with Stühler and despite obvious benefits such as time and cost, he stresses caution should be used particularly when it comes to valuable tasks. “If you look at ChatGPT, it’s fine if you’re looking for recommendations for something low-risk. I need something for my wife’s birthday next week, you input three things that she loves and ask it to help. It’s great,” he tells us. “But it comes from data sources on the web that aren’t always governed, controlled or trustworthy. It’s whatever is out there. What about the algorithms that come with ChatGPT? I don’t know what’s influencing the search criteria. On Google, if you pay you are at the top of the search bar. But I don’t know what ChatGPT is governed by.”
Michael van Keulen, Chief Procurement Officer at Coupa
Managing data leakage
Danny Thompson, Chief Product Officer at apexanalytix, explains that one of the biggest challenges with generative AI is being aware of a leakage of sensitive information combined with a contamination of important data. “We have a database of golden records for 90 million suppliers who are doing business with Fortune 500 companies and that is the best information we’ve been able to accumulate about the suppliers and their relationships as a supplier to large companies,” he tells us.
Danny Thompson, Chief Product Officer at apexanalytix
“We want to make sure we’re not loading sensitive information into a generative AI function that might allow just random people to access that data. Ultimately the customers in the space that we’re operating in are serious companies moving around large amounts of money and facing real risks that they have to manage. It’s really important that the data that they have is either highly accurate or at least they understand the degree to which it’s accurate. This means if you’re using the solution that you don’t understand the level of trust you can have in it, then you shouldn’t be using it yet.”
Can generative AI bridge the talent shortage?
Amid talent shortages in procurement, there are some sections of the procurement space questioning to whether AI and machine learning can plug the gap and reduce the necessity of recruitment. Naturally, this raises the debate of whether robots will replace humans. Stefan Dent, Co-Founder and Chief Strategy Officer at Simfoni, adds that while AI and machines won’t replace humans, it will mean people will need to find new forms of work and take on higher-value roles.
Stefan Dent, Co-Founder and Chief Strategy Officer at Simfoni
“The shape and structure of the modern procurement function will change quite dramatically and people will need to upskill,” he discusses. “A lot of the work will be taken over by the machine eventually either 20%, 50%, and then a hundred percent. But the human needs to have that in mind and then plan for that next three to five years. The procurement function of the future will be smaller, and they should purposely be doing that, to then look at solutions to find a way to enable it to happen naturally.
Future proof procurement
“For someone who’s joining procurement now, you’ve got this great opportunity to embrace digital. Young people can question ‘Well, why can’t it be done by a machine?’ They’re coming in with that mindset as opposed to fighting being replaced by a machine. I think for graduates coming into procurement, they’ve got the opportunity to play with digital and actually change the status quo.”
As we look to the future, gen AI and new forms of technology will continue to change the world and the way we work. In the short term, work is expected to continue to upgrade the user experience and workflows through gen AI in order to build greater trust for the end user. As transformation continues to happen, businesses and wider society must embrace new types of AI to thrive and stay ahead of the latest trends. The potential that gen AI tools possess is expected transform the workplace of tomorrow while delivering value-add such as time and cost savings on a day-to-day basis.
Given the speed of evolution and development, it is yet unimaginable exactly what form the digital landscape will take in years to come. However, that horizon brings with it fresh opportunity and excitement revolving around a whole new world of technology at our fingertips. The future is digital.
As AI continues to emerge in a big way, Vicky Kavan, Vin Kumar and Nicolas Walden explores what the AI opportunity is in procurement?
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Procurement is a hard function to impress. Other parts of the business can afford to get carried away now and then, but not procurement. Everything in procurement comes down to finding value and then making sure you don’t overpay for it.
Artificial intelligence (AI) might seem like just the kind of emerging new technology that procurement would shy away from. But, as many procurement leaders already understand, this would be a big mistake. In our work with the world’s largest companies, we see two kinds of major emerging AI opportunities you won’t want to miss. The first group – how we execute our procurement using, for example, new autonomous sourcing systems – can save millions today. While the second – the advent of AI-driven automation and enhancements across almost every industry and areas of spend – will help save you even more tomorrow.
Savings today
In terms of the impact of AI, procurement executives predict that supply market intelligence (50% of respondents), contract management (43%) and bid optimization (37%) will be some of the greatest opportunity areas for AI technology.
Despite this, and even as most AI and generative AI systems remain pilot projects, autonomous sourcing systems are already transforming how procurement functions operate at large multinationals. Many procurement executives have told us that they find these systems, which can automate execution in either tactical or strategic areas and provide enhanced decision support, extremely valuable:
Clients tell us these systems are helping them reduce cycle times dramatically – from months to weeks or weeks to days – and cut costs by 10% or more. Supplier discovery? Shorter. E-sourcing? Shorter. Contract development? Shorter. While it is in the early days, time savings of 30% or more can be possible.
When MTN Group, an African multinational telecommunications giant, installed its Procurement Cockpit platform, the system paid for itself in four weeks because the AI-enabled software quickly identified new opportunities, consolidated pricing insights from around the sprawling corporation and accelerated negotiation preparation.
These systems are now making themselves useful across a range of sectors. Procurement executives at a major U.S. retailer, major European telecom and major European energy company all told us that these systems have saved time and money. Use cases include replacing the need to write detailed requirements, sourcing questions and even contracts through the use of modified templates through to tactical price negotiations.
Strategic drive
From strategy to insights, sourcing and negotiating – to contract drafting and supply risk management – AI-enhanced systems will make procurement faster and simpler. Although feature sets and value propositions vary from vendor to vendor, promising autonomous sourcing systems fundamentally change how technology engages with stakeholders using chatbot-style interfaces to summarise requirements as an output of discussions; search and identify providers of products based on a variety of market, process and business considerations; prepare request for proposals and contracts; and maintain a higher degree of compliance with regulations. Some of these systems can even execute simple one-round negotiations. At the moment, Globality, Fairmarkit and Pactum (for negotiations) are three of the biggest names in this space.
Savings tomorrow
Eventually, we expect that AI-enhanced functionality is likely to yield major cost savings in almost every spend area, business function and industry sector.
Contact centres or marketing services, for example, could already send out automated posts and even voice responses that mimic the voice of your choice. A travel agency might be able to supplement human customer service with a robot concierge, making it possible to achieve a much greater level of service than ever before. Such changes won’t happen immediately – implementing them is not a quick win – but AI enhancements will be a huge source of value and service improvements down the line.
Category managers, be advised: the general consensus among purchasing executives we polled recently is that fleet, digital tech, advertising and general equipment are the categories that will benefit most from AI-enabled technology.
Of course, as with most powerful tools, AI-powered services also create new sets of potentially considerable risks. For example, you will need to make sure that your contracts are clear about what your vendor can do with your data – can it be aggregated in a large language training model? If that model leads the company to develop a more advanced service, do you want to be compensated for your contribution? Are you covered for potential liabilities if you transfer customer data to your AI vendor and your customer’s information is somehow revealed? If you work with an AI vendor and create intellectual property on its platform, who owns that new product? There are many new angles and issues that you will need to consider.
Looking ahead
Over the next five to 10 years, AI is likely to transform many aspects of business, including procurement. Based on The Hackett Group’s analysis of 44 Level 2 processes across the source-to-pay, end-to-end process – for a company performing at the median of our database – there is a potential to reduce staff by up to 46% over the next five to seven years.
Clients have told us they see digital technology (including AI) as the most transformative trend facing procurement in the next few years (71%) – more important than data (51%) or environmental, social and governance, and sustainability (47%). For procurement professionals, how the work is done and where they will find value are both likely to change dramatically. Given the speed with which we expect these opportunities and their attendant risks to develop, now is a good time to start thinking about the opportunities AI can create for your team.
Just how much of the procurement process can be automated, and who does it help?
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It’s hard to argue that 2023 will be remembered as the year that generative AI exploded into the public consciousness. Image and text generation in the form of ChatGPT and Midjourney ignited excitement, controversy, contempt, and a fervour to adopt in equal measure. The generative AI industry is predicted to be worth more than $660 billion per year by the end of the decade.
But while there’s no denying that generative AI will be a part of the economic landscape of 2024 and beyond, it’s not yet clear what that will look like. More importantly, it’s no guarantee that generative AI will, uh, generate any ways for the technology to make back the hundreds of billions already spent to develop it.
It wouldn’t be the first major trend to be backed to the hilt by big tech firms, only to dissolve into nothingness like that racoon who drops his cotton candy in a puddle. In stark contrast to 2022, this year’s tech roundups and trend predictions have put a conspicuous lack of emphasis on the metaverse. Now, to be clear, the fact that Yahoo Finance calculated that “Mark Zuckerberg’s $46.5 billion loss on the metaverse is so huge it would be a Fortune 100 company” is great news for those of us who didn’t want to spend our thirties attending meetings in a glowing virtual mallscape surrounded by cutesy, animated versions of our bosses and coworkers. Huge relief. It’s also quite funny. More relevantly to the topic of generative AI is the cautionary tale that, unless big, expensive technological developments can be monetised, they will disappear.
So, how do we monetise generative AI?
How to make generative AI useful
Technology is most valuable when it solves problems, and saves time and money, or at least improves people’s quality of life—when there’s a measurable benefit of some kind, sometimes to humanity, and usually to shareholders. That’s the stuff that sticks around.
While its applications and capabilities—especially when it comes to creative tasks or just the ability to make something actually original—are limited, generative AI may actually be a good fit for the procurement sector, potentially solving a major issue the industry is currently experiencing.
Generative AI and the Procurement Skill Shortage
The procurement sector is short on talent—with five out of six procurement leaders claiming they will lack skills, staff, and other vital human resources in the near future. This is the case for several reasons, but primarily: an ageing workforce is starting to retire faster than new hires can skill up; also, the requirements of the job are becoming more technology centric as procurement digitally transforms, leaving departments underskilled even if they’re no understaffed; and lastly, the amount of work for procurement functions is increasing overall, as it becomes more of a driver of business efficiency and innovation.
If generative AI could be used to reduce procurement teams’ workload by automating certain aspects of the job, it could be a key piece of the puzzle when it comes to solving the skill shortage.
Retail giant Walmart has been successfully running pilot projects using its AI-powered Pactum solution to automate supplier negotiations. According to Deloitte, not only did Walmart find it “helpful for landing a good bargain, three out of four suppliers prefer negotiating with AI over a human. This strongly indicates that the ecosystem is ready to embrace this disruption.” While I’m not sure if this example is an endorsement of AI or an indictment of Walmart’s procurement team, the ability for generative AI to take over routine communication, negotiation, and other interactions in the source-to-pay process could free up huge amounts of time to focus on more strategic activities.
Gen AI’s future
It’s not hard to imagine that both buyers and suppliers could input their desired results and parameters into a generative AI negotiator and outsource the relationship management entirely. Out of curiosity, this morning I set up ChatGPT in two windows and had it conduct an RFP, tender negotiation, and sale agreement for the sale of an order of self-sealing stem bolts between O’Brien Enterprises and Quarks. It was a very civil, if slightly roundabout affair, and everyone seemed to come away happy—hacky business journalists especially.
Goofy demonstrations aside, there’s real potential for significant elements of routine communication and relationship management in the procurement process to be automated, or at least assisted by generative AI. If correctly combined with data analytics on contextual information ranging from weather patterns, commodities pricing, and supplier behavioural history, a generative AI could offer useful insights to procurement professionals while its generally low threshold for usability allows less tech-savvy procurement professionals to harness more powerful digital tools.
How Big Data can increase resilience, mitigate disruption, and help procurement teams spot danger before it’s too late.
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In the procurement sector, successfully managing risks while achieving your other strategic objectives is what sets a successful procurement function apart from those that can expect to experience disruption. Today, however, procurement teams face greater risk than ever before as supply chains become more complex, ESG goals become more ambitious, and the parameters for compliance get narrower.
Technology—powered by artificial intelligence and big data analytics—is radically digitalising the procurement process. While this has the potential to increase efficiency, revenue, and accelerate the procure-to-pay process, it has also driven complexity. Luckily, digital transformation also holds the key to managing this complexity. Digital tools, powered by artificial intelligence and machine learning, can tackle larger and more complex amounts of information than ever before. These analytical tools and their more powerful capabilities in turn have seen viable data sets balloon to include vast quantities of structured and unstructured data from throughout the supply chain, gathered together under the umbrella of Big Data.
Data source
Big Data, in gathering together vast amounts of information about every aspect of the source-to-pay process, in addition to broader contextual information ranging from economic instability to weather patterns, can help procurement professionals build up a more comprehensive, nuanced understanding of their procurement process than ever before. The level of visibility is unprecedented, even in a sector where supply chains are more complex than they’ve ever been.
Complex supply chains are more prone to disruption. More moving parts and longer distances to travel mean higher likelihoods of things going wrong. Michael Higgins, founder and CEO of Clutch, wrote recently that “risk is inherent at every step of the supply chain, from moving raw materials to manufacturers and between manufacturers and the distributor,” adding that “The added value of big data analytics is predicting potential disruptions, giving procurement managers time to make intelligent decisions.”
Procurement transformation
Advanced analytical tools can be used to track the weather, potential disruptions to agricultural or construction operations, political unrest like demonstrations or riots, and changing legislature that may affect everything from compliance to price. Because Big Data analytics are increasingly capable of collecting and analysing all of these factors and more, procurement professionals have the capacity to counteract sources of risk that traditionally would have seemed as inevitable as an act of divine wrath.
The risks to a supply chain are really representative of risks to your suppliers and their networks. Big Data analytics is also granting insight into the workings of—allowing a huge number of variables tied to each supplier to be tracked and used to make decisions. The result is a more agile and reactive procurement process that can analyse and respond to data analytics in real time, as opposed to trying to make best guesses based on past results and limited human judgement.
Procurement is truly transforming from the back office to the boardroom—becoming more strategic, digitally empowered, and complex than ever before—and Big Data analytics are increasingly a vital part of the function within the modern source-to-pay process.
Jamie Ganderton, Vice President at Proxima, examines the future of sustainable procurement going into 2024.
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As we step into a new year and inch closer to the global sustainability targets set for 2030, the spotlight on sustainable procurement will only continue to intensify. The aftermath of COP28 has placed an even greater emphasis on the role that large corporations play in global decarbonisation. This, coupled with incoming rules and legislation across Europe and the United Kingdom, such as the European Corporate Sustainability Reporting Directive (CSRD), has underscored the critical need for agile and proactive approaches to corporate sustainability action.
The Scope 3 Benchmark, a tool developed to enable organisations to collaborate to advance progress on sustainability targets, has shown that Scope 3 still remains a challenge and 2024 will be a pivotal year in addressing some of the fundamentals as we move within just two short contract cycles away from 2030. Looking ahead, the focus will sharpen on bridging the gap between sustainability objectives and procurement strategies, with an emphasis remaining on translating lofty sustainability goals into actionable procurement strategies. As we navigate 2024, collaborative advancements, data-driven insights, and the proactive evolution of procurement practices will be critical drivers, propelling sustainable procurement into a new role of implementing purposeful action.
Embedding sustainability targets into procurement strategies
Whilst it seems like an obvious starting point, many procurement teams have not yet fully embraced the need to translate sustainability requirements into procurement strategy. Even for those that have, challenges remain to translate sustainability language into effective procurement strategy. There is a tendency for organisations to panic and jump straight into supplier engagement, without first planning who they are going to engage and what are they going to need from them.
The goal for many in 2024 should be to plan out how the next six years are going to look and begin progress as soon as possible, because we know that change takes time and never happens as quickly as we intend. Sustainable procurement transformation is going to require focus and investment to get right. The core focus areas should be measuring emissions to drive action, developing the functional enablers to support the change, and developing the strategic levers for decarbonisation.
Leveraging emissions measurement to drive action
The primary starting point is to understand your emissions, in detail. Embrace carbon emissions measurement and start reporting them, ideally across all categories of Scope 3, but at least the core supplier-related areas. Following the GHG Protocol’s spend-based methodology is an adequate starting point, provided the outputs you develop allow you to drive insights into your emissions “hotspots” and start evolving greater accuracy as data quality and supplier maturity improves. Procurement teams can then begin to develop the strategic decarbonisation levers they will need for their categories.
Making procurement functional enablers
Building a sustainable procurement function requires the right support pillars, but evidence coming from the Scope 3 Benchmark suggests that some key foundations are missing. Firstly, there is a lack of directly invested resources, and there are also limited numbers of support team members. The volume of interaction with suppliers on Scope 3 is high, therefore you need someone to set the strategy and have an effective team to enact it. Even medium-sized businesses will have a reasonable number of material emitting suppliers who need engagement and management, which creates an increased workload for supplier management teams.
Additionally, many organisations have limited Scope 3 learning and development capability plans to support team members in developing their carbon literacy and bridging the skills gap.
At some point procurement needs to be bold and make carbon a key consideration throughout decision making, from up-front category planning, through to RFx and sourcing processes, negotiations and contracting, and post-contact supplier management. If there is no consideration given to carbon with equality to the classic cost, quality and service evaluation, then we will never make different decisions. There will never be a commercial incentive to suppliers to support decarbonisation efforts and we will inevitably fail. In 2024, we will begin to see more forward-looking CPOs begin to build carbon pricing into their decision-making, paving the way for processes to change.
Developing policy to help suppliers face reality
Traditional procurement policies are usually written once and then set in stone without the need to revisit them any time soon. Over the coming years, the old Procurement Policy is a tool that has the power to make a huge impact and one that needs its own evolution. This policy development will enable a blanket application of sustainability to be adopted without procurement intervention in every sourcing decision. Between now and 2030, we need to strengthen the requirements annually to allow suppliers to gradually get used to the changes and ratchet up the pressure over time. At some point in the future, there will be a decision not to trade with some companies if they have not met minimum standards. This tough line should motivate those to change or risk losing business.
Once procurement teams get to grips with what is driving carbon emissions in the supply chain, they then needs to develop the right approaches to motivate, encourage, and sometimes force suppliers to act. Some suppliers will be on board with the need to decarbonise and happily support the process, whereas others will need significant levels of ‘encouragement’. Some categories will be relatively straightforward to plot a pathway to decarbonisation, whereas others have more complex challenges and require more strategic levers. Category teams will need to build a comprehensive picture of their suppliers and in many cases begin the co-development of solutions to tomorrow’s problems. Research and innovation, product reengineering, and demand management can all play a significant role in reducing emissions, but release of value may be some time in the future, which places a greater emphasis on 2024 being the year to truly put weight behind the efforts.
A green future
As we look to 2024, a lot needs to change if we are going to meet the looming global sustainability targets. Many procurement teams are still grappling with integrating sustainability into their strategies. The next few years mark a critical juncture and demand meticulous planning and swift action. Transforming procurement practices to align with sustainability goals requires measured steps, starting emissions measurement and building a strategic decarbonisation plan from there. Whilst there is a lot to be done, with the right strategies in place, procurement teams are poised to play a pivotal role in accelerating organisations’ progress towards net-zero.
A consortium of volunteers from California have slowly restructured their state schools’ digital procurement process. Next year, it plans to go national.
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Procuring digital goods and services for public schools in the US has reportedly been a fraught process for decades. A fractured landscape between underfunded public institutions and a private tech sector has struggled to even accurately assess students and regulators’ needs, let alone finding the right edtech (education technology) to meet those needs.
This is all made harder by an increase in the amount of technology being integrated into schools—whether that’s good, bad, or maybe both, it’s undeniably expensive. The global education technology market was valued at $123.40 billion in 2022 by Grand View Research. It’s expected to expand at a rate of 13.6% between now and the end of the decade.
The power of education for procurement
Edtech is also a wide umbrella, with examples ranging from apps, overhead projectors, and chromebooks for students to thousands of screens, digital signage, and “content management platforms” like those found in Christopher Columbus High, an all-boys prep in Miami which the South Korean tech giant Samsung has transformed into a “connected campus”. In the US, procurement functions working for individual school districts are often forced to work with smaller budgets, fractured regulatory landscapes, and to compete with private schools with larger budgets that drive overall prices in the sector up.
The Education Technology Joint Powers Authority (Ed Tech JPA) was formed “out of frustration” with the existing system, or lack thereof, in 2019. The volunteer group, made up of procurement specialists and school purchasing professionals, has spent the past four years streamlining procurement for digital products and services, leveraging the buying power of multiple schools to negotiate prices, buy in bulk and save money.
From a grouping of school districts located in Irvine, San Juan, San Ramon Valley, Fullerton, Clovis, El Dorado County and Capistrano Unified districts, the consortium has grown to include 163 member districts that educate around 2.3 million students. The organisation has been awarded 23 procurement contracts to date, and is growing rapidly in education.
At the California IT in Education (CITE) conference, held in Sacramento during November, JPA President Brianne Ford, predicted that next year would see the program expand beyond California and make group bargaining procurement for edtech a national feature of the US school system.
Ask Procurement—a generative AI procurement solution—is being developed for the market by IBM using Dun & Bradstreet’s “huge data cloud”.
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In order to develop more effective and market ready digital solutions for supply chain and procurement professionals, IBM is partnering with Dun & Bradstreet, a data-dealer with access to vast quantities of raw information gathered from a wide variety of sources, as well as cutting edge analytical tools. Together, the companies will work on expanding the capabilities of IBM’s watsonx to expand their use of generative artificial intelligence (AI).
Through the collaboration IBM and Dun & Bradstreet intend to develop multiple offerings for clients to incorporate into their AI workflows, leveraging IBM’s AI and data platform, and fueled by Dun & Bradstreets’.
Ask Procurement
The leading solution in development, according to an IBM press release, is Ask Procurement, a generative AI-powered procurement solution that will “help empower procurement professionals to unlock new data and insights with a 360-degree view into all aspects of a company’s business relationships to help increase savings, reduce time, and mitigate the potential for risk.”
Ask Procurement is expected to use Dun & Bradstreet’s platform, but feature watsonx supported models and other generative AI capabilities “fueled by Dun & Bradstreet’s vast Data Cloud.” The solution is expected to be available to procurement teams in the second half of 2024, integrated with Dun & Bradstreet solutions or an enterprises’ existing ERP or procurement solution.
“At Dun & Bradstreet, being a trusted data partner and a responsible AI partner to organisations are synonymous,” said Ginny Gomez, President, North America, Dun & Bradstreet. “As two trusted brands that bring nearly 300 years of combined experience to the businesses we serve, Dun & Bradstreet and IBM are ideally suited to help companies responsibly navigate the rapidly evolving generative AI space because we know their business environments and processes well. And with hundreds of thousands of organisations globally relying on us every day, we believe there is no better company than Dun & Bradstreet to lead the industry and our clients into the future.”
A closer look at some of the best tools to help your procurement function capture the potential benefits of a world powered by big data.
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Procurement is becoming an increasingly data-driven field. Correctly gathered, organised, and analysed, Big Data sets can help a procurement department do everything from increase efficiency and reduce costs, to make more ESG-conscious decisions or shore up their supply chain against unexpected disruption. However, managing huge amounts of structured, unstructured, internal, and external data can present a significant challenge for procurement staff. This is especially true when procurement professionals haven’t needed to also be data analysts until recently. This means there might be understandable skill gaps in your team.
Luckily, there exists a wealth of digital tools designed to capture, analyse, and generate insights from massive amounts of data. This is all specifically catered towards enhancing and elevating your procurement function. Here’s a closer look at five digital tools to help maximise the potential of Big Data in your procurement function.
1. GEP Smart
With AI-powered spend analysis, as well as strategic sourcing, purchase order processing, and invoice management, GEP Smart is one of the more broadly capable and robust procurement tools on the market. The platform is capable of absorbing, collating, and converting large data sets into everything from compliance procedures to supplier management strategies.
2. Kissflow
For smaller organisations still in the process of growing their procurement teams, Kissflow can help bridge the gap between a legacy or underdeveloped procurement function and where it needs to be with less emphasis on learning complex new digital tools. Kissflow is all about being simple, accessible, and customisable. The platform handles basic procurement functions natively, but integrates with a huge variety of other tools and programs.
3. Coupa
Focused largely on spend management, Coupa unified, streamlines, and empowers the source-to-pay process. The firm uses Big Data analytics to manage working capital and forecast budgets, giving procurement professionals more visibility over finances.
4. Tamr Procurement Analytics
Tamr Procurement Analytics specifically targets the problem of siloed data within the supply chain, helping procurement professionals quickly unify their data sets and start using artificial intelligence to generate insights at speed. The AI and machine learning decision engine underpinning Tamr’s platform enriches user data while also curating it against a rigorous set of standards to ensure quality.
5. TARGIT Decision Suite
TARGIT is a business intelligence and analytics tool that can gather observations from throughout the supply chain. This allows them to be more easily converted into actionable insights. The platform embeds directly into internal and external-facing portals, allowing a procurement team to share dashboards with the entire supply chain network. By creating a holistic impression of the entire supply chain, TARGIT improves the results of its predictive analytics, increasing efficiency and resilience.
At DPW Amsterdam, Kathryn Thompson and Fraser Woodhouse, Partner and Director at Deloitte, discuss the rise of generative AI and the impact on procurement.
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Procurement is changing.
That’s something that isn’t lost on Kathryn Thompson, a Partner at Deloitte.
As part of her role, she leads the Sourcing and Procurement Market Offering within Deloitte’s Consulting division in Europe, Middle East and Africa. Originally from Australia, Thompson has worked in procurement since 1996 and has observed quite the evolution over the past two and a half decades.
Procurement’s transition
Over the years, procurement has shifted from a traditional back-office function to an entity operating at the fore of a company’s strategy. Having been involved in the industry for more than 25 years, Thompson has had a front-row seat to procurement’s digital transformation. While she affirms that AI has changed procurement, she isn’t convinced that generative AI is changing the space – yet.
Kathryn Thompson speaking at DPW Amsterdam 2023
“We see lots of AI tools pulling from different data sources to apply intelligence to different decisions,” she explains. “But the generative part, beyond contract summaries or pulling together draft RFPs, remains to be seen at scale. One of my more sophisticated clients has run 300+ Proof of Concepts in AI across their business, including and beyond procurement, and admits they are yet to scale or drive meaningful ROI from any POC. At the moment, the generative AI side for us, isn’t getting past proof of concept or the pilot stage yet.”
Fraser Woodhouse is a Director at Deloitte and has been with the firm since February 2019. He believes that procurement and sales teams will use gen AI for RFPs over the next six months. “I think they’ll do it without telling anyone,” he explains. “It will eventually get to a point where I think that sort of crutch will become a necessity. When it’s built into the enterprise platforms, people will forget how to write contracts because the AI does it automatically. People will even use it to write their emails.”
The AI dilemma
AI on its own is pointless – it simply doesn’t operate the way you need it to. That’s why the importance of making tech work in a way that creates efficiency has never been more important. For Woodhouse, he insists it’s about putting a human at the right place in the process. “One of the solutions I saw was a gen AI assistant helping write an RFP built in, but then the supplier has a gen AI assistant helping do the response to the RFP as well,” he tells us. “Very quickly you’ve got two AIs negotiating with each other, and that doesn’t work unless a human is curating stuff at that point in the middle.”
Given the ease of AI usage, there is a discussion as to whether tech implementation could go too far the other way. Could humans lose the ability to perform simple tasks they previously wouldn’t have thought twice about? But Woodhouse is quick to dispel that myth and believes that despite the growing reliance on technology, people won’t be rendered useless. “People didn’t forget how to communicate when spellcheck came around, they could communicate better,” he explains. “If you are a supplier and are responding to an RFP and you’re pressing their generative AI button to build the response and five of the other suppliers are doing the same thing, who’s going to stand out? The ones who wrote it themselves or at least edited it and had meaningful input.”
“You can use AI for the transactional, easy stuff but there must be a value underpinning it,” adds Thompson. “The winners are going to be the ones that are human about things.”
Fraser Woodhouse and Kathryn Thompson speaking to CPOstrategy at DPW Amsterdam 2023
Procurement’s place
With such significant innovation happening, it is seen as a transformative time to be in procurement. As automation speeds up, the necessity to upskill new graduates coming into the workforce and encourage them to learn higher-value work earlier in their career journeys is becoming increasingly important.
“Covid and the following work from home attitude has a lot to answer for,” explains Thompson. “Pre-Covid, you would rarely work from home. Consultants, suppliers, delivery partners always went to the client’s site. That’s where innovation, creativity, results that are more than the sum of their parts happen. That’s not replicable by generative AI. We need to get everyone back out there and doing things. Rather than replacing jobs, we’re replacing tasks. The tasks that we’re replacing are the likes of data analysis, synthesising, and summarising. Hopefully, it means we’re doing real-life negotiations, brainstorming and innovation instead which are the things that people love to do. Fingers crossed, it just means the bar goes up.”
Automotive supplier Continental has chosen to work with JAGGAER to implement its global purchasing strategy while driving digitalisation.
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Spend management firm JAGGAER has announced it is working with automotive supplier Continental to push its digitalisation agenda.
In a press release published on Monday (December 11), it was revealed the manufacturer will use JAGGAER’s spend management tools to implement its global purchasing strategy. The JAGGAER ONE suite will counteract previously isolated solutions and harmonise the areas of purchase-to-pay, source-to-contract and business partner management.
A multi-stage rollout is set for launch, beginning in Germany and the United States before being slowly expanded globally.
The release detailed that one of the most important factors for Continental choosing JAGGAER was due to the extensive and highly standardised range of functions of JAGGAER ONE, which already covers many existing requirements. In addition, this not only ensures a quick time-to-value, but also ensures a low implementation risk. Continental confirmed it found JAGGAER’s multi-ERP capability “particularly impressive”, with a total of 30 ERP systems needing to be connected.
Following the project’s launch earlier this year, the implementation of JAGGAER solutions within Continental will take place in several stages. Initially, the company will focus on the procurement of non-production materials and raw materials. It will start with the optimisation of the source-to-source contract process. In the next project phase, Continental will focus on the procure-to-pay process to ensure security of supply for employees globally. This is done via predefined catalogues and to optimise follow-up processes.
As well as the global rollout and digitalisation, there are also plans to expand the use of software to direct purchasing.
Only one in six procurement teams have “adequate talent” to meet their future needs, as industry demands grow and evolve.
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Fewer than a fifth of procurement directors and executives believe that their teams contain “adequate talent” to meet the future needs of their organisations’ procurement functions.
In a recent survey of 111 procurement leaders, analyst firm Gartner found that, while procurement leaders remained fairly confident in their current talent pools, when asked about their ability to meet future demand, confidence plummeted.
“Procurement leaders are generally confident in the current state of their talent and the ability to meet their near-term objectives,” commented Fareen Mehrzai, Senior Director Analyst in Gartner’s Supply Chain Practice. “However, our data shows that chief procurement officers (CPOs) are worried about the future and having sufficient talent to meet transformative goals based around technology, as well as the ability to serve as a strategic advisor to the business.”
The threat of an industry-wide talent shortage has been looming for several years, and isn’t constrained to the procurement and supply chain sectors.
In the UK, half of all employers expect to face talent and skills shortages when recruiting procurement and supply chain professionals—something 20% of firms believe will be exacerbated by Brexit. In Europe, firms say they already lack “highly qualified procurement personnel”, with 78% of procurement leaders surveyed as part of a recent Accenture report “increasingly confronted with skills shortages in their procurement departments.”
A Different Beast: Procurement Professionals’ Key Competencies “Shifting”
One of the key reasons that procurement leaders lack confidence in their industry’s talent pipeline to meet future demands is reportedly the shifting nature of the modern procurement function.
“Procurement leaders are aware that the competencies required to drive transformation are different from traditional procurement skills, and that there are significant gaps between their current and future needs for the most important competencies,” Mehrzai said. Only 4% of surveyed leaders said that no gap existed between their current capabilities and their need for technology and data skills, with 68% of leaders saying technology and data skills had become more important to the operation of their procurement function in the past year.
Increasingly, procurement is a data-driven, technology-focused sector, but it appears the development and recruitment of available talent lacks behind the sector’s need to not only drive transformation within the business but also serve as a strategic advisor to its key decision makers. As generative AI and data analytics are adopted in greater concentrations across the sector, the demand for professionals who are primarily equipped with technology and data-centric skillsets — at the potential expense of a traditional procurement background — will only increase.
At DPW Amsterdam 2023, Prerna Dhawan, Chief Solutions Officer at The Smart Cube (a WNS company), tells us about the importance of remaining focused on fixing the problem and not leveraging technology for technologies sake.
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“You don’t need AI or even gen AI for the sake of it.”
In today’s world, everyone is obsessed with what’s new and fresh. Like in most other functions, in procurement, the latest craze is generative AI, with ChatGPT being one prominent example. Despite new technology’s clear benefits, such as cost and time savings, it’s important to keep the problem you’re trying to solve and the business impact you’re looking to make front of mind.
Prerna Dhawan is the Chief Solutions Officer at The Smart Cube. Like many of her peers, Dhawan recognises the potential that new technology brings but also shares concerns. “Like everyone else, we’ve been on that bandwagon as well,” she tells us. “For us, there have been two key learning so far. We have already done one live deployment of gen AI. We went live with our gen AI model earlier this year, which enables users to skip the stage of manually searching for content on Amplifi PRO, our on-demand procurement intelligence platform. You just ask the question and our platform leverages a custom NLQ framework and gen AI to provide a natural language response. Using a combination of our own AI models and gen AI provides a more dependable, accurate response as pure Gen AI isn’t fully functional for all types of analysis and can’t be trusted completely.”
Navigating AI adoption
Indeed, there has been criticism from some sections about ChatGPT providing hallucinations and making key data up. For multi-million pound organisations responsible for high levels of spend, this isn’t good enough. A second learning Dhawan is keen to get across is that she believes that gen AI is being dominated by hype. She explains that with any “new shiny object”, it should be treated with caution.
“I’ve tried to explain this a little bit, but everyone is excited about new things. A recent example is another use case where we were experimenting with our digital assistant,” she explains. “There was a point where we used a 100% gen AI approach, and we were still getting issues and hallucinations where the queries weren’t being answered correctly. The team said we needed to make it work and I explained that, ultimately, a client needs to solve the problem, they’re less hung up on how this is done. Sometimes people get lost with the technology and the approach. You have to ask yourself, are you solving the problem? If the answer is to just input a human and you don’t need AI, then do that.”
Prerna Dhawan, Chief Solutions Officer at The Smart Cube, sits down with CPOstrategy at DPW Amsterdam 2023
The journey
Armed with more than 16 years of experience in developing client solutions, managing strategic relationships, defining product strategies and driving profitable growth, Dhawan has worked with procurement, supply chain and corporate strategy teams across many global 2000 companies. Throughout her career, she has helped them embed intelligence and analytics as enablers of competitive differentiation and business transformation, along with The Smart Cube’s co-founders Gautam Singh and Omer Abdullah.
The Smart Cube is a WNS company and is considered a trusted partner for high-performing intelligence that answers critical business questions. The Smart Cube works with clients to figure out how to implement answers faster through customer research, advanced analytics and best-of-breed technology. The firm transforms its data into insights – enabling smart decision-making to improve business performance at the top and bottom line. Together with WNS, expert resources are combined with leading digital technologies, merging human intelligence and AI with innovation.
Digitally-enabled future
While AI’s challenges should be acknowledged, Dhawan is in no uncertain terms about the importance of stepping out of comfort zones and meeting fear head-on. Change can be a divisive topic with human nature being to cling on to what’s familiar. However, this can result in becoming reactive and failing to keep up with competitors.
Prerna Dhawan, Chief Solutions Officer, The Smart Cube
“As leaders, if we want to change the game of procurement and redefine the value we create for a business, we have to be more open to embracing new things,” she explains. “If you learn what the capabilities of new technology are and where you can actually use it, everything has strengths and weaknesses. Ask yourself – do you want to be an early adopter or do you want to be a laggard in your industry? All of this has the potential to give you that competitive advantage. It’s about being open, experimenting at pace, but also not being blinded by the magic and assuming everything will just work. There will be changes needed to your processes and people’s mindsets.”
Procurement’s future
With the future of procurement set to continue to be digitally-enabled and full of innovation, Dhawan believes the function now has its seat at the table and is ready to thrive.
“If I look at my journey from when I started in procurement, clients were asking questions like ‘Who are the suppliers in the market? How do I get the best price?’ Procurement is now getting involved at the new product development stage and is even advising the business on what ingredients to use while taking a more total value approach,” she discusses. “When you’re thinking about the product, do you want to put in palm oil or sunflower oil based on sustainability considerations, and how can you justify additional costs of a sustainable supply chain? Procurement isn’t just supporting the bottom line but also influencing the broader business goals of sustainability, innovation and resilience. It’s a great time to be here.”
Conrad Smith, Founder and CEO at Graphite Systems, discusses the similarities between Formula One and procurement amid significant digital transformation.
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“Our business, like the F1 driver, knows to go fast.”
You’d be forgiven for thinking that procurement and Formula One are worlds apart at first glance. However, to Conrad Smith, Founder and CEO at Graphite Systems, they are actually a lot closer than initially meets the eye. A petrolhead by his own admission, Smith shared the stage with Haas Team Principal Guenther Steiner at DPW Amsterdam 2023. As a purchaser with almost 30 years of experience, Smith has overseen quite a transformation during his procurement career. He says that with everything going digital, you would assume that purchasing would accelerate. But it is, in fact, the opposite.
The pace of purchasing
“Over these 30 years, you would think purchasing would be getting faster,” he tells us. “Business is speeding up, but purchasing is slowing down – that’s stunning. When you think about it, where else in the world is slowing down when everything’s going faster and faster? Even though we’re investing in Coupa and Ariba and all of these expensive purchasing tools, it’s still slowing down. Our business stakeolders know business is speeding up, and so their tolerance is going away. In the nineties, when you onboarded a supplier, you just needed commercial data, name, address, tax, and banking.”
Conrad Smith (left) with DPW founders Matthias Gutzmann and Herman Knevel
Having been founded in February 2019, Graphite Systems is the premier supplier life cycle and risk management solution. The emergence of risk and due diligence has become a primary function within procurement. Vendor due diligence during the procurement process ensures users can identify and mitigate the risks present with a vendor they want to do business with during the contracting process. For Smith, he believes that this transformation has been 15 years in the making.
“I think that it was typical that a purchasing leader would point to other stakeholders and say it’s legal that’s holding this up, privacy or security. They’re the ones stopping the process from happening,” he explains. “And quite frankly, I’ll admit, those were my early thoughts. This is like a hot potato – I don’t want to be owning it. I look stupid because of the slowness I described. Think how stupid the business thinks we are when they come and say, I’m working on a project, I need this consultant here on Monday. And our best response is that it’ll take weeks or months to onboard the supplier”
“Weeks matter, and we need to go through all this risk and due diligence. It’s really important to do the risk and due diligence, but we can’t do that at the expense of the speed of business. While business is quicker, in every measure that you look at, purchasing is going slower. It’s dumb, and the business knows that, and it means we lose credibility. It needs to happen, but we need to be very intelligent about it and not just do things the same ways we’ve always done them.”
Conrad Smith with Haas Team Principal Guenther Steiner at DPW
Procurement’s changing
Smith explains that one of the reasons he can relate to the F1 analogy is that while cars are going faster than ever, the drivers are far safer today. “Every year, we see massive accidents take place,” he tells us. “I think last year, a car that was flipping head over heels tumbling and hit the fence before slamming into the ground but the driver was okay,” he explains. “There’s this principle that is very important in almost any situation where somebody says, you can have this or you can have that. It’s a false choice.
“You have to pick speed, or you have to pick safety. If you go in with a requirement that says it has to be fast and it has to be safe, that’s the F1 example. You have to go into purchasing and say it’s a non-negotiable. It has to be fast and safe. How can we rethink the design so it can go fast and be safe? That’s really my passion, and it’s possible. It doesn’t mean it’s easy, but it’s possible. Frankly, in the case of this purchasing problem, it’s way easier than it should be. But we’re still stuck on passing paper back and forth instead of just saying, there’s my profile. Everything you need is in my Graphite profile – just like everything you need to know about me [as a professional] is in my LinkedIn.”
The future of creation, management, and sharing of data and documents between buyers and suppliers absolutely needs to evolve from emails, spreadsheets, and PDFs into a modern social network architecture. This transformation of information sharing has already proved its speed and efficiency in most other aspects of our lives. It’s time to quit wasting time and money on supplier onboarding and embrace modern technology in this critical procurement process.
Anthony Payne, chief marketing officer of HICX, tells us why we won’t reach net zero unless we fix data collection.
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As we approach COP28, large manufacturing brands are in the net zero spotlight. It’s been a year since the UN Expert Group released Integrity Matters, a report clarifying the exact metrics brands must meet if they wish to claim net zero success. Those planning to do so, account for around half of the world’s largest listed companies, according to the latest Stocktake, a number which has doubled in the last two and a half years. Despite this momentum, however, brands are slow to implement.
Now, with the conference marking another year closer to the 2050 Paris Agreement and other deadlines, it’s time to step up delivery. What this means is that the strategies behind net zero pledges need a boost.
As a supplier experience evangelist and a marketer, I view this challenge through a different lens. The way in which we engage suppliers to get their data needs significant improvement. And the way forward is to market to suppliers.
A growing conundrum
Most of today’s major brands have expensive procurement technology with which to engage suppliers, technology that has often evolved to be complex, clunky, and hard to use. As a result, supplier adoption of these tools is low, and therefore supplier engagement in projects to cut carbon and provide quality information is low. Brands have the challenge therefore of getting suppliers to adopt their expensive tech and engage in net zero efforts.
Additionally, we’re seeing that what each party expects from the brand-supplier relationship, is misaligned.
Anthony Payne, chief marketing officer of HICX
Suppliers, at the start of the relationship, are highly incentivised to work with a brand and they want to get to three things: the first purchase order, delivery of that first service or product, and payment. From that point, they just want to continue transacting and renewing business. This is their “steady state.”
A brand’s steady state, on the other hand, is more complex. In addition to transactional work, brands need a continuous flow of information around compliance, quality, performance, tax, carbon footprint and an awful lot more. Nowadays, brands also want to be efficient and automated. This brings new technology, whether it’s extensions to established technology or new specialist tools. Of course, with new tools come new processes.
Suppliers, as we’ve discussed, primarily want to receive orders, deliver on them and be paid. But now, they are also expected to respond to requests for a whole set of information, on a continuous basis. They’re also facing a lot of change in the form of ever-complex technology landscapes and evolving processes – and this isn’t just for one brand, it’s for all their customers and it’s leading to suppliers suffering from what we sometimes call, ‘initiative fatigue.’
The need for brands to collect data is here to stay and it’s time to deal with the thorny issue of how we can get suppliers to adopt the necessary tools and engage in net zero requests.
We need suppliers
Further to this conundrum, brands face something of a basic and rather obvious truth; they need suppliers. For example, brands need suppliers to provide carbon information, ideally using the tech setups that already exist, and they need them to engage in this activity over and above “business as usual”.
Why then, don’t more brands make their suppliers’ lives easier? We’re missing a trick. Let’s flip the way in which we work with suppliers – rather than bombarding suppliers with information requests, let’s encourage them to do what we need.
We can learn from marketing
Let’s turn our attention to another department, one that has had to apply the principle of encouragement rather than force. Marketing cannot force potential customers to buy or adopt a product or service, instead, it engages customers, encouraging them to adopt or buy. This is usually by appealing to a need or emotion.
What’s obvious in the customer-facing world is customers have a choice. For example, as much as I would love to be able to require an audience to buy what we’re selling, to come to our events and read our content, I obviously can’t insist.
This is now, more than ever, the same with suppliers. Like potential customer, suppliers have a choice. The fact that brands need suppliers in order to collect net zero data, gives suppliers more agency. Suppliers now get to exercise choice through their behaviour, and it’s this choice that is absolutely central.
Now don’t get me wrong. It’s not that suppliers want to veto what brands need from them, it’s more that they’re facing too much noise in the form of new technology, information requests and the resulting processes. They’re overwhelmed.
If you want suppliers to engage in your net zero efforts, think differently. Simply piling on more pressure won’t get the best of them. Rather, let’s think more about persuasion and encouragement, and how to show them value. The marketing process involves engaging customers, building strong relationships with them and offering them value, with the purpose of capturing value in return. You’ll see three-quarters of this process is about how we appeal to customers, not the other way around.
If we apply this concept to suppliers, we get a useful way of thinking about the relationship. Why don’t we engage suppliers more, build stronger supplier relationships and create value for them? If marketing is anything to go by, the result will be that we capture value from suppliers – like getting them to complete compliance questionnaires, do forecasts, take part in quality programs and log into (and actually use) those expensive systems.
Rather than trying, in vain, to force suppliers to engage in net zero activity, let’s market to them.
Now, as net zero delivery dates creep closer, brands can empower themselves to step up by stepping into the shoes of suppliers and appealing to them. As we explore new ways of working with suppliers, who knows what solutions could be inspired?
By Anthony Payne, chief marketing officer of HICX, the supplier experience platform
Costas Xyloyiannis, CEO at HICX, discusses why the time is now for supplier experience in supply chain and procurement and its rise to the top of conversations in the space.
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“I feel like the focus is shifting.”
Gone are the days of supplier experience being hidden away in the background. Today, it sits as an increasingly important target area within the procurement and supply chain space. But it hasn’t always been this way.
For Costas Xyloyiannis, CEO at HICX, he is pleased to see supplier experience’s conversation grow. “I’ve been in this space for 23 years and even if we go back three or four years ago, no one was talking about it,” he tells us. “It’s great to see a movement beginning to happen.”
Speaking with CPOstrategy at HICX Supplier Experience Live in Amsterdam, a day before DPW Amsterdam kicked off, he revealed how satisfying it was to see its evolution take place. And clearly there’s a market for it. Scores of people filled the Tobacco Theatre in Amsterdam all eager to listen to the many discussions and speakers attending the half-day event. “It is very satisfying because you see people’s minds changing in the same way that it did for the customer and employee experience,” he explains. “What you have to think about is that almost every company is also a supplier so it’s in your interest to focus on the supplier experience side. In another context, you’re also a supplier and people should understand that we’re all in it together. If you don’t think about solving it, then you’re going to have that pain yourself.”
Driving Supplier Experience
Indeed, it’s an issue that needs solving. Xyloyiannis explains that not understanding the necessity of supplier experience is a common misconception because it affects everyone in different ways. “Sales and marketing are the ones likely to understand what it means to be a supplier but they’re detached from the problem,” he says. “They are probably going into a portal and filling things in many times, it’s just not procurement doing it so that’s why they can’t make the connection. What we all need to realise is that focusing on supplier experience is in all of our interest. Ultimately, you have to think it’s just the right way of solving a problem because I create efficiency for myself and I’m also a supplier.”
HICX Supplier Experience Live in Amsterdam in October 2023
Xyloyiannis goes on to explain that if the focus is on supplier experience, an opportunity has been presented to create net efficiency – which is a massive win for all. “This benefits everyone because it’s not a zero-sum game,” he says. “If you think about business cases of other solutions, it’s we’re going to fire people and cut headcount. If I take the US government example of 150 million a year to DNB, this would’ve been a saving they would make without impacting any other functions internally. No heads would have to be cut; nothing would have to be outsourced. In a way, it’s free money for everyone when you can create net efficiency.”
Moving forward
Today’s Chief Procurement Officer has a lot on their plate. Amid navigating continuous innovation and transformation, ESG’s ever-increasing influence and battling inflation concerns all on the back of an already disruptive few years, procurement finds itself at an interesting moment. But looking ahead to 2024, supplier experience has its seat at the table and will only become a hotter topic in the years to come, according to Xyloyiannis.
“A lot of leading companies are putting huge amounts of focus on it,” he tells us. “Henkel posted on LinkedIn last year that they were driving their whole strategy around supplier experience. Then you’ve got Heineken and Unilever who are getting more involved in the space too. I think it is very much at the forefront, particularly in companies which produce goods and services. Supply chain has become very global and there’s a benefit to outsourcing and all these things, but it does make it very fragile. That’s why now it’s become important to focus on supplier experience because we have such a high dependency on one another.”
In this article, Veridion’s CEO unveils the exciting world of AI in Supplier Discovery, shares the company’s journey into data enrichment, and concludes with some behind the scenes of how the company is enhancing its Search API with natural language capabilities, paving the way to data-driven future in procurement and beyond.
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In today’s world, global supply chains are facing persistent volatility and disruptions, leaving procurement companies extremely exposed to the fluctuations of markets and the associated risks from vendors. This unstable environment highlights the necessity of innovative approaches in procurement management, particularly the adoption of AI-powered intelligent data.
Deloitte’s 2023 Global Chief Procurement Survey reports that 89% of companies worldwide have been negatively impacted by inflation-related cost risks in the last year, with 79% also facing substantial supply shortages. These figures underscore the critical need for innovative strategies and technologies to address these challenges in procurement.
Embracing AI for supplier discovery: A game-changer in procurement
Perspectives from Veridion’s CEO, Florin Tufan
As procurement firms aims to master the complexities of the evolving supply chain landscape, artificial intelligence (AI) emerges as a transformative solution that promises significant benefits, especially in enhancing supplier discovery.
Veridion, a company at the forefront of data enrichment and innovation, is leveraging AI to streamline data-driven growth across many areas within industries. Florin Tufan, Veridion’s CEO, offers candid perspectives on the opportunities and challenges presented by AI in procurement, with a special focus on its capacity to refine the supplier discovery procedure.
Tufan talks about how leveraging AI for supplier discovery is transforming procurement from a process constrained by limited information and relationships to one that is dynamic, informed, and resilient. AI-enabled data allows companies to comprehensively understand the supplier landscape, enabling them to analyse and evaluate a vast array of suppliers quickly and efficiently.
“We come from a world where it wasn’t possible to learn everything about the entire universe. If you had three suppliers for one highly important thing, you’d much rather spend a lot of time strengthening that relationship and putting better protection in place. There was no easy way to ask about others and question whether you were working with the right ones while finding out if you had enough resiliency. No, you want to work with the best ones so that you’re covered and get on with the work no matter what.”
However, Tufan also highlighted that while AI has the potential to significantly cut down the time companies spend searching for new suppliers, it’s not a magic wand that instantly fixes all procurement issues. There are still things to be fixed in the supplier discovery process.
CPOstrategy speaking with Veridion CEO Florin Tufan at DPW Amsterdam
Veridion’s approach: Addressing the need for a more proactive and comprehensive approach in supplier risk management
Tufan’s insights suggest a pressing need for a more proactive and comprehensive approach in supplier risk management.
Tufan pointed out a critical shortfall in the procurement strategies of many large companies—they lacked sufficient redundancy in their supply chains. When the pandemic struck, these companies scrambled to identify and connect with the best possible suppliers in various regions. However, the process was fraught with inefficiencies. “The discovery phase alone took weeks, and that was before even determining if those suppliers were a suitable match. By the time companies could establish redundancy, it could be two years later, and that’s simply too late,” Tufan explained.
He observed that the focus in procurement has traditionally been on what is known about the top suppliers based on past interactions, often neglecting the broader, more holistic view of a supplier’s status and potential risks. “There are numerous instances where companies face downturns or disruptions due to economic or political factors, and their clients often find out too late,” Tufan noted.
Who is Veridion? The company’s journey to data enrichment in procurement
Veridion, a Romania-based company, operates in the segment of source-of-truth business data, providing comprehensive and up-to-date insights on private companies. The company’s solutions are addressing particularly procurement, insurance, and market intelligence data challenges and are powered by AI and machine learning capabilities. This technology enables Veridion to extract maximum value from data, enabling efficiency and innovation for their customers.
One of Veridion’s earliest projects in procurement, which significantly contributed to its exploration of data enrichment solutions, involved collaborating with semiconductor companies seeking to diversify from China and US manufacturers planning to onshore to South America. This experience gave CEO Florin Tufan and his team deep insights into the complex challenges of global supply chain relocation. Tufan described this journey as both humbling and enlightening, particularly in understanding the significant impact of supply chain shifts on everyday products.
The company’s approach to addressing these challenges has been methodical and innovative. By leveraging AI and machine learning, they have developed more efficient ways to harness data, enabling businesses to make informed decisions in rapidly changing environments. This approach is not just about providing data but enriching it to offer meaningful, actionable insights.
Veridion has become a key player in transforming how companies approach procurement and supply chain management. By focusing on data enrichment and leveraging advanced technologies, they have positioned themselves at the forefront of this critical industry, offering solutions that are as dynamic as the markets they serve.
This “incredible journey”, as described by Tufan, exceeds the goal of business expansion. It’s about comprehending and effectively responding to the complex challenging of global with real-time, accurate data.
Looking forward: Veridion’s CEO perspectives on latest technology innovations
“I’m 99% percent excited! At the core, we’re an AI company.”
Florin Tufan’s vision for the latest cutting-edge technologies and innovations such as generative AI is one of optimism and excitement. He sees it not just as a technological leap, but as a tool that will become integral to daily life and business operations, enhancing efficiency and connectivity across the globe.
When asked what big news is coming soon, Florin announced an upcoming enhancement to their Search API, set to launch this year. This significant update introduces semantic search capabilities, leveraging natural language processing to enable more intuitive, human-like search experiences. With this advancement, users will be able to conduct searches that closely align with their specific needs and queries.
Veridion’s Search API is modernising multiple procurement processes from supplier search to enrichment, setting a new standard of excellence with first-class vendor data. By incorporating advanced AI capabilities, this intelligent search engine has made significant strides in deduplication, cleansing, and enriching master data, addressing a critical challenge many companies face. Organisations often struggle to understand the full potential of their existing supplier networks for sourcing opportunities. Veridion’s data-centric approach ensures that companies can now leverage their current supplier base more effectively or find new ones, uncovering hidden opportunities and driving efficiency in procurement strategies.
It looks like Veridion is reshaping the procurement landscape, turning complexity into clarity and offering an unparalleled user experience. The company is marking a paradigm shift towards a more efficient, data-driven future in procurement and beyond.
At DPW Amsterdam 2023, Alan Holland, CEO of Keelvar, tells us about the acceleration of digital transformation in procurement and what it means for the next generation of the workforce.
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Keelvar’s mission is simple – to help procurement teams globally to scale sourcing excellence.
Keelvar is powered by unique artificial intelligence, designed by category experts, to deliver significant savings and operational improvements for global enterprises such as the likes of Siemens, Coca-Cola, Samsung, Novartis and more. The company was founded in Europe’s largest AI research lab by a team of computer scientists and engineers specialising in AI, optimisation and game theory applied to strategic sourcing. Keelvar has raised $42 million to date in funding to accelerate product development and global growth.
The company is led by Alan Holland who has served as CEO since the company’s foundation in September 2012. Indeed, in his first year, he led the organisation to win the Cork Company of the Year in the small company category, and the firm has more recently been awarded a Gartner Cool vendor.
Having previously served as a lecturer in artificial intelligence in University College Cork’s Computer Science Department, Holland specialised in Optimisation, Game Theory and Algorithmic Mechanism Design. Such experience has helped give Keelvar an edge in terms of innovating with offerings that exceed competitors’ technical capabilities. This enables Keelvar to define an entirely new category of the solution, putting Keelvar in an ideal position to lead this new category that Keelvar has called autonomous sourcing.
CPOstrategy sitting down with Alan Holland, CEO at Keelvar, at DPW Amsterdam 2023
Evolution at scale
Procurement is in a state of flux. The industry is experiencing unprecedented amounts of innovation and change in a way which has ripped up the playbook of what went before it. However, Holland believes it is only in the past half decade or so where transformation has really started to take place. “If we look at the last 10 years, the first five of those procurement was very slow to change,” he discusses. “What we saw were technology landscapes dominated by a small number of large suites vendors who had acquired many companies, but most enterprises were satisfied in buying all the modules they would need to run their procurement function from one vendor. Rarely was it the case that the various modules did what their customers needed. Some of them might have worked in some ways, but others just didn’t serve the need at all.
“In the second five years of our being, things started to change. We did start to notice an increasing acceptance that best-of-breed was the way forward and that enterprises needed to accept that if they were to get the buy-in from their stakeholders, then they needed to work with a combination of best-of-breed vendors and piece together their specific technologies landscape rather than just buying it in bulk from one. I would say it was gradual at first and then suddenly, but it’s only been suddenly in the last couple of years. The pandemic likely accelerated some of that change.”
Trust first
Holland explains that in recent years, large multinationals are placing an increasingly important level of trust in smaller, best-of-breed vendors such as Keelvar to allow them to run their sourcing events and meet niche demands. He believes that in the past it simply wouldn’t have happened and strives to prove that faith right. “I suppose that’s a process where enterprises are gradually increasing their trust in what are smaller vendors, but these smaller vendors are becoming bigger because we’re serving hundreds of large enterprises,” he explains. “We’re gaining in strength and momentum and the barriers to adopting best-of-breed at scale are lowering and the market willingness to jump those barriers is increasing. That momentum is just gathering more and more force.”
Alan Holland, CEO at Keelvar
Using tech as an enabler for talent
Procurement’s talent shortage and the ways to bridge has been a hot topic for years. Whoever you speak to within the industry, everyone will have a different viewpoint. Some say procurement needs a rebrand, others say it’s a lack of education while others think technology could hold the key. For Holland, he believes it’s about making tech work and freeing up people in procurement’s time to focus on more value-add work that will help solve strategic goals.
“What is attracting graduates to procurement now is working with intelligent systems that are powered by AI and that allow them to be strategic and not working on routine or tactical tasks because machines are taking over the data-intensive areas of processing these workflows,” he tells us. “Our second product, which we launched about three years ago is autonomous sourcing. These are sourcing bots that are intelligent software agents that you can now design, build, and operate your own sourcing bots. If you’re somebody who understands best practices in sourcing, you can now build automated workflows so that instead of having to run sourcing events one by one and get through 15 or 20 a year, now you could design bots that are running hundreds of these events per annum.”
Procurement’s bright future
While not only opening up people’s day, using technology as an enabler to make life easier also acts as a way of encouraging the next generation into the industry. “What you’re doing is freeing up many other people’s time to spend on relationship management or innovation discovery and talking to the market, finding out what new suppliers you should be dealing with, visiting suppliers to check things are in order,” he says. “And that is the type of work that people enjoy doing. Machines are taking more of the data-intensive work off their tables, and machines are not good at work related to establishing trust. Machines have no empathy, but people do. The soft skills in procurement are becoming ever more important because the machines are taking over the harder skills. That is leading to a transformation in the type of work that procurement is doing.
“It’s also leading to a transformation in the interest levels that graduates emerging from universities have for this sphere. When it used to be that they were first introduced to a legacy system and told that this is what they needed to use to do their job. Young workers are coming with higher expectations about software and rightfully so, and enterprises are reacting to the need to satisfy the technology requirements of younger recruits now, which is a very good thing. It’s accelerating that digital transformation that we are seeing.”
The next step
Looking ahead, Holland is full of positivity for the future and believes decision-making in procurement is easier than it’s ever been. He believes tomorrow is “very bright” as procurement enters an era with intelligent software agents which can automate workflows and make the human workday more efficient. “There’s a whole new range of possibilities where creative and thoughtful planning will provide a competitive advantage for organisations and procurement can be far more influential in how successful their companies can be. It’s a game-changer.”
At DPW Amsterdam 2023, Brandon Card, Co-Founder and CEO at Terzo, discusses the rise of his organisation amid the COVID-19 pandemic and how it used the disruption to its advantage.
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Terzo means third in Italian.
With the two founders having Italian heritage, they chose to describe what they set out to build – a platform that brings third parties together.
Terzo uses powerful AI technology to extract, analyse, and visualise its customer’s contract data. Terzo’s AI data extraction capabilities also reach beyond contracts and can solve an organisation’s document problems, from invoices to POs and more. Its platform was designed on the foundation of contract intelligence, providing business teams the necessary data to improve productivity, optimise spend, reduce costs, and manage risk and governance across their entire supplier ecosystem. Terzo is the first solution to provide critical data and terms to both legal and business teams to make decisions together.
Terzo’s journey
Brandon Card is the Co-Founder and CEO at Terzo. His company’s journey’s start was an interesting one, having been founded days before the onset of the COVID-19 pandemic and the lockdowns that then ensued. But, reflecting on the disruptive nature of the situation, Card believes it actually helped get Terzo up and running quicker. “It just accelerated our timeline because we wanted to build fast,” he reveals. “When we put the team together, we had this concept that we wanted to get the product out as fast as possible. We knew that with Covid happening there was going to be a huge shift in how people were working. People were going to need to buy new solutions faster and it’s going to be harder to control spending. We knew procurement was going to have a host of challenges across the supply chain with this interruption with Covid. Our team on the engineering side believed we need to build faster.”
This led to Terzo’s team on the engineering side of the house to work diligently throughout the rest of 2020 and into 2021 on building code and new releases with the vision of getting the Terzo product into the industry quicker. “We thought we might be able to help procurement given the challenges they have now with all of these new needs that the business is going to bring,” he says. “We probably built the product about 50% faster just because there were no distractions so there’s pros and cons when everything happens in life. Our team really worked well together and they buckled down and they took that time to focus on Terzo. It’s something I’m very proud of this team for doing that.”
Brandon Card speaks with CPOstrategy at DPW Amsterdam 2023
Developing relationships
A big part of what Terzo does revolves around strengthening relationships by uniting teams to unlock insights so organisations can make smarter decisions and maximise value from suppliers, customers and partners. Card believes this mantra holds the key to long-term success in procurement.
“It’s critical for us because when we think about whether we’re doing spend analytics or contract intelligence, it’s all about understanding the relationship with these different entities you’re working with,” discusses Card. “We’re not there yet but my big vision in the future is to build an enterprise relationship intelligence platform to understand every single business that you’re working with, whether it’s a customer, a supplier or a partner. The truth with these big organisations, a lot of their suppliers are also partners or customers. These relationships are very complex and they’re very critical to innovation.
“If you’re doing anything in the cloud right now, if you’re doing anything with AI or even autonomous driving, you need partners to get this done. You can’t build it in-house. And years ago, people would build in-house. When we were young growing up in the nineties, everyone had to build their own data centres and build their own software. We’re in a world now where you can go and turn things on online in a few minutes, and that’s where we want to be so you can push product out faster, competitive advantage, and I think these relationships are critical to procurement having a competitive advantage and driving value for the whole business.”
Procurement’s place
In today’s world, procurement is in the driving seat. The function isn’t siloed anymore, stuck in a back-office room and out of the way of everyone else. Despite such significant innovation, there is sometimes a perception that procurement is still boring. For Card, he believes one of procurement’s biggest challenges is changing that age-hold mentality of procurement within a c-suite.
“It’s about educating the CEO or the Chief Financial Officer (CFO) of large organisations just how critical procurement is. A lot of them just don’t understand,” he tells us. “That’s the challenge we have, and that’s something we want to change. In the future, the CFO is going to treat the head of sales the same they treat the CPO. Right now, the chief revenue officer gets special treatment in every organisation. If you run sales, you’re treated differently because you bring in revenue. If you’re procurement, you’re lucky if you’re at the table. But I do see that changing.”
While Card believes this shift is already beginning to happen with younger CFOs, change such as this doesn’t happen overnight. “By doing this, you’re going to have a really balanced organisation and reduce risk while optimising their costs,” he discusses. “Ultimately, they’re going to be more efficient, and the teams are going to be working a lot better together. There’s going to be a better culture when leadership buys in because then procurement feels valued. They work harder, and that vibe carries throughout the organisation. That’s something that we want to help push for procurement but we know it’s going to take time.”
At DPW Amsterdam 2023, Daniel Barnes, Community Manager at Gatekeeper, discusses the evolution of the procurement function and the influence tools such as generative AI are having in the space.
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“It might sound harsh, but people just won’t have a job if they don’t change.”
For Daniel Barnes, Community Manager at Gatekeeper, his thoughts are clear. Technology is here and it’ll only get more advanced.
Barnes has been the Community Manager at Gatekeeper since June 2022. The company he works for is a next-generation Vendor & Contract Lifecycle Management (VCLM) platform that was born in the cloud and works on any device. Gatekeeper has a strong focus on collaboration, clear actionable data, obligation and compliance tracking, email alerts and most of all ease of use. The firm has a ‘zero training’ mantra driving a fanatical focus on usability that results in an application internal stakeholders and suppliers can use effortlessly.
The Gatekeeper Platform provides a suite of vendor management, contract management, kanban workflow, collaboration and reporting features. Customers can extend the functionality of Gatekeeper with additional modules to meet their required use cases, as well as integrating with over 220 third-party solutions.
Technology potential
Since joining the company, a key consideration for both Barnes and Gatekeeper has been the influence of generative AI. However, Barnes explains that while the potential of the technology is exciting, they are being strategic about how to leverage AI.
“We’re probably taking it a little bit more of a slower approach,” he tells us. “We have a contract summary function at the moment which means for any contract we summarise it so that anyone in the business can get a really quick understanding of that contract. We’re also exploring whether we’re going to bring in a Gatekeeper bot that allows us to get insights analysis in a very conversational manner. One thing we really believe is that contract and vendors aren’t just for procurement or legal. Everyone in the business has to contribute to make these successful. A lot of the issues, data and information behind these are legally complex. Procurement language is difficult when you’re talking about RFPs or you’re talking about risk. Someone in the business doesn’t care about that, they just want to get whatever they have brought, they want the service, they want it performed, they want it on time and they want a good relationship. We’re trying to figure out how to use AI like that.”
CPOstrategy speaking with Daniel Barnes at DPW Amsterdam 2023
The rise of Gen AI
Generative AI isn’t exactly new. In fact, it actually dates back to the 1960s. Among the first functioning examples was the ELIZA chatbot which was created in 1961 by British scientist Joseph Weizenbaum. It was the first talking computer program that could communicate with a human through natural language. But, given the introduction of a far more advanced model – ChatGPT – gen AI is the name on not only procurement’s lips but the wider world too. Barnes questions what you need to make AI successful at implementation.
“You get data and most procurement and legal teams have an issue with data because they don’t have it in one place,” he explains. “We fundamentally believe in this three-pillar approach. It’s to restore visibility and to have all your vendors and their contracts in one place. From there, you take control of that by digitalising all of your processes. Once they’re digital, you can track and automate them from various data points that you have in your vendor and contract records. That allows you to safeguard compliance, whether that’s regulatory, legislative or by contractual obligations. They’re all different forms of compliance that you need to track. Most teams are really struggling just with those. When we talk about gen AI, the reality is most teams are still so far away from even being able to realise those benefits. Today, gen AI looks powerful once you have the pillars in place and I’m really excited about its future.”
Procurement’s evolution
Indeed, procurement stands at a unique moment. With some in the space used to operating a certain way through legacy systems and others embracing a digital transformation and the technological innovation that brings with it, Barnes recognises that people who are reluctant to change could be left behind. “I think there has to be a willingness to change,” he tells us. “I’ve been talking about change in procurement since 2019, and I would say 80% of people who are engaged are hesitant and don’t want to change. That’s a really big concern. But my biggest worry is they don’t want to know in the first place. One of my fears is you’ve got so many solutions that genuinely can eliminate work in procurement teams. I’m worried for those people who don’t want to change because what are they doing when their work’s automated?”
The future
Barnes, who also hosts the World of Procurement podcast and YouTube channel, believes there is a current cultural divide in procurement and the industry is at a make-or-break moment. He affirms procurement will go “one of two ways”.
“You’ve got people who are stuck in the past that are archaic with what they’re doing. Then there’s those who are really pushing the profession forward,” he explains. “I see it as a moment in time where procurement kind of goes one in two ways. It’s extinct in terms of how it used to be. There’s solutions that I’ve seen which have automated workflows and are doing the work that traditional procurement people used to do. We can pull people along, but there has to be an initial willingness to change too or it’s not going to happen. That’s why I think it’s great to see people that are showing that willingness. They may not have the answers, but they want to learn.”
Michael van Keulen, CPO at Coupa, discusses the emergence of gen AI and whether procurement is in a golden era amid technology transformation.
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Generative AI, or gen AI for short, is one of the hottest topics in procurement today.
Indeed, the introduction of ChatGPT has only accelerated its prominence into wider consumption. Gen AI allows its users to quickly generate new content based on inputs. These models could include text, images, sounds, animation, 3D models or other types of data. One of its biggest draws is the ability to understand different learning approaches and allows organisations to move quickly to leverage large quantities of data.
But despite obvious benefits such as time and cost, Michael van Keulen, Chief Procurement Officer at Coupa, stresses caution should be used particularly when it comes to valuable tasks. “If you look at ChatGPT, it’s fine if you’re looking for recommendations for something low-risk. I need something for my wife’s birthday next week, you input three things that she loves and ask it to help. It’s great,” he tells us. “But it comes from data sources on the web that aren’t always governed, controlled or trustworthy. It’s whatever is out there. What about the algorithms that come with ChatGPT? I don’t know what’s influencing the search criteria. On Google, if you pay you are at the top of the search bar. But I don’t know what ChatGPT is governed by.”
Van Keulen is a passionate and seasoned procurement evangelist with a comprehensive track record of driving value through business transformation at global companies. Since March 2020, van Keulen has been the Chief Procurement Officer at Coupa, a leader in cloud-based business spend management software, where he is responsible for driving best-in-class procurement practices across the company, supporting business development and being a source for peers looking to elevate and transform procurement. Van Keulen is especially passionate about building teams, driving value, organisational transformation, CSR, and diversity and inclusion.
CPOstrategy speaks with Michael van Keulen, CPO at Coupa, at DPW Amsterdam
The rise of AI
In the case of Coupa, the firm has been conducting its community.ai platform for the past decade which has been at the heart of the company’s strategy. Community.ai analyses real-time spend data, applies AI to compare company’s metrics against others and offers ways for organisations to be more efficient, profitable and sustainable. Van Keulen believes that the biggest difference between what Coupa offers and what gen AI provides is the trust factor.
“At Coupa, we measure information based on real spend, data and suppliers that are doing real business together – the internet isn’t doing that,” he discusses. “We’ve got nearly $5 trillion of spend under management from real transactions and real suppliers. That number continues to grow as customers and suppliers join the Coupa community. Pretty much all of our customers have trusted us with access to their sensitive data which we anonymize and then share back with the entire Community. As a member of the community I know I can trust it because it comes from a source that is reliable, sanitised, relevant and well-governed. As well, we have certain standards and algorithms that we built-in all based on outcomes that our customers are looking to receive.”
Van Keulen believes there is a misconception in procurement that ready-made data sets are out there that are capable of meeting customer requirements. “The truth is most tech companies out there today don’t have access to customer data because their customers won’t let that happen,” he explains. “But at Coupa, our customers have already given us access to their data. This means we now have a real, reliable, accessible, governed and structured data set that has been anonymized. When we then apply AI, you actually get prescriptions that are meaningful and relevant to procurement. I think the misconception is that this type of data set is easily found, but it’s not, we’ve been building this for over 10 years. There’s no other company out there that has the same level of spend data as Coupa.
“It’s the same as Google Maps. The only way that Google Maps works is because everybody uses it. It allows me to get from A to B to C to D, back to A in the quickest time and with the least amount of disruption. The only way that that works is because we’re all using it. And I look at AI no differently in spend as I do with AI in my private life.”
Michael van Keulen, CPO at Coupa
Bridging the talent gap via AI
The need for fresh talent in procurement has never been so important. Procurement, like many industries, is lacking a defined path to welcome the next generation of talent, a feeling which has only been amplified on the back of COVID-19. This means the need to find ways to meet that shortage head-on, whether that’s through education, an industry rebrand or via AI. In van Keulen’s mind, he believes developing the correct tech landscape could hold the key.
“I’ve actually said this for a while,” he explains. “For too long, we brought in super smart people and then we would let them work in some antiquated old-school ERP, in Excel and run RFPs in emails. Nobody wants that, especially the current workforce because they’re used to and have been raised with Amazon, they all have TikTok accounts and are used to all these other e-commerce websites which have very seamless systems. If they come into the workforce and I let them work in some outdated ERP environment with email as the means of communication, that talent is either going to leave procurement because they think it’s boring or they’re just going to leave the overall organisation and work somewhere else. We don’t want that to happen, so you need to have the right tech landscape in place.”
Once the strategy is formed, van Keulen explains that is where the fun of procurement begins. “Then procurement’s the coolest function in the world and we will close the talent gap,” he says. “The talent is out there, they’re just not coming to procurement. They’ll go to finance, marketing, legal or IT instead. If you execute procurement properly, it’s the best because you’re right at the heart of everything. But you need the right people, operating model and operationalisation of your procurement process as well as the right technology. You need all of those elements or it’s never going to work.”
The greatest time in procurement?
Given the disruptive nature of global challenges and its ripple effect on procurement and the supply chain over the past few years, organisations are increasingly waking up to the importance of developing greater strategic relationships with suppliers. COVID-19, inflation issues, natural disasters and wars have meant today’s CPOs have been forced to firefight and think more strategically than ever before. Van Keulen recognises the turbulent nature of recent years and believes major transformation is already underway in procurement. “Historically most executives in any company would pay very little attention to their supply chain,” he reveals. “Due to recent events, companies are realising that they need to be closer to their suppliers. Perhaps in the past, the CEO would only spend a small fraction of their time with suppliers but those metrics are changing rapidly.”
As the ground lies in procurement, some sections of the industry now believe it is the industry’s greatest era given the level of possibilities. Widely considered a back-office function tucked in a corner and working in a silo, procurement is a totally different beast in today’s world. For van Keulen, he likes the variety.
“I wear so many different hats every single day,” he explains. “I always say sometimes I’m an accountant, others I’m an environmentalist. Sometimes I’m the treasurer or a finance person, but I’m also sometimes a psychiatrist. Sometimes I’m a doctor, a nurse, a lawyer, a judge, an environmentalist and yes even a wizard. I never know what my day looks like. I can plan it, but something may happen where everything goes out the window. Procurement will always be going through some type of disruption and it’s about how you drive the competitive edge and how you drive value despite that. Procurement really is the best gig in the world and it’s great that more people have started to see that now too.”
Giorgio Sarno, Senior Data Scientist at Stratio, on how AI and ML can unlock data from both internal combustion and electric vehicles to reduce their carbon footprint and hasten the transition to zero-emission transport.
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A single bus journey pollutes 82% less than the same journey by car.
For this reason, a small decision like taking public transport instead of driving is a big step towards lowering emissions. If we then consider the significant reduction in greenhouse gas emissions that transport operators can achieve by implementing eco-driving solutions or by transitioning to electric vehicles, choosing the bus over personal vehicles becomes an even more sustainable choice. Transport operators are already moving in the right direction in terms of minimising the environmental impact of their services, and they’re doing so by leveraging vehicle data.
The bus is essentially a black box, where vehicle technical data is locked and remains largely inaccessible to transport operators. However, by automating the collection and analysis of this data, fleet managers can rely on artificial intelligence (AI) and machine learning (ML) algorithms to implement a predictive maintenance approach. This means that vehicle sensor data can be turned into actionable insights to help reduce the carbon footprint of internal combustion engine (ICE) buses, hasten the transition to zero-emission transport, and minimise breakdowns and downtime, resulting in a more reliable public transport service.
Car vs Bus
With cars representing 72% of EU road transport emissions, it’s key to make public transport the preferred form of travel. However, in order to create a push towards shared mobility and leverage the environmental benefits of public transport, operators and public transport agencies need to ensure it can live up to the promise of reliability, getting passengers where they need to be, when they need to be there. To guarantee reliability, it is necessary to turn our attention back to the most crucial component of public transport: the vehicle.
AI predictive maintenance is like a digital stethoscope for buses, enabling operators to tune in to the state of health of their vehicles’ critical systems and components. By collecting the data from built-in vehicle sensors and analysing the patterns that indicate the condition of components, maintenance managers can leverage real-time, actionable insights to inform their decisions. AI can identify tricky faults that humans could overlook – tracing leaks in the compressed air system or the wear and tear of brake pads, for example.
With such a system in place, bus operators can depend on real-time monitoring to assess whether their vehicles’ brake pads need to be replaced, meaning that parts can be ordered in bulk and that maintenance can be scheduled during off-peak periods to avoid service disruptions. Maintenance and repairs can be scheduled automatically and more accurately, contributing to better fleet utilisation and cost savings. More importantly, by preventing equipment failure, vehicle breakdowns can be pre-empted to reduce downtime and protect both revenue and customer experience.
The data on equipment behaviour, failure modes, and degradation patterns can also inform asset management strategies, including engineering decisions related to repair, replacement, or refurbishment of components and systems. By extending the useful life of assets and maximising their performance, operators can minimise waste generation, reduce the need for new equipment production, and lower the environmental impact associated with resource extraction, manufacturing, and disposal.
Moreover, early identification of sub-optimal operating conditions enables engineers to fine-tune equipment settings, adjust operational parameters or identify faulty components, reducing energy consumption and resource waste. By optimising resource utilisation, operators can function at higher energy efficiency, reduce carbon emissions, and enhance the overall sustainability of their operations.
Curbing ICE emissions
Predictive maintenance solutions can also be used to inform eco-driving strategies to further reduce the carbon footprint of ICE bus road usage. By analysing driver patterns, optimal RPM and idling time, operators can implement strategies to lower fuel consumption and put in place a range of continuous improvement processes. Arriva Czech Republic has recorded a saving of 942 litres of diesel per vehicle per year using this approach. This equates to 2.6 tons of carbon dioxide emissions avoided per vehicle, per year.
Speeding the transition to EVs
For transport operators, new EV technology poses challenges as well as opportunities. It comes with new breakdown patterns and failure modes and requires a new knowledge-set to minimise life cycle costs and optimise battery maintenance and route management. Additionally, the greater up front, maintenance and infrastructure costs of the transition mean that operators must have a detailed strategy in place to minimise the impact of the shift on their bottom line.
Just as with their ICE counterparts, by combining the granular collection of vehicle data and large-scale data processing with autonomous AI systems, public transport operators can gain valuable insights from the new EV data they have access to, creating a continuous feedback loop that constantly increases the ways in which data can be leveraged. The performance, faults, and range of EVs can be analysed and used to inform the planning of smooth, efficient, and profitable operations.
Predictive battery analytics for example can provide an accurate, comprehensive view of the battery health evolution of an EV bus, allowing for effective route planning and charging requirements, as well as usage optimisation metrics to extend the lifespan of the vehicles. This is crucial given the high proportion of the overall cost of an electric bus that the battery represents. By leveraging State of Charge (SoC) and Depth of Discharge (DoD) data, fleet managers can understand if the operation profile can be changed to maximise battery life, reducing the total cost of ownership of electric buses. This type of analysis is fundamental for an operationally successful and profitable EV fleet deployment.
The future of AI and ML for public transport
By onboarding next-gen AI and ML predictive maintenance technology, the future of sustainable, affordable, and highly efficient public transport is promising. The actionable insights on potential component failures, fuel consumption and operational efficiency offer full control over the health of both ICE and electric buses. This can be harnessed to enhance reliability, encourage passengers to move away from private car usage, curb emissions and wastage through inefficient driving and maintenance strategies, and pave the way for a smoother and faster transition to EV usage.
AI is constantly learning, picking up data about different categories of vehicle and enabling fine tuning for improved operations. It is a system that will keep on growing with huge benefits and impact, contributing to the goals of sustainable and reliable public transport. With some operators already implementing predictive maintenance, the approach will become more ubiquitous in 2023 and beyond, representing the new frontier when it comes to smoother, more efficient and environmentally friendly operations.
Matthias Gutzmann, Founder of DPW Amsterdam, discusses the conference’s rise to prominence, reflects on challenges and reveals future plans.
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“Our challenge is always around asking ourselves how can we make DPW Amsterdam better every year?”
It’s fair to say Matthias Gutzmann, Founder and CEO of DPW Amsterdam, doesn’t believe in standing still and resting on his laurels.
Since launching DPW in 2019, the conference has grown from strength to strength and is now widely regarded as the biggest and most influential tech event in procurement and supply chain on the planet. And despite welcoming over 1,250 procurement professionals with more than 2,500 virtual attendees watching along at home in its 2023 edition in October, Gutzmann is eyeing continuous improvement.
In 2018, Gutzmann was researching procurement conferences to showcase his then-employer, Vizibl, a startup. He was frustrated by the options. The existing conferences were prohibitively expensive for a limited startup budget, lacked investors, and failed to attract an audience of startup businesses, which is critical for the development of digital capabilities and to drive innovation. Identifying this gap in the market, Gutzmann left his job in New York, moved into his parents’ house in Germany, and invested his entire personal savings to launch DPW Amsterdam.
“As soon as one conference finishes, we’re already thinking about the next one,” he explains. “We all sit down and think about how we can improve the experience and what new technologies we can bring in next time. It really is a 12-month process to bring it all together.”
Bringing DPW to life
Held at the former stock exchange building, the Beurs van Berlage in Amsterdam, this year’s theme was “Make Tech Work” which focused on turning digital aspirations into a reality. There was a deep dive into discussions surrounding AI and machine learning in procurement, digital transformation strategies, sustainable procurement, supplier collaboration, risk management as well as innovation and disruption. The two-day event was centred on ensuring the vision of digital procurement happens now and how organisations can be challenged to deliver results instantly instead of only concepts and theories.
Despite significant success, Gutzmann maintains that there are some difficult aspects to get right in order to make the magic happen on the day. DPW Amsterdam builds client booths themselves instead of allowing sponsors to bring them themselves. “That’s a massive undertaking to get this done because we need all the design elements from the sponsors,” he says. “It’s that quality standard but we know it comes with more work instead of just allowing people to bring their own stuff. We have Simone Heeremans, Head of Production, who is amazing and oversees logistics such as catering to the suppliers.
“There is also the sales part of the conference which is selling the tickets and sponsorships. We have created this pull for the conference that we didn’t need to build a proper sales team around it. That said, there’s always a stress factor to get the numbers we want every year and grow it. So far, so good.”
The uniqueness of the conference, the problem it solves, and the timing of the launch in 2019 were the basis for today’s success and fast growth.
WHAT MAKES DPW AMSTERDAM SO UNIQUE?
Matthias Gutzmann:
1. THE AUDIENCE
Traditional procurement conferences only attract procurement professionals. But, DPW Amsterdam recognised the need for breaking this silo and for more collaboration in order to harness the potential of new digital technology, targeting an audience of procurement professionals, business leaders, suppliers, startups, data scientists, investors, and young talents No other procurement conference brings this variety of people together.
2. WORLD’S BIGGEST STAGE FOR PROCUREMENT STARTUPS
DPW Amsterdam is built to bring startups into the procurement ecosystem. In 2023, we displayed over 50 startups, giving delegates a unique insight into procurement innovation.
3. ATTENDEE EXPERIENCE I always thought procurement events felt boring – and I felt lost in a sea of guys wearing suits and ties. So, at DPW, our goal is to make procurement cool and sexy. Not an easy feat, I know. Our dress code at DPW Amsterdam is strictly “startup casual.” You’ll see t-shirts, hoodies, and sneakers from attendees, exhibitors, sponsors, and speakers alike. This dress code embodies our entrepreneurial spirit. But it also breaks down barriers– and levels the playing field between big-shot enterprise CPOs and 20-something startup founders.
Better than ever
A large focus for Gutzmann and his team has been tweaking the formula of the virtual experience. Due to the impact of COVID-19, DPW was forced to cancel its 2020 conference before offering a virtual-only event in 2021. The experience, although different, was praised for its ‘TV feel’ and still created a buzz for those watching at home. However, with day-to-day life returning to a new normal, DPW Amsterdam reverted to an in-person conference in 2022 but offered a hybrid solution for those keen to watch the action from afar. “There wasn’t really anything special about it,” he discusses. “If you run an eight-hour live stream from only one stage, you aren’t likely to keep people watching. That’s why this year we asked ourselves: what can we do to increase the virtual experience? So we did just that.”
This year, Gutzmann and his team set about creating a pop-up broadcast studio to generate a television feel with live coverage from podcaster and host of Let’s Talk Supply Chain Sarah Barnes-Humphrey, as well as a reporter conducting interviews on the expo floor. “Now we’ve got cameras moving around which helps bring the whole conference to life,” explains Gutzmann. “We’ve really ramped it up this year and turned it into a large production.”
Up until this point, DPW has run solely in Amsterdam which Gutzmann believes has acted as his organisation’s competitive advantage. It is this approach that has enabled DPW to allow it to reach the level it is today. Hosted at the Beurs van Berlage, Gutzmann is full of admiration for the historic building which was built in 1896. According to Gutzmann, he believes it is what sets DPW Amsterdam apart from other conferences operating in the space.
“We love it here, it’s unique and I feel it’s a key part of the experience,” he says. “But we’re becoming bigger and we might need to build something completely from scratch. Every year, we think about how we can do things differently. I don’t know if bigger is necessarily better, it’s also about the quality of the solutions we bring in. My goal is to map out the entire end-to-end tech ecosystem and bring in that diversity of solutions.”
Bright future
Procurement, like many industries, is suffering from a talent shortage. The need to find ways to plug that gap, whether that’s through education, industry rebrand or AI, has never been so crucial. With an eye on the future, Gutzmann believes in procurement’s workforce of tomorrow and gave out around 100 free student passes this year. “When we talk to CPOs everyone’s talking about talent shortages so we understand the need to bring in that next generation and show them that procurement could be the way forward for them,” he says. “I think in the context of digital, who better to do digital than the next generation? They are more tech savvy so we need them and it’s a great opportunity for both sides because they can meet CPOs and it’s also becoming a place for recruitment too. We are doubling down on young talent 100% and it’s a win-win.”
Gutzmann is candid about the future of DPW Amsterdam and is always open to feedback while striving for continuous improvement. He believes in the value of innovation and shaking things up in order to best meet attendee’s needs. “I always think we can always bring in new speakers, but this year’s agenda was incredibly strong,” he discusses. “It’s really about listening to the people. Ultimately how can we be more relevant around the solutions as well here? How can we better matchmake people? I was wondering about how we can work pre-event with some of the corporate attendees that are coming to the conference around mapping out their challenges to then have more meaningful matchmaking at the event because it’s an innovation showcase here as well. There’s more value to be had but we know that also comes with more work. There’s always more we can think about.”
With an unprecedented amount of technology at procurement’s fingertips today, Gutzmann is in no uncertain terms about what the next chapter of the space holds. “It’s the best time to be in procurement,” he explains. “It’s the most exciting era to be in procurement and supply chain. We need to get loud about it and celebrate that fact.”
CPOstrategy examines why replacing legacy systems could hold the key in procurement to achieve long-term success.
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As technology evolves, modernising legacy systems in procurement becomes essential.
Change management is never straightforward or linear. Indeed, legacy systems are familiar to an organisation and the workforce might be reluctant to embrace a new way of working, or at least at the very beginning.
But how much damage is clinging to outdated processes doing to an organisation?
Replacing legacy systems
“For many organisations, legacy systems are seen as holding back the business initiatives and business processes that rely on them,” according to Stefan Van Der Zijden, VP Analyst at Gartner. “When a tipping point is reached, application leaders must look to application modernisation to help remove the obstacles.”
People often like their routines and a preferred methodology of how something is completed. This can lead to pushback from the workforce about the purpose of ‘fixing something if it isn’t broken.’ And the point of change for the sake of change is a valid one, up until an alternative which is going to demonstrate tangible benefits. The truth is that most legacy systems don’t allow for growth with older technology often not able to interact with newer systems and processes. In ‘7 options to modernise legacy systems’, Gartner pointed out six main drivers of application modernisation with three from a business sense and three from an IT perspective.
These are business fit, value and agility as well as cost, complexity and risk. If a legacy application isn’t meeting new requirements needed by a digital business, it needs to be modernised to fit properly and should be enhanced to offer greater value to the business. Without agility, a digital business will struggle to keep pace with the latest trends or craze and put the organisation at risk of falling behind competitors. Whereas from an IT side, if the total cost of ownership is too high or if the technology is too difficult to use, then modernising could be vital.
Overcoming resistance to change
Ultimately, change management is an essential component of any Chief Procurement Officer’s role. It can range from a small swap, such as a change of supplier, to wide-scale amendments such as altering the way goods and services are acquired or implementing a procurement or software transformation. According to data from group purchasing firm Una, 70% of change management efforts fail. In order to combat this, there are three key steps to overcoming resistance to change. These are engagement, managing resistance and not neglecting training.
Market disruptions, evolving customer demands and the necessity for a digital landscape has forced businesses’ hands. They are now faced with the task of completing legacy modernisation as a matter of urgency to deliver innovative products and services quickly and efficiently. Failure to do so could lead to being reactive instead of proactive – a risk that in today’s fast paced and ever-changing world that should be taken with caution.
CPOstrategy compiles five ways that ChatGPT can transform procurement amid the rise of generative AI in the space.
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ChatGPT is seen by many as a catalyst for the next wave of technology transformation.
The technology, which was developed by OpenAI, has quickly become the buzzword of the year and one of the hottest topics on the c-suite agenda.
And its promise extends to procurement – an industry that relies heavily on the need for achieving efficiency, transparency and cost savings. Having already made its mark on a variety of industries already, procurement hopes that by embracing ChatGPT it will allow teams to make greater strategic decision-making to drive long-term value.
Here are five ways ChatGPT can transform procurement.
1. Rapid research
Through ChatGPT, time-consuming and cumbersome tasks such as research can now be completed almost instantly. Generative AI tools such as ChatGPT can analyse significant amounts of data and provide insights on market fluctuations while also searching for new suppliers, products and capabilities to secure better deals.
2. Automated procurement processes
ChatGPT can be used to discover patterns and identify trends which will allow procurement teams to make data-driven forecasts. Through leveraging predictive analytics, organisations can anticipate demand, optimise inventory levels and manage their supply chain more effectively.
3. Easier communication with suppliers
Tools such as ChatGPT can improve supplier performance tracking through automating data collection and analysis. Its focus on cooperation and transparency throughout the procurement process allows for stronger supplier relationships and more innovative thinking.
4. Enhanced risk management
A major benefit of generative AI in procurement is improved risk management and the ability to foresee potential dangers. Through identifying potential hazards such as financial instability among suppliers or non-compliance with procurement processes, ChatGPT can help businesses manage and reduce risks.
5. Cost savings and increased efficiency
ChatGPT can help organisations to save costs by automating operations, increasing stakeholder participation and allowing real-time data analysis. By reducing the amount of time and effort for tasks like evaluating bids and selecting a vendor, ChatGPT could shake up the procurement process immeasurably.
At DPW Amsterdam, Gregor Stühler, CEO and Co-founder of Scoutbee, and Karin Hagen-Gierer, CPO and Strategic Advisor at Scoutbee, discusses the rise of chatbots and their influence in procurement.
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Scoutbee was created with the idea of improving supply chain resilience through AI and big data to transform the way organisations use supplier data to discover and connect with suppliers.
The company, which was founded in 2015, offers an AI-powered Scoutbee Intelligence Platform (SIP) which uses graph technology and predictive and prescriptive analytics to deliver holistic supplier visibility that helps procurement make confident supplier decisions, drive cross-functional efficiency and optimise existing technology investments.
Scoutbee’s AI-driven data foundation connects teams to any data point, internal, external, third-party and more, as well as any data combination necessary to orchestrate a resilient, competitive and sustainable supply base.
Gregor Stühler is the CEO and Co-founder at Scoutbee. He believes that waiting to invest in AI tools and underlying data training will be companies’ greatest sustainable disadvantage of the next decade. “AI is not an off-the-shelf product, so you can’t buy AI unless it’s a pre-trained AI on a specific use case but then it’s not a competitive edge,” he tells us.
“A competitive edge only emerges when you have a clear use case and training on top of that. The companies that start using those AI solutions sooner with their data have much better training in place. As a result, they’ll always be ahead of the game quite significantly. Companies that use off-the-shelf AI products will do well, but the ones that actually take it meaningfully and start trading on their own use case and their own data will be the ones that will be accelerating.”
Gregor Stühler, CEO and Co-founder and Karin Hagen-Gierer, CPO and Strategic Advisor, at Scoutbee
AI – Changing the game?
Karin Hagen-Gierer is CPO and Strategic Advisor at Scoutbee. She explains that there are a multitude of ways in which tools such as generative AI are having an impact on procurement to change the game.
“AI is great to help with mundane and boring tasks,” she discusses. “It can help us with vendor requests that come in and can be appropriately channelled. It can help your colleagues to navigate procurement. When they have questions, they can interact with a chat solution and be guided in a much better way to find what they want much quicker. I think if we look at how it can enhance our teams’ effectiveness, it is really in market analytics, supplier searches, supplier evaluations, and ChatGPT that could help us broaden the spectrum. If you then look to more tailored solutions like Scoutbee then it’s a very different ball game that procurement professionals have at their fingertips. I’m noticing a drive on both efficiency and effectiveness in this space.”
Despite AI’s draws, Stühler is well aware of the challenges and hesitations around digital technology. As far as he is concerned, there are two waves of generative AI to be aware of. “Wave one is about having a prompt and how tools such as ChatGPT can help with that,” he says. “For example, what are 10 RFI questions for aluminium cans?
“Wave two is where I merge and synthesise all of my data into our large language model and it has reasoning to drive decision-making and scenario planning. You do have to be careful though because you have to give the system all your critical data but you don’t want to input this into an open model. This means the use case has to be that you deploy a large language model in your own infrastructure, and own your own graphic card that will never actually leave your organisation.
Gregor Stühler, CEO and Co-founder at Scoutbee
“This is the biggest concern that we’re seeing because ChatGPT has brought a lot of progress but also a lot of questions. Now, when people hear that we want them to merge their data into a large language model that’s completely private, we’re met with some resistance when we explain to them that their large language model is running on their very own graphics card that we don’t have access to. That tends to give them more comfort to put their data into it,” he continues.
Stühler adds that he believes there are some misconceptions around ChatGPT and the nature of how accurate the data it provides actually is. As is the case with any new technology, these things take time. “It’s always the same. It happened with electric cars, nobody thought that would solve the battery issue,” he discusses. “I think we are right at the peak of the hype cycle when it comes to those things and people have figured out what they can use it for. With wave one of generative AI, it is fine to have hallucinations of the model and if something is spat out that is not supported by the input.
“But by the second use case, hallucinations are not okay anymore because it’s working with accurate data and should not come up with some imaginary creative answers. It should be always supported by the data that is put in. This is very important that people understand that if you train the model and if you have the right setting, those hallucinations will go away and you can actually have a setting where the output of the model is 100% accurate,” he further emphasises.
Procurement’s potential
According to Karin Hagen-Gierer, there is an incredible opportunity to create value in procurement today. Following unprecedented global challenges over the past few years, CPOs have never been in the boardroom so often – something she’s keen to stress.
“The value of procurement through crisis has been proven,” she says. “We tend to say, it’s not a core business, but very often if things don’t go right, it becomes core very quickly and you are in the CEO’s office more than you might like. It’s the breadth of the role that allows to drive value: You impact the P/L impact, topline, and the ESG agenda to name a few. But then there is a need to future-proof your team’s skill set around how you can drive more impact from being more effective in the respective tool sets you’re using, the questions you’re able to solve solutions for. Additionally, you have to work on improving your efficiencies. Teams are not getting bigger, so you need to be enabled in a very different way to really drive all this value.”
Karin Hagen-Gierer, CPO and Strategic Advisor at Scoutbee
Stühler reflects on the past and admires the transformation procurement has undergone in the past decade since he joined the industry. “I came to procurement in 2012 and even then I remember this function being solely responsible for paying invoices and calling trucks to arrive sooner – at first glance,” he says. “Combined with the crises that now happened over the last couple of years, post-Covid has proven procurement’s value – and the impact organisations such as Scoutbee can make.
“I think two key things will happen in the future. Firstly, the tech landscape is exploding so quickly that there must be a consolidation that will happen. Secondly, when it comes to generative AI I think those pragmatic use cases will become the new normal. ChatGPT will be like Google today to get insights. Generative AI and large language models will get increasingly powerful over time and will help if you feed it the right data and connect it to different data streams that you have internally. It can become this true copilot and help you with complex scenario planning and make you aware of weak spots in your supply base while helping you to strategically take the right steps. The future is exciting,” he concludes.
Stefan Dent, co-founder at Simfoni, and Richard Martin, CEO at Thinking Machine, discuss the power of data in procurement and the future of AI.
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“See spend differently”.
Simfoni is revolutionising how businesses spend their money – via data. In today’s ever-changing world, everything is underpinned by data at Simfoni.
Founded in 2015, Simfoni is a leading provider of spend analytics, Tail Spend and eSourcing solutions to global businesses. Simfoni’s platform utilises machine learning and AI to accelerate and automate key parts of the procurement process which saves time and money while creating a pathway for supply chain sustainability. Its solution quickly distils and organises complex spend data to help discover opportunities and savings. It also gets up and running in days with an on-demand spend automation solution.
Indeed, Simfoni aims to take the hassle out of procurement through its automated, fluid platform that offers a unique pay-as-you-save pricing model which reduces barriers to technology adoption. Through fused revolutionary technology with AI-enabled content and deep expertise to automate, streamline and simplify procurement. Simfoni’s composable platform provides a selection of advanced automation modules that help customers sky-rocket savings and achieve sustainability objectives.
Stefan Dent, co-founder, Simfoni
Stefan Dent co-founded Simfoni and now serves as Chief Strategy Officer. He tells us his organisation was created ‘with a purpose to be different’. “A lot of customers have been working on full suite solutions for some time, which was seen as a sort of panacea for all ills that would solve everything,” says Dent. “It solved some areas such as direct spend, but these are large, mega expensive solutions that aren’t particularly agile. Ultimately, we came up with our own solution which is purposely different. We launched as a composable, agile solution that works with existing systems to boos ROI on tech spend. We apply next-gen technology to procurement that democratizes access to digital procurement tools – opening-up digital solutions to organizations of any size and across any sector. It means we can open our solution up to the masses and not just for large organisations.”
Relationship with Thinking Machine
Simfoni is powered by analytics. Its analytics solution informs spend, as well as watching how change is measured and performance is tracked over time. Now eight years old, Simfoni has fostered alliances with several younger companies offering specialist tools which have been embedded within the Simfoni platform. One such company is Thinking Machine, led by CEO and Founder Richard Martin.
Thinking Machine was founded in 2019 by Martin after he discovered the industry needed to find a better use of data to address ‘complex spend’ such as in Telecoms where you have multiple vendors, manual and frequent billing, changing tariffs and users. Martin explains that he witnessed all types of companies going through the same problems instead of only large companies. “Thinking Machine was developed as a way to give customers a single source of revenue across all services, pricing and demand but in a way that can be done at the very lowest level,” says Martin. “We would take all that complexity and be able to roll it up into actionable evidence that could be reconciled against their top-level financial numbers. It gives procurement directors the tools they need to actually be in the driver’s seat when it comes to their procurement operations.”
Developing key, strategic relationships with partners that can be depended on is an essential component to the success of any long-term business relationship. Simfoni relies on Thinking Machine to help manage its load and enable customers to go deep with Thinking Machine to extract even more value from their data. “We offer our clients the opportunity to go deep within certain domains,” discusses Dent. “We can then bring in Thinking Machine to help extract even more value from the data on complex spend.
Stefan Dent and Richard Martin speaking to CPOstrategy at DPW Amsterdam
“Thinking Machine’s application will ingest a large quantum of complex data. Their tools work like magic and allows data to be put into a readable format so they can make sense of the actual spend and quickly identify optimisation opportunities. This is part of our philosophy to work with niche technology partners because we shouldn’t do everything, so we need to put our resources where it counts. Resources like Thinking Machine work well by plugging into us, which means we offer incremental value to our clients without them going to market separately.
“It can also be very hard for a young company to work with large corporates because they’re untried or untrusted. This means for a company like Thinking Machine to connect with Simfoni is a win-win for everyone.”
Procurement’s bright future
Given the space procurement finds itself in today, the future is set to continue to be transformative. For Martin, he believes the introduction and influence of generative AI tools will help meet challenges in procurement head-on. “For the first time you see how it’s actually possible to be a unicorn with a 10-person team,” he explains. “The scales of efficiency are just out of this world. In terms of the procuretech industry, I think we’ve had a problem for a while now because there’s been all these best-of-breed solutions that are doing bits and pieces but is very difficult to stitch together into one cohesive platform that customers can make use of without having to know how to use 50 different tools.
“I think Gen AI offers a path to helping to smooth over some of those challenges and figuring out how to bring these things together. I think enterprises are going to start finding a lot more value in having all these best-of-breed solutions, such as Thinking Machine and Simfoni, while being able to use AI as a way to put this together into more of a single common layer that they can access. It is a very exciting time.”
For much of the past decade, Dent explains that he has believed that machines will take over mundane and outdated ways of working. Now, with the influence of tools such as Open AI’s ChatGPT, that digital future has only been accelerated and change the workforce of tomorrow. “Most CPOs of today are saying they need more headcount but I think they will soon be thinking very differently,” he discusses. “We predicted some time ago that Procurement departments will get smaller in headcount, maybe by even up to 50%. The procurement function of the future will be a lot smaller, leaner, and meaner. Procurement teams will be more intelligent and strategic, in terms of both the people employed, and the digital tools used to manage spend.”
While Dent believes AI and machines won’t replace every human in procurement, it will mean forward-thinking teams need to embrace new technology with humans taking on higher-value roles. “The shape and structure of the modern procurement function will change quite dramatically, and people will need to upskill,” he discusses. “A lot of the work will be taken over by the machine eventually either 20%, 50%, and then a hundred percent. But the human needs to have that in mind and then plan for that next three to five years. The procurement function of the future will be smaller, and they should purposely be doing that, to then look at solutions to find a way to enable it to happen naturally.
“This is arguably the best time for people to join procurement, as you’ve got this great opportunity to embrace digital and make it happen. Young people can question ‘Well, why can’t it be done by a machine?’ They’re coming in with that mindset, as opposed to fighting being replaced by a machine. I think for graduates coming into procurement, they’ve got the opportunity to play with digital and change the status quo which is a wonderful thing.”
Procurement is one of the leading industries when it comes to embracing new solutions and ways of working. The space is waking up to the massive value that can be created through autonomous negotiations. And making a name for itself in the procuretech ecosystem is Pactum.
Pactum is an AI-based system that helps global companies to automatically offer personalised, commercial negotiations on a significant scale. The system adds value and saves time for both the Pactum client and their negotiation partner by aligning values to determine win-win agreements via easy-to-use chat interface that implements best-practice negotiation strategies.
Scott Mars has been the Global Vice President of Sales at Pactum AI since December 2022. He explains that his organisation is always striving to grow and expand its service offering. “At Pactum AI, we’re defining the space,” explains Mars. “We’re a creator for autonomous negotiations, we work with some of the world’s largest organisations and we’re really looking to expand the pie. The name Pactum originates from the Latin definition of an informal agreement between two parties. We can do up to 10,000 negotiations at once and unlock hundreds of millions of dollars of savings for our clients. We’re typically looking at tail-end suppliers and tail-end spending that no one’s touching. In many cases, that represents 80% of the negotiations.”
Exponential savings
Mars highlights a recent example of incredible savings achieved through Pactum AI’s solutions in a short space of time. Recently, Pactum worked with a travel and leisure firm in the UK to introduce its autonomous procurement solution. “We conducted a very brief implementation over two weeks, which led to a much larger enterprise rollout,” he discusses. “The CPO was actually on holiday while we implemented the autonomous procurement solution with his team. This involved optimizing payment terms with some of his long-tail suppliers.
“When he got back from holiday, there were 50 DocuSigns sitting in his emails, all related to extending payment terms. Many of them were remarkable successes, resulting in an average extension of negotiated payment days by more than 30 days and a 3% average gain from negotiated discounts and discount periods. This means we secured an average discount of 3% on each invoice when paid within the agreed-upon discount term. Our unwavering commitment to enhancing overall value not only positively impacts our clients but also extends to their suppliers, creating a win-win scenario for all involved.”
With AI having such a transformative effect on procurement, achieving efficiency and cost-effectiveness is more streamlined than ever through digital tools. But being alert to new threats, particularly in a space that is so open to innovation, does bring data security concerns. Mars recognises the challenge of cybersecurity and affirms Pactum ensures the safety and confidentiality of sensitive procurement data remains secure in chatbot interactions.
Digital future
“Everything is hosted in a private cloud, so each customer has a private instance. It means all of our data is secure from a generative AI perspective,” he tells us. “Large language models (LLMs) are great, they’re creative but they have their problems which means we’re only using safe LLMs. All of our negotiation design is kept in-house, and we use rule-based explainable AI which means all the data is secure per each customer. We have the largest repository of behavioural science, so those learnings are shared across our customer base, but all the customer data and all their negotiations are private to each customer.”
Looking ahead, Mars is excited about procurement’s digital future and explains Pactum AI’s vision is to transform global commerce. “At the moment, we’re only doing buying, but we are looking to move into the sales side as well,” he discusses. “Large companies have a huge footprint. For example, the Fortune 500 is 66% of the US economy. The plan is for us to move into selling which will give us the scale to transform global commerce. It’s definitely a grand vision, but we do feel that we’ll move from buying into selling and transform global commerce.”
For procurement generally, Mars is adamant that the space is in its “golden age” with the magnitude of vendors within the procuretech ecosystem hitting unprecedented numbers. “I was speaking with a CPO recently and he said 10 years ago you could name the procure to pay and ERP vendors on one hand, now there’s hundreds of them and all these periphery vendors for AI and spend,” he reveals. “The most visionary procurement leaders aren’t just looking at these all-encompassing solutions, they’re bolting on niche solutions into their ecosystems to make their teams more efficient. I think we’ll start to see a consolidation in the coming years of all these little companies into a few larger players to do really an end-to-end type solution. I expect someone to come up with a solution to close the loop in procurement.”
Shaz Khan, CEO of Vroozi, discusses why AI is the great equaliser for companies to optimise procurement.
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In today’s ever-evolving business landscape, companies are facing a multitude of challenges when it comes to managing and controlling their spending. From global supply chain disruptions, outdated technology solutions, labor shortages and much more, these challenges have an immense impact on a company’s financial health and overall efficiency. Additionally, procurement teams are regularly tasked with new responsibilities beyond spend management and purchasing, such as managing supplier risk, building, and implementing CSG and ESG initiatives, studying economic trends to determine price elasticity, finding new sources of supply, and cleaning up disparate and dirty data. Yet most companies simply do not have the human capital or bandwidth to execute these areas with quality and control.
When it comes to bridging the gap between the obligations that procurement teams are tasked with and efficiently executing on these tasks, AI may be the great equaliser to help solve these problems. While AI has turned into somewhat of a buzzword in today’s market, there’s no doubt that the technology has powerful capabilities to truly transform procurement in the foreseeable future. For those changes to take place, it is important for procurement professionals to continue to articulate the problems they are facing on a daily basis, as this will force the industry to evolve and adopt the proper solutions for better business outcomes.
Shaz Khan, CEO and co-founder, Vroozi
The problems: Unchecked spending, outdated tech, and lack of governance
Irresponsible spending can wreak havoc on a company’s financial well-being. With non-managed indirect and direct spend categories, companies experience up to a 40% increase in costs, consequently eroding their gross margins and increasing operating expenses. This usually stems from lack of visibility into non-payroll spend categories, combined with old and antiquated technology solutions within enterprise infrastructure that makes it difficult to extract data, analyse spending patterns, and generate meaningful reports on total addressable spend (sound familiar?). Poor data quality and the need for data cleansing can impede effective spending management, leading to faulty decision-making that hinders procurement efforts.
Unchecked spending can also foster a culture of mistrust and overall decreased morale among employees. When employees perceive that their hard work and dedication are being undermined by wasteful spending practices, workers begin to feel disengaged — which leads to reduced productivity. When spending is not carefully managed, there is a risk that critical projects or departments may not receive the resources they need to thrive. This not only causes anxiety about the organisation’s financial health, but it also can lead to concerns about resource allocation and fairness. Therefore, it creates broader mistrust in organisational leadership.
One of the biggest culprits in inefficient spending management comes from a lack of visibility into supplier contracts, which stifles a company’s ability to identify cost-saving opportunities. Hidden fees, price escalations, and unexpected cost structures can be buried in supplier contracts. A lack of visibility can result in unexpected cost overruns, impacting the organisation’s budget and profitability. Departments may also struggle to fully understand the terms and conditions within these contracts, including performance expectations, delivery schedules, and penalty clauses. This lack of clarity can increase the risk of contract breaches, quality issues, or delivery delays.
The long-term benefits of incorporating AI into procurement
With more at stake within procurement departments than ever before, AI serves as a turbocharged catalyst for procurement teams to optimise their processes. Procurement leaders are increasingly delegated additional responsibilities and AI offers an invaluable assistant that can process, predict, and deliver information and outcomes without exhausting human resources. For example, predictive and smart reordering can keep items that require ongoing restocking on a regular purchasing cycle. AI can also help identify alternative sources or suppliers for this item that may offer additional cost-savings and attractive incentives. As this technology becomes increasingly more capable, it’ll save procurement departments hours of time — freeing up employee bandwidth to then focus on optimising supplier relationships and other strategic tasks.
Earlier, we discussed how unchecked spending leads to mistrust and disengagement within an organisation. AI can help re-establish morale and an engaged staff by gamifying the procurement process. For example, a company can create a scenario where employees and teams are rewarded with soft benefits for complying to procurement policies, reducing maverick spend, improving supplier relationships, or negotiating a new deal with a strategic supplier. These soft benefit rewards can be programmed into the system to track and signal when teams are hitting these goals. Gamification, particularly when entire teams are rewarded together, can foster camaraderie and a dynamic culture built around the thrill of victory, aligning employees with the company’s procurement strategies.
Ensuring a smooth transition to AI-driven procurement processes
When beginning the transition towards an AI-infused process, it requires an honest assessment of existing processes, data quality, and technology infrastructure to identify pain points and areas where AI can provide the most value. Integration will require some level of customization to meet the specific needs of your business, such as custom algorithms, workflows, or user interfaces. This is an ongoing process. Optimisation requires the continuous gathering of feedback from users and stakeholders to identify which areas are working well and which features need improving. Be prepared to adapt as you go along. AI is a rapidly evolving field, and we are in the very early stages of realising the true potential of this technology.
As the AI revolution takes place in procurement, employees need to be introduced to new technologies to understand the strengths and more importantly the limitations. However, when thinking of the big picture, Procurement teams must be prepared to upskill their talent pool and recruit new talent to maximise AI’s potential including investing in certifications in data science, cloud platforms, supply chain management, and data analytics. To reap the benefits of automation, data-driven insights, and enhanced decision-making, leadership requires teams that have skills to use and interpret AI tools effectively — particularly when it comes to data management. AI solutions rely heavily on data and procurement teams must know how to effectively manage this data, including data cleansing, integration, and analysis to ensure that the algorithms receive high-quality input data and large language models for accurate results and the promise of real predictive analytics.
The promise of a brighter future
This is also why collaboration between departments is essential. For AI technology to be implemented effectively, it requires synchronisation and cross-functional collaboration between IT, data science, corporate procurement, finance, and other departments. Companies that cultivate these collaborative ecosystems within their departments gain a strategic edge in terms of stability and future growth.
It’s important to note that while AI is a productivity and enablement tool, it is not a replacement for human intellect, willpower, and execution. Therefore, it’s essential to seek knowledge and expertise from insights from companies, networking groups, and individuals with practical experience in AI and GenAI capabilities. Remember, it’s important that you do not let AI drive your business, but rather let your business needs drive AI adoption. Define the specific problem that you aim to solve and determine if AI is the right tool to boost these areas.
Ultimately, the incorporation of AI into procurement processes holds the promise of a brighter, more efficient future for businesses. Procurement departments face many challenges but if they address these pain points with a strategic approach that involves the adoption of modern technology solutions while upskilling their workforce, businesses can expect to soon see enhanced visibility into their spending and gain a strategic edge in a competitive market. One thing is certain, AI will transform the procurement professional and function into a data analytics and supplier relationship mastermind.
ORO Labs has announced it has raised $34 million in Series B funding led by Felicis with participation from existing investors.
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ORO Labs has announced it has raised $34 million in Series B funding led by Felicis with participation from existing investors including Norwest Venture Partners, B Capital, and XYZ Venture Capital.
The move will see increased support for ORO Labs, which is a global SaaS provider and creator of the world’s foremost smart workflow orchestration platform for procurement, as it scales international and platform growth.
This latest round closes at the one-year milestone of ORO’s launch and the company’s November 2022 $25 million Series A, bringing total investment raised to $60 million.
ORO orchestrates company spend and supplier management across siloed systems and data to improve procurement workflows, increases visibility and makes it easier for business users.
ORO Labs co-founders Sudhir Bhojwani and Lalitha Rajagopalan
Humanising the procurement experience
The innovative platform helps companies quickly create intake workflows, build an integrated and orchestrated procurement tech stack, and dramatically simplify user engagement with purchasing throughout the organisation.
“We’re on a mission to humanise the overall procurement experience, simplifying and guiding end-to-end supplier engagement for efficiency and compliance,” said Sudhir Bhojwani, CEO and co-founder at ORO Labs. “Our Series B financing is further validation, not only of our success in executing, but also the opportunities as we continue to develop and scale ORO for international expansion and a host of new use cases – bringing incredibly easy start-to-finish procurement to even more organisations for agile operations and happy employees.”
“Our 2023 CFO survey identified procurement as the top pain point for CFOs and the number one spending priority,” said Victoria Treyger, general partner at Felicis Ventures. “ORO’s platform approach to orchestrating and simplifying workflows is driving adoption with global Fortune 1000 companies across a range of industries from financial services to pharma. Sudhir, Lalitha, and Yuan share a rare combination of deep procurement knowledge with the passion and insight to transform the category.”
ORO Labs co-founder Lalitha Rajagopalan noted, “I’m personally thrilled to have a woman investor joining the ORO board. Victoria brings keen go-to-market insight and a genuine love for procurement that will help us continue to scale our business, as well as a diverse perspective that aligns with important supplier inclusivity imperatives for our enterprise customers.”
Tackling the future
In use by leading global Fortune 200 enterprises, ORO provides organisations with a next-generation platform that streamlines procurement and reduces supplier cycle time using workflow automation. From intake to spend control, and contract management to supplier relationships, ORO’s smart procurement workflows empower organizations to optimize efficiency and drive success.
“Coordinating a global procurement organisation effectively and holistically with all stakeholders involved is a constant challenge for any enterprise,” noted Matthias Dohrn, President of Global Procurement for BASF. “ORO allows us to better do our part as procurement and orchestrate and scale thousands of value-generating procurement and business measures across the globe, understanding KPIs from a global perspective to streamline our processes, better engage employees and to generate EBIT. The low-hanging fruits are gone, and to manage thousands of improvement ideas, you need a tool to deliver – this for us is ORO.”
The news comes after ORO Labs was announced as the growth stage track winner of DPW‘s DEMO 2023 competition at DPW Amsterdam last month.
At DPW Amsterdam, Ashwin Kumar, vice president at GEP, discusses procurement transformation and what tomorrow’s challenge could look like.
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Transformation. Procurement has witnessed quite a bit in recent years.
Given the widespread adoption and acceleration of AI and data-driven processes over the past decade, change has been a necessity rather than a nice to have.
Evolution of AI transformation
Ashwin Kumar is not unfamiliar with change. Having worked at GEP since May 2008, he has had a front-row seat to the transformation and change procurement has overseen. Now Vice President, he tells us about the evolution of the procurement function and how the landscape is shifting to meet future market demands.
“I think the way we see the industry evolve over time is because we started with web 1.0, simple ERPs that were fragmented with no easy way to connect systems,” he tells us. “Data was all behind firewalls and it was very expensive to manage or mine data. Then we had a big technology breakthrough in cloud systems where the people who were managing the storage said they had a solution. You can just simply push data out of the cloud and what we saw was a lot of that control that the CIOs had on data architecture and the software systems and solutions was being given to different functions.
“A lot of that enrichment of data happened because of the cloud platform that enabled it. Back in 2010, we made the decision to move away from a SaaS platform because even then we believed the future was cloud and that’s where data is going to be which could mean a gold mine. Our CEO made a very conscious decision to basically stop a really good product that was working and move to the cloud platform.”
Ashwin Kumar, Vice President, GEP
The GEP difference
Today, a global leader in AI-driven procurement and supply chain transformation, GEP helps enterprises take the lead and, using the power of data and digital technology, to stay ahead in the connected global economy. More than 1,000 engineers have spent the last 7 months to design and launch GEP’s new AI-native, low-code platform for sustainable procurement and supply chains, GEP QUANTUM. This new platform, launched last week, powers GEP SMART, the industry’s leading source-to-pay procurement application, GEP NEXXE, its next gen supply chain solution, and GEP GREEN, enabling companies to track, measure and achieve their ESG goals.
With the transformative power of AI, GEP enables businesses to operate with greater efficiency and effectiveness, gain competitive advantage, boost profitability and maximise both business and shareholder value. GEP helps global enterprises across industries and verticals build high-performing, resilient and sustainable supply chains.
Investing in dedicated spend analytics and solutions has become an essential part of the procurement process. Data is king and ultimately the more companies know and can predict, the better off they’ll be. However, some companies are still lagging behind when it comes to adopting digital tools created for better visibility and transparency. Kumar questions the reason for this and points to the possibility that there could be a perception that digital tools were hype or a fad – but affirms spend visibility is the real deal.
“If you look at spend data, if I’m the business stakeholder, you’re coming and showing me things that happened six months before,” he tells us. “One of the things we actively tell customers is to understand that there is a difference between spend and cost. Spend is basically the last AP data that you get, which means it’s not even current.”
Procurement’s greatest time?
Given the disruptive nature of the past few years, procurement has had to stand up and be counted. For Kumar, he reflects on global challenges such as Covid, a war in Ukraine and inflation and its knock-on effect on procurement and the supply chain. He maintains that it’s a “difficult time” to be in the industry at the moment given the hurdles procurement and the wider world has faced head-on recently.
“We started off with Covid where we went and told suppliers, sorry, I don’t have money to spend so I’m going to stop spending,” he tells us. “Two months later, you tell them there’s a supply shock and since I’m your preferred customer, can you do something for me? Make sure my products are getting to me on time. Then six months later, there was a war in Ukraine where you were testing suppliers to see which side they were on and questioning whether or not to do business with them. After that, there were inflation concerns so things are constantly changing and you’re pivoting from one problem to another.
“It now means you need to have a platform ecosystem with multiple solution options so that there isn’t a single point of failure and avoid the need for a “transformation” every two years. Given the pace at which things are changing in the macro environment, those single points of failure are quickly going from lack of supply to resilience to risk to people to visibility. It could be something else tomorrow, it could be ESG tomorrow, we simply don’t know. I could have a really good risk assessment tool, but that might not be my focus six months from now – it could be something else. So resilience in the form of digital ecosystem housing different point solutions is paramount.”
Koray Köse, Chief Industry Officer at Everstream Analytics, speaks to us exclusively at DPW Amsterdam and discusses the importance of leading from the front in the supply chain
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Everstream Analytics sets the global supply chain standard.
Through the application of AI and predictive analytics to its vast proprietary dataset, Everstream delivers the predictive insights and risk analytics businesses need for a smarter, more autonomous and sustainable supply chain. Everstream’s proven solution integrates with procurement, logistics and business continuity platforms generating the complete information, sharper analysis, and accurate predictions required to turn the supply chain into a business asset.
Koray Köse is a supply chain expert, futurist and multi-lingual thought leader, CPO, researcher, and published author. He specialises in working with CSCOs, CPOs, CIOs and other c-level executives while possessing more than 20 years of success in developing global supply chain and sourcing strategies, re-engineering and transforming business processes, and maximising financial resources. Köse is experienced in designing new business frameworks, risk and governance processes and deploying full-scale ERP and procure-to-pay systems to drive efficiencies through digital transformation. He is an expert in industries such as automotive, pharma, life sciences, IT, electronics and FMCG and has served as Chief Industry Officer at Everstream Analytics since June 2023.
Koray Köse, Chief Industry Officer, Everstream Analytics
World’s first Slave-Free Alliance
Recently, Everstream became the world’s first Slave-Free Alliance (SFA) validated modern slavery and forced labour technology provider. Everstream’s collaboration combines the firm’s multi-tier supplier discovery and AI-powered risk monitoring and analytics with SFA’s proprietary forced labour intelligence to expose unknown risks and protect global supply chains from modern slavery and exploitation.
“We’ve had issues in supply chain before, like conflict minerals for instance was a big topic,” Köse tells us. “Legislation came that was rather weak, where companies can say we can’t confirm nor deny that we have conflict minerals in our products. Modern slavery takes it to a whole different level. In essence, you may get import issues the moment that you might be suspicious, or the government import controls may say, ‘this comes from a specific region that has general exposure’. You basically have a disruption in your supply chain.
“If you forget about the business side, your business is actually promoting ethics that your own company in its statement and the way you live don’t align with and you didn’t know about it. So unknowingly you have actually incremented the issue that you are tackling on your own and within your environment. For us it was important to live up to the promise and look for an NGO that is impactful, has a mindset that is all about partnership and not blaming or shaming, it’s about changing the environment.”
Breaking down barriers
Around 50 million people worldwide are living in modern slavery. It remains a serious problem in nearly every region, with over 40% occurring in upper-middle to high-income countries. Due to the opacity and complexity of today’s global supply networks, companies are increasingly vulnerable to the risk of forced labour. According to a study cited by Slave-Free Alliance, 77% of companies expect to find modern slavery somewhere in their supply chain. Through this alliance, Everstream will actively contribute to enhancing capabilities and eradicating modern slavery and forced labour from global supply chains.
“We started that partnership to transfer our knowledge and also get insights from their end and understand what the upcoming issues were in the arenas of modern-day slavery that we should keep an eye on and how to help our clients to be informed and avoid getting exposed,” says Köse. “That’s where I started to talk with Hope for Justice and have collaborated with them during my time at Gartner as well. Then legislation is pushing the matter to the forefront of supply chain issues.
“Now, there is also financial impact and disruption and there’s the ability to do good and live up to the promise of your own vision and the way you want to conduct your business. Then I wanted to put our product to test and make sure that it lives up to the promise and if it doesn’t then we fix it. We went through a validation process and we got 90% plus accuracy in the feedback, which is important as it’s another confidence boost that we’re doing the right thing and we should continue on that path. We are the first world’s first validated modern-day slavery solution to tackle the issue – we’re very proud of that.”
The value of due diligence
In today’s fast-paced world, due diligence has become more important than ever. Companies must ensure they are generating the best value for money and that the product that they’re purchasing actually meets their needs. Köse believes companies almost have no choice in 2023.
“It’s an element that is not only preserving value, but it also creates it too,” he explains. “In the past it was more like a checkbox exercise that you conducted because everyone thought it was the right thing to do. Meanwhile, you had spillovers that you didn’t know about. It’s almost like what I don’t know, I don’t care. Since transparency requirements have been augmented significantly and the realisation of transparency as a value driver has dropped through Covid almost instantaneously in the c-level boardroom, compliance has become a value driver.
“It’s not just a checkbox exercise where you say that you are compliant. It is an affirmation of your product quality, brand and innovation that speaks to the customers and the choice they make. If you are concatenating beliefs and values to your product in that moment, you just have created a customer and that customer will be retained throughout the lifetime that you actually care about what they care about.”
Zip has been named as the most innovative fintech solution after being recognised with an award.
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Procuretech firm Zip has announced its platform was chosen as the Most Innovative Fintech Solution by the 2023 Tech Ascension Awards.
The awards evaluate the top innovations in fintech, judging applicants based on technology innovation, market research and competitive differentiators.
Class-leading vendors recognised by the awards deliver technology that solves critical industry challenges and produces valuable business outcomes for customers.
Zip, which delivers an industry-leading intake solution, provides enhanced spend visibility, integrations into a company’s tech stack and new AI capabilities to accelerate workflows and identify savings.
The company’s platform modernises procurement workflows with a single front-door for employee purchases.
Setting the standard
“Our intake-to-pay solution is a revolutionary approach to procurement, and we’re thrilled to be recognised,” said Rujul Zaparde, co-founder of Zip.
“Zip not only improves efficiency across every business function but contributes to a new, highly improved employee experience by solving first for employee adoption of spend controls.
“We’re on a mission to continue setting the gold standard for procurement. Zip is the only platform that seamlessly streamlines procurement processes from intake all the way through to payments.”
The Tech Ascension Awards applicants are judged based on technology innovation, market research, hard performance stats and competitive differentiators.
The awards acknowledge leaders in enterprise and consumer technology. Two panels of enterprise and consumer industry experts judged submissions based on factual company descriptions. They were also measured on relevant statistics and data points as well as distinctiveness in the marketplace.
“As AI, cloud and interoperability serve as the new driving forces, we’re honoured to recognise these leaders in innovation,” said David Campbell, CEO, Tech Ascension Awards.
“We look forward to continuing to recognise companies that hold the power to transform the financial landscape for the better, driving advancements that improve accessibility, security and simplified experiences for users.”
Georg Rösch, Vice President Direct Procurement Strategy at JAGGAER, discusses his organisation’s approach amid significant transformation and evolution
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Procurement is at a sliding doors moment – its direction of travel could go one way or another.
The influx of new technology makes procurement a dynamic and interesting industry in 2023. Following global challenges felt around the globe, procurement practitioners have had to step up in the face of adversity. To the industry’s credit, procurement has so far come through it but now it’s about embracing the world of today and finding ways to deal with pressing issues such as ESG and the knock-on effects of a war in Ukraine while also navigating inflation concerns. Of course, all this is on the back of COVID-19, of which the aftermath is still felt in some quarters.
In a recent CPOstrategy Podcast, Georg Rösch, Vice President Direct Procurement Strategy at JAGGAER, tells us all about how spend management giant JAGGAER is helping procurement teams overcome the challenging backdrop and discusses digitalisation strategies within the space.
Georg Rösch, Vice President Direct Procurement Strategy at JAGGAER
The road in which procurement professionals end up where they do is always an interesting one. Can you tell us why procurement was the path you chose which led to your journey to JAGGAER?
Georg Rosch (GR): “I would say I stumbled into the procurement space. Growing up, I was always a technology person and had a very early interest in computers. When everyone was playing video games, I was playing around with software and started coding. Eventually, one thing led to another, and I found myself in a small procurement startup in Vienna in development. This is really where I found out that this is interesting stuff. 20 years later, it’s crazy to think I’m still doing it because I didn’t even know this field existed and I think that is felt industry wide. But I still love it and getting to combine procurement with technology is something I’m really interested in.”
In your view, how would you explain JAGGAER and sum up what differentiates it from other players in the space?
GR: “JAGGAER has been around for more than 25 years and came together through a lot of mergers and acquisitions. I came through from a branch that was local to Austria and the company has become one of the largest procurement software vendors out there. What I really like about JAGGAER is our vision of autonomous commerce. First of all, it sounds weird for a procurement software vendor not to have the word procurement in the tagline. But that’s done on purpose, because when you think about what a procurement software firm really does, it’s about communication and collaboration between buyers and sellers.
“For a while, JAGGAER was really good at the indirect procurement side which revolved around the whole P2P process. That’s really where a lot of our business came from. But this has evolved over the past 20 years into more of the source-to-contract process that’s being added which is proving so important. It’s not just the execution, but also the strategy of how you build everything and how you find the right sources. As part of autonomous commerce, we created four pillars. It’s networked, intelligent, comprehensive and extensible which spells NICE so it’s very easy to remember.”
Can you expand on the NICE strategy that JAGGAER has developed? What is its true meaning?
GR: “Networked basically means you collaborate with your suppliers, buyers, sellers, partners – everyone. It’s like the modern-day town square where the commerce happens – it’s the foundation of everything. Then it needs to be intelligent which means the question isn’t just about what data you have, but how do you intelligently use the data to drive the processes? Next, you have comprehensive. That encompasses all the functions you have starting from analytics, category management, supplier management, sourcing contracts, ePRO, supply chain management and quality management. It’s all of these beautiful things and how they work together.
“Finally, extensibility means a lot of different things. It means being open to communicating with other systems. With our platform, you can bring in a lot of external data – ESG and sustainability, risk, enriched supplier data, and more – from our partners into our solution. This allows you to make smarter decisions across the procurement cycle. Another aspect is that not every company is the same. Extensibility also means, ‘how can I tailor the solution to my needs?’ This completes the picture that we are working towards here at JAGGAER.”
The procurement space itself has undergone major transformation over the past decade and suddenly, it is so much more than just a back-office function out the way of everyone else. What has been the catalyst for its transformation in your opinion?
GR: “Procurement is really at a make-or-break moment. Supply chain and procurement have been really in the spotlight in the last couple of years. It’s been a case of ‘oh my, there aren’t any shipments coming anymore’ or ‘people are not buying the stuff that they bought before because our whole way of life changed.’ So, we were working from home, and we were not going out to restaurants or buying new clothes because we were all in our tracksuits all day. Society shifted. This meant procurement and supply chain management was really important because they needed to navigate all of this.
“This is why expectations and visibility of these functions rose during that time. But now we’re at a critical point. Can those functions deliver the value that they should? And can they continue this momentum? This is why I’m saying we’re at the make-or-break moment and there are a lot of companies that really made this transition and change to where procurement is an advisor to the business which is so critically important. Think about everything that’s not going away such as ESG with the environmental element, human rights and the governance of those different processes. Procurement is playing such a critical role of managing all these different agendas within our board level topics today.”
How is JAGGAER driving value to companies in a way that perhaps it didn’t before?
GR: “At JAGGAER in procurement, you want to cater to the most mature companies but many of your potential customers are not the most mature firms. It’s a challenge and that’s the balance that you need to strike. You have to be ahead of the curve and in front of the market, so we take this very seriously. We have a dedicated team that’s only working on what we call innovation to uncover questions like how do we use these new technologies? How can we bring this into the solution? How do we drive value for our customers with these things? We did this by coming up with what we call a maturity matrix, where you can see which step of the maturity scale you are on right now.
“It’s five steps in total but no one is at step five yet. The current technology that exists today is at step four, but the space is constantly changing. As a customer, they can measure where they are and say, ‘I might be a two at that process, but I’m a three at that’ and work out what needs attention. They can ask themselves the question, should I even do this? Does this make sense for me as an organisation? We really try to work with those maturity models because it helps us whenever we work with a customer to assess where they are and tell them this is where you can go, and this is what you can achieve by doing that.
“It helps us have the right conversations with our customers which was part of the vision of autonomous commerce. We have autonomous commerce as our North Star and know where we and the industry are aiming for, so it’s imperative our customers know the way too.”
How important is it that any technology introduced actually serves a purpose instead of being introduced for technologies sake?
GR: “People love technology, I love technology. But in business, we shouldn’t use a tool just because we like it. Tools should drive value. I won’t use something just because it sounds fancy. I’ll take whatever solution can truly solve the problem. At JAGGAER, when we evaluate solutions, we always consider what really helps us as an organisation and what drives value. At the end of the day, we are here to make our customers successful. And how is that success measured? Each customer might have different KPIs, but in the end, it’s driving valuation and value for the company. Value can look different for your organisation, whether it’s higher customer or shareholder value. We have to be very pragmatic about the means of how we help because what works for one company potentially doesn’t work for another.”
What does the future of procurement look like to you? How exciting/challenging does the road ahead look for the space?
GR: “I believe it’s continuing the path that we’re on right now which is bringing more data and market intelligence into the whole procurement process. Procurement overall has to move away from gut feeling decision-making. Success stems from bringing all the information that’s needed for procurement into a solution for data-driven decision making. What I’m seeing right now is more strategic information regarding important topics such as environmental impact and human rights. All of this should make a difference and influence the decision making in procurement. This is how procurement drives the sustainability agenda of the company and reliability across the supply chain. This is really where I see procurement going. It’s about taking in all this information, being the advisor to the business, and making the right decisions to drive the company strategy. The future is exciting.”
CPOstrategy visits HICX’s first Supplier Experience Live as organisations gear up to remove friction and become a customer of choice.
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Supplier experience has never been such a hot topic.
After decades in the darkness, the importance of supplier experience is finally on the agenda.
Truthfully, success can’t be achieved alone. Without happy, committed and strategic supplier relationships, a business will stagnate. And now, organisations are waking up to the potential a robust supplier base could unlock.
The rise of Supplier Experience
Earlier this month, HICX launched its first-ever Supplier Experience Live the day before DPW Amsterdam. Hosted at the Tobacco Theatre in Amsterdam, it was recognised as an official DPW Amsterdam side event. The event’s vision was to help organisations use supplier experience to remove friction and become a customer-of-choice.
The half-day event began with a welcome from Ragnar Lorentzen, Chief Commercial Officer at HICX, who opened the door to the world of supplier experience and the market developments that have led the way. Lorentzen handed over to the first keynote speech from Dr. Elouise Epstein who explained that the ERP system was dead. Epstein suggested that the solution could be how well you exchange data with third parties.
Following Epstein was a panel discussion that featured Ruth Bromley, Director of Procurement Enablement at Heineken, Adam Hubbard, Senior Manager of Supply Chain, Governance and Performance at EDF which was moderated by Tommy Benston, VP of Global Client Management at HICX. The conversation advised of ways to gain a competitive advantage in procurement and supply chain through supplier experience management. Bromley highlighted three key learnings: speed, standardisation and simplicity, believing in a “single source of truth”.
Dr. Elouise Epstein
Driving supplier adoption
Later, Anthony Payne, CMO at HICX, discussed how to drive supplier adoption and engagement through supplier marketing. Payne explained the value of segmentation which is the process of dividing the market into subsets of customers who share similar characteristics. Payne equipped the audience with six recommendations to take forward and advised them to use caution with the language they use with suppliers. Following the coffee break was Duncan Jones, former Vice President and Principal Analyst at Forrester Research, who unpacked the reality of how to decide on the correct types of solutions in the new best-of-breed era amidst a transition away from the traditional database-centric approach.
The afternoon continued with a panel discussion involving Marc Bengio, Senior Director and Head of Technology Enterprise Procurement at Johnson & Johnson, Lance Younger, CEO at ProcureTech and Jacy Bassett, VP of Professional Services, to explore the topic “Demystifying the technology landscape: How do you architect for Supplier Experience?” Each speaker gave their viewpoint on how to arm the procurement function of tomorrow to meet the challenge of an ever-changing digital world. The conversation offered guidance and counsel amid an explosion of transformative solutions in the space.
Costas Xyloyiannis, CEO at HICX
Bright future
Finally, Costas Xyloyiannis, CEO at HICX, took to the stage to announce the launch of IUBN which he explained was a streamlined way to identify legal entities in a bid to create net efficiency within the supply chain. One system, one time, everywhere.
Speaking exclusively to CPOstrategy at the event, Xyloyiannis told us, “It’s pretty significant running an event like this. I’ve been in the space 23 years, and finally, I feel like the focus is shifting. Two or three years ago no one was talking about supplier experience so it’s great to see a movement starting to happen. It is very satisfying because you see people’s minds changing in the same way that it did for the customer and employee experience.
“What you have to think about is that almost every company is also a supplier so it’s in your interest to focus on the supplier experience side. In another context, you’re also a supplier and people should understand that we’re all in it together. If you don’t think about solving it, then you’re going to have that pain yourself.”
Supplier experience is just getting started. Reimagine the possible.
Global research and advisory giants Deloitte and DPW has announced a partnership to bring procurement innovation to organisations.
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Deloitte and DPW has announced a partnership to bring procurement innovation to organisations.
Under the terms of this strategic alliance, DPW LABS, the consulting arm of DPW, and Deloitte will work together to refine the boundaries of innovation in procurement.
From problem and strategy definition to proof of concept and deployment, through the DPW LABS innovation capabilities and digital ecosystem and Deloitte’s global transformation capabilities, the move allows for impact to be delivered at scale.
Deloitte is a global provider of audit and assurance, consulting, financial advisory, risk advisory, tax and related services.
The firm, which is a member of the Big Four in professional services, currently has about 330,000 employees in more than 150 countries and territories.
Founded in 2019, DPW stands as a global leader in procurement innovation. DPW LABS empowers organisations to identify and seize collaborative innovation opportunities with DPW’s line-up of pioneering startups, scale-ups, and tech innovation experts.
Herman Knevel, co-founder and co-CEO at DPW, said: “We are excited about this strategic partnership with Deloitte.
“This partnership will enable us to join forces and make tech work, expand and complement our impact at global scale.”
Michiel Junge, partner of sourcing and procurement at Deloitte, added: “We are united in our mission to make procurement awesome.
“The partnership with DPW will enable our clients to tap into DPW’s capabilities and ecosystem and define their procurement future.”
The move comes after DPW welcomed over 1,250 procurement professionals to Amsterdam for its annual conference.
DPW Amsterdam has quickly made its name as a hub of innovation and collaboration. It is one of the biggest and most influential tech events in procurement and supply chain.
CPOstrategy travels to the Netherlands to soak in the atmosphere of one of the world’s biggest and most influential tech events in procurement and supply chain – DPW Amsterdam 2023
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“You are the reason why DPW exists.
“It’s been my mission from day one to break procurement out of its silo and create what I call the end-to-end ecosystem and that is you.”
Digital Procurement World (DPW) Founder Matthias Gutzmann’s first address to the crowd gathered before the main stage had a clear tone of appreciation.
The rise of DPW Amsterdam
Today, DPW Amsterdam is one of the world’s biggest and most influential tech events in procurement and supply chain. Its exponential rise in a relatively short space of time is undeniable. Its story began with a frustrated Gutzmann having discovered a lack of procurement conferences to showcase his previous employer. This led to Gutzmann finding a gap in the market and set about solving the issue himself. He left his job in New York, moved into his parent’s house and invested all his savings to launch DPW. Months later, DPW’s launch conference in September 2019 welcomed 400 industry leaders while being praised from across procurement. Under the watch of Gutzmann and co-CEO Herman Knevel, DPW’s influence and pull has only grown since.
This year’s event was located at the historic former stock exchange building, the Beurs van Berlage. Built in 1896, the building breathes character and history. Its architecture and rich past, alongside its central Amsterdam location, showcases its sense of place and being.
Innovation
DPW Amsterdam has quickly made its name as a hub of innovation and collaboration. This year, more than 1,250 procurement professionals gathered to connect, learn and innovate, while over 2,500 virtual attendees watched along at home. The buzz and hum of chatter was audible, the sense of excitement evident. And the attendees were certainly in for a treat. This year’s theme was “Make Tech Work” which focused on turning digital aspirations into a reality. There was a deep dive into discussions surrounding AI and machine learning in procurement, digital transformation strategies, sustainable procurement, supplier collaboration, risk management as well as innovation and disruption. It was all centred on ensuring the vision of digital procurement happens now and how organisations can be challenged to deliver results now instead of only concepts and theories.
Speakers across the two days included renowned experts and visionaries including the likes of Dr. Elouise Epstein, Partner at Kearney, Yossi Sheffi, Director of Massachusetts Institute of Technology and author David Rogers, among dozens more. Sarah Barnes-Humphrey led superb virtual coverage of the event and allowed those unable to make it to still feel a part of such an important conference in the procurement calendar. There were book signings from Sheffi and Atif Rafiq, eye-catching tech innovations showcased on stage and even an appearance from F1 legend and Haas Formula One team principal Guenther Steiner.
Digital future
To sum up, in comedian and host of DPW Amsterdam Andrew Moskos’ opening speech he reflected on procurement’s evolution and transformation. “Procurement used to be boring but now we’re all rockstars. We run the company, we’re in the c-suite, we run ESG, sustainability, risk, and 80% of the spend of a company goes through us.”
Change is here and procurement holds the cards. Let’s Make Tech Work.
CPOstrategy examines 10 of the best ways to use artificial intelligence (AI) in procurement
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Artificial intelligence (AI) is one of the biggest buzzwords in procurement. Everyone wants to get their hands on it and introduce it into their strategies.
Particularly in procurement, AI is often talked about being the answer to all challenges. It can be used to overcome complex problems and deliver efficiency while also being introduced within software applications such as spend analysis, contract management and strategic sourcing.
In this article, we will list 10 of the best ways to use AI in procurement.
1. Machine learning spend classification
AI algorithms can help categorise, clean and classify data automatically. Machine learning spend classification helps detect patterns and uses them for prediction while allowing for better decision-making. Examples of spend classification techniques include supervised learning, unsupervised learning in vendor management and classification reinforcement learning.
2. Natural Language Processing (NLP)
National Language Processing (NLP) is the branch of artificial intelligence focused on understanding, interpreting and manipulating human language. It can be used to gain valuable data and information to streamline time-consuming processes. Information contained in legal documents can be interpreted through AI for the procurement of relevant data. It allows procurement professionals to get ahead and use an AI assist engine to receive alerts to proactively monitor progress. It also allows for compliance over the life of multiple agreements with the same or several vendors.
3. Robotic Process Automation (RPA)
Robotic Process Automation (RPA) mimics human actions to eradicate repetitive tasks. While not strictly AI in the traditional sense, RPA does provide procurement with opportunities to improve process efficiency and is part of the wider family of AI. It can assist with the likes of contract management, input identification as well as purchase request and order submission, among more benefits.
4. Anomaly detection
With AI being able to process vast amounts of data quickly, it is able to stay up to date on the latest developments and changes in the procurement space at speed. Automated notifications on things such as anomalies, new opportunities and recommended activities allows for immediate action to be taken and provide suggestions on what should be done instantly. Rapid detection will ensure risks are mitigated and resolved before they become problems.
5. Purchasing
AI can be utilised to automatically review and approve purchase orders. Chatbots can be used to check the status of acquisitions or automatically approve virtual card payments. AI can analyse data and assess the reliability and quality of suppliers based on predefined criteria. This helps the purchasing team select the best suppliers quickly and accurately.
6. Contract management
Contract management can benefit through using AI to create, store, review, index, retrieve, analyse, negotiate and approve agreements. A big benefit delivered by contract management solutions that use AI is standardised metadata reporting which eliminates the need for category managers and legal counsels to manually read contracts to gain insights into the commercial part of their supplier relationships.
7. Supplier risk management
Supplier risk management is an important part of the procurement process and is around understanding what happens if a supplier fails to meet its obligations. To combat this, AI can be used to monitor and work out potential risk position through Big Data. Millions of different data sources are screened in order to provide alerts on potential risks within the supply chain.
8. Accounts payable automation
AI can automate most manual tasks in accounting such as data entry and invoice routing. Using AI for this substantially reduces procure-to-pay cycles, minimises the need for humans to get involved and integrates multiple workflows into a seamless process.
9. Strategic sourcing
Using AI in strategic sourcing is a key tool in a procurement practitioner’s arsenal. AI can be used to manage and automate sourcing events while also leveraging machine learning for the recognition of bid sheets, as well as specialised category-specific e-sourcing bots such as raw materials and maintenance.
10. Automated compliance
AI can also be used as a valuable tool for compliance officers to help work out potential risks, monitor employee behaviour, generate reports, provide recommendations as well as educating employees about the importance of compliance. For organisations without a source-to-pay system, compliance is a useful alternative and allows procurement teams to seamlessly compare payment terms, identify duplications as well as determine non-compliance.
Doug Laney is Innovation Fellow at West Monroe and a leading Data & Analytics strategist. We caught up with the author of Infonomics and Data Juice to talk tech and how companies can measure, manage and monetise to realise the potential of their data
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Our cover story explores the rise of data and information as an asset.
Welcome to the latest issueof Interface magazine!
Interface showcases leadersaiming to take advantage of data, particularly in a new world of AI technologies where it is the fuel…
How to monetise, manage and measure data as an asset
Our cover star is pretty big in the world of analytics… We meet the guy who defined Big Data. Doug Laney is Innovation Fellow at West Monroe and a leading Data & Analytics strategist. We caught up with the author of Infonomics and Data Juice to talk tech and learn how companies can measure, manage and monetise to realise the potential of their information. In his first book Laney advised companies to stop being fixated on hindsight-oriented analytics. “It doesn’t actually move the needle on the business. In the stories I’ve compiled over the last decade, 98% have more to do with organisations using data to diagnose, predict, prescribe or automate something. It’s not about asking questions about what happened in the past.”
Canvas Worldwide: A data-driven media business
Continuing this month’s data theme, we also spoke with Alisa Ben, SVP, Head of Analytics at full-service media agency Canvas Worldwide. Data has transformed the organisation, and what its clients do. “We look holistically at the client’s business and sometimes the tools we have might be right for them, sometimes not. It’s more about helping our clients achieve their business outcomes.”
TUI Musement: from digital transformation to digital pioneer
At travel giant TUI, handling data effectively is paramount when communicating consistently and meaningfully with up to 25 million customers annually. David Garcia, CIO for TUI Musement, talks about the tech evolution driving the travel giant’s provision of experiences, transfers and tours. It’s a big part of its operational shift from local to global. “As a CIO, I’ve always been interested in how the tech innovations we drive can support the business and add value.”
Hiscox: making cybersecurity more accessible
Liz Banbury, CISO at Hiscox and president of (ISC)² London Chapter, talks to us about how cybersecurity can become a more accessible, realistic career path for almost anybody. “When I was at school, topics like computer science didn’t even exist,” Banbury explains. “In one of my first jobs, over in Hong Kong, we were still using a typewriter! A lot has changed. My key point here is that there’s a lot of cybersecurity professionals who are really good at their job. They are inspiring, and have come from all walks of life. Crucially, they don’t have a maths, computer science, or technological background at all. But they still make great cybersecurity professionals.
Portland Community College: Risk vs Speed in Cybersecurity
Reet Kaur, former Chief Information Security Officer at Portland Community College, discusses the organisation’s transition to the cloud amid a digital transformation journey. “I don’t want to work with people who just say yes all the time. I want my ideas challenged to help forge the excellence in the security programmes I help build.”
DBHDS: Cybersecurity in healthcare
The Virginia Department of Behavioral Health and Developmental Services (DBHDS) exists to create ‘a life of possibilities for all Virginians’ and transform behavioural health. Its focus is on supporting people across the entire commonwealth. It helps them get the support they need in order to take wellness and recovery into their own hands. In an area like healthcare, sensitive information is all over the place, meaning cybersecurity is a priority – and this is where Glendon Schmitz, CISO at DBHDS, comes in. “The security team exists to help the wider organisation achieve its objectives with data. We’re there to protect the business, not the other way around.”
Also in this issue, we schedule the can’t miss tech events and get the lowdown on IoT security from the Mobile Ecosystem Forum.
This month’s cover story sees us speak with Brad Veech, Head of Technology Procurement at Discover Financial Services.
Having been a leader in procurement for more than 25 years, he has been responsible for over $2 billion in spend every year, negotiating software deals ranging from $75 to over $1.5 billion on a single deal. Don’t miss his exclusive insights where he tells us all about the vital importance of expertly procuring software and highlights the hidden pitfalls associated.
“A lot of companies don’t have the resources to have technology procurement experts on staff,” Brad tells us. “I think as time goes on people and companies will realise that the technology portfolio and the spend in that portfolio is increasing so rapidly they have to find a way to manage it. Find a project that doesn’t have software in it. Everything has software embedded within it, so you’re going to have to have procurement experts that understand the unique contracts and negotiation tactics of technology.”
There are also features which include insights from the likes of Jake Kiernan, Manager at KPMG, Ashifa Jumani, Director of Procurement at TELUS and Shaz Khan, CEO and Co-Founder at Vroozi.
This month’s exclusive cover story features a fascinating insight into the procurement function at lighting giant, Signify.
A forward-thinking enterprise constantly reevaluating and adapting its operations against an ever-changing landscape, Signify has recently transformed its procurement function. And so we join Luc Broussaud, Global Head of Procurement/CPO and Arnold Chatelain, Transformation Program Director for Signify’s Procurement Organization to see why, and how, they have evolved procurement at the company.
Signify is a global organisation spread over all continents and Luc heads up the procurement function. According to Luc, he and his team no longer engage in traditional transactional procurement, but instead leverage digitalisation to deliver competitive prices as well as what they call ‘concept saving’, “Which is how we redesign or improve our product; leveraging the knowledge of our suppliers to make it cheaper, more efficient, easier to manufacture and install, and more sustainable for the planet.”
Luc joined Signify in 2018, after being the CPO of Nokia (based in Shanghai) and has always been working within procurement. He joined Signify with a broad skillset and a wealth of experience. “I joined because the people I talked to, from the COO to the CEO and CFO were all incredibly knowledgeable and passionate about procurement,” he reveals. Read the full story here!
Not only that, but we also have some incredible insights from procurement leaders at Heijmans, Datadog, HICX, DPW, ProcureCon Asia and SourcingHaus Research! Plus, the very best procurement events of 2023.
We explore the transformation of sustainability in procurement & visions of a future where sustainability & procurement are fully integrated.
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Dr Carsten Hansen, Founder of SourcingHaus Research and Consulting Group, explores the transformation of sustainability in procurement and envisions a future where sustainability and procurement are fully integrated and mainstreamed.
Mike Randall, CEO at Simply Asset Finance, discusses how to build a people-first strategy that enables growth.
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As the UK economy continues to balance on the edge of a recession, employee retention is quickly being pushed to the top of CEOs’ lists. Over the past couple of years, the job market has shifted dramatically with previously unheard terms such as ‘the great resignation’, ‘quiet quitting’ and ‘hybrid working’ becoming commonplace. People are rightly prioritising their working situation and job satisfaction levels, questioning whether they believe in the organisations they are committing so much time to.
Consequently, there has been a power dynamic shift in favour of the workforce. Reportedly in the third quarter of 2022 businesses witnessed over 365,000 job-to-job resignations across the UK. In similar fashion, the phenomenon of ‘quiet quitting’ – doing the bare minimum required of a job – has become a growing concern but its rise is prompted by a growing number of employees feeling disengaged in their roles.
Against this backdrop of a highly turbulent job market, and increasingly difficult macro-economic pressures, it’s vital for CEOs to prioritise a people-first strategy to ensure healthy growth for their business in 2023. Data from Deloitte has even revealed that experts believe how engaged a workforce feels can directly correlate to overall business output, with 93% of HR and business leaders in agreement that building a sense of belonging is crucial for organisational performance.
Mike Randall, CEO at Simply Asset Finance
However, creating the right environment and recruiting, maintaining and nurturing the right talent to ensure a people first approach can be daunting. With this in mind, here are four learnings CEOs might want to consider when approaching this challenge:
1. Define your beliefs
Before CEOs and founders can hope to attract the right talent, it is critical to first distil and translate the business vision into something that can be understood by employees. Put simply, this means defining the business’ beliefs.
Some business leaders may already refer to this as an ‘employer brand’, and it can be key to not only securing better talent, but also saving a business money in the long-term. Data from LinkedIn for example, recently found that a strong employer brand can help to reduce employee turnover by as much as 28% and cost-per-hire by 50%. Defining these beliefs – or the tenets a business does and doesn’t stand for – is therefore the perfect exercise to put a vision onto paper, and clearly communicate it to its prospective talent.
2. Build a solid culture
Once these beliefs have been defined, they must be reflected, and built into a strong culture. A business’ beliefs should permeate through the whole organisation – from customer communications, to how staff are treated, to how leaders run the business. Culture should essentially be a representation of a business’ beliefs being put into practice.
Building a strong culture in a business, however, is not solely about these beliefs but also extends into how employees are equipped with the tools they need to succeed. Companies that invest in learning and development for example, have been found to benefit from a 24% higher profit margin than those that don’t, according to the Association of Talent Development. Training and development should therefore be seen as a worthwhile and necessary investment that can solidify your culture and ensure profitability, not just an unavoidable cost.
3. Invest in retention
With research from Oxford Economics estimating the average turnover per employee earning £25,000 a year to be £30,000 plus, there is an evident cost to businesses that fail to invest in retention. Tackling this will mean regularly taking the time to truly understand what makes employees tick – and more specifically, understanding their motivations, attitudes, behaviours, strengths and weaknesses.
As the past few years have evidenced, individuals are no longer deciding where they work solely based on salary, but are also thinking about employer values, flexibility, and benefits. To avoid employee churn, businesses should regularly take time to understand what drives their employees and implement retention strategies to address these drivers. Gathering and analysing employee data will play an important role here over the coming years, and should be built into a long-term strategy to optimise employee satisfaction.
4. Build for the future
A common challenge encountered by modern businesses and startups wanting to take a people first approach, can be their ability to stay committed to it. As a business grows in size and becomes successful, it can be all too easy to let external factors dictate its purpose and for it to lose sight of what it initially stood for. The reality is that when this happens, a business is in its most vulnerable state – as its beliefs become increasingly distant, and worse, employees no longer understand what it stands for.
When creating a people-first strategy its therefore important to think long-term. If there are external factors that will potentially put this strategy at risk in future, it’s crucial to identify them, and put in practical steps to mitigate them where possible. The pandemic, for example, is a prime example of an external factor that interrupted the status quo of many businesses – disrupting employees, customers and operations in general. While they can be unpredictable in nature, having a plan to get through these times can help to get you back on track and reassure talent that a solution is in place.
In this economic climate, defining beliefs, building a solid culture, and retention plan should be at the core of every business’ strategy. It’s only when these things are in place that a business can hope to attract and retain talented people that exude the same passion and values built into the heart of a business. As while a business’ growth may be defined by its leaders, it is delivered by its people who are putting that vision into practice.
Procurement is in a state of flux. Against a backdrop of economic uncertainty, the procurement landscape is volatile and requires…
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Procurement is in a state of flux.
Against a backdrop of economic uncertainty, the procurement landscape is volatile and requires agility to navigate turbulent waters. But, despite significant disruption could there still be opportunity?
Simon Whatson, Vice President of Efficio Consulting, is optimistic about the future of digital procurement and despite a challenging few years he is confident of a successful bounce back. He gives us the lowdown on the direction of travel for digital procurement in 2023.
As an executive with considerable experience in the space, we’d love to learn more about your background and how you ended up in procurement. Why was this the specialism for you and how did you get involved to begin with?
Simon Whatson (SW): “I think the one-word answer of how I came into procurement was accidental. I studied maths at university, with a year in France, before I began looking for different roles to apply for.
“Eventually, I was offered a position with a big plumbing and heating merchant with global operations. I worked in that supply chain team for two and a half years. Although it was called supply chain, a lot of the work was procurement, which involved negotiating with suppliers. It was after that stint there, that I discovered consulting and joined a boutique procurement consultancy. Now I am onto my third consultancy and I’m very happy here!
“In terms of why I’ve stayed, one of the success factors in procurement is being able to work cross-functionally. Procurement doesn’t own any of the spending that it is responsible for helping to optimise. It must work with other functions and the spend owners. I quite like the people side of that, building relationships, almost selling internally to bring teams together. That really appeals to me and is a key reason why I’ve been very happy in procurement.”
As we move into exploring procurement today in 2023. The space is filled with challenges and complexities. You only need to look at the last few years. Covid, war in Ukraine, inflation – how would you describe the world’s recent challenges and their effect on the industry and what do you feel CPOs and leaders can do to combat these issues?
SW: “I would flip it around and say that these are not so much challenges but rather opportunities for procurement. When I started my career 18 years ago, procurement was often fighting to get a voice and there were complaints that procurement was not represented at the top table, but the war in Ukraine, inflation, COVID and ESG, these are things which are now on the C-suite agenda and procurement is ideally positioned to help companies face those challenges. If you think about COVID and the war in Ukraine, procurement is in a privileged position to help with this.
“I see some procurement functions that prefer to do what they know, which focuses on the process and transactional side. However, there are also many forward-thinking CPOs and procurement professionals out there, that have really seized this opportunity of being on the C-suite agenda and drive the thinking and the solutions to some of these big challenges we’re seeing.”
Although new technology in procurement has been around for well over a decade, digitalisation has become so much more of an important topic. How would you sum up where procurement and supply chain are in terms of digital transformation today?
SW: “It’s a bit laggard, but digital transformation is difficult, and we have to recognise there are some real trailblazers. There are some firms doing some fantastic things in digital to produce better outcomes. If you contrast your experience when you’re buying something in your private life, it’s much easier than 20 years ago. You can get access to a wealth of pre-sourced things, whether it’s food, a holiday, a car, or a book. You can see reviews of what other people think of these things.
“But when you go into your workplace as a business user and you want to buy something, it doesn’t quite work like that yet. You often have to fill in a form, send it off and wait for them to come back to you. They might come back a little bit later than you were hoping and might tell you that they don’t have that part on the supply frameworks. I think people sometimes get confused about how it can be so easy to buy something as large as a car or a holiday on their sofa at home, but when they want to buy something at work, it seems to be quite cumbersome. Digital can help a lot with that, but it is incumbent on organisations and procurement functions to figure out how to recreate that customer experience that we’ve become accustomed to in our private lives.”
With a new generation of leaders growing up with technology, some might say that it could be a key driver in helping to speed the adoption in procurement along. Is this something you would agree with or what would you point to as a key driver?
SW: “I do think that it will act as one of the catalysts for further digital transformation in organisations, because if procurement doesn’t manage to recreate that customer experience that the new generation expects, then they won’t use procurement going forward and will look to bypass it.
“The analogy that I’ve used previously in this case is one of travel agents. I remember as a child, my parents were able to take us on holiday and I remember the whole process. We would walk into town to the travel agent, and look at some of the brochures of options. They often then had to phone the various airlines or resorts on our behalf. They might not be able to get through, so we’d have to come back the next day. I remember as a child being quite excited by the whole process but actually, thinking back, it was quite cumbersome. You compare that to now, with being able to review online, and you can get instant answers to your questions. It’s not a coincidence that travel agents don’t really exist anymore.”
How much of a challenge is it to not get caught leveraging technology for technologies sake? How important is it to stay true to your approach and be strategic?
SW: “We conducted a study of many procurement leaders and CPOs a few years ago, and one of the things that we found was that about 50% of procurement leaders admitted to having bought technology just on the basis of a fear of missing out, without any real understanding of the benefits that technology was going to bring. That was a real shock and a revealing find because technology is not cheap, and its implementation is quite disruptive. If you’re purchasing a system because everybody else is using it, then there could be some pretty costly mistakes. It is really important to make sure that when buying technology, it is because the benefits are fully understood.
“My advice to companies when looking to digitalise is own your data, visualise that data, and manage your knowledge. If you can focus on getting those things right in that order, and make your technology decisions to support that goal, then that’s a much better way of thinking about it rather than just jumping in and buying a piece of technology.”
It’s clear that the procurement space is an exciting, but challenging, place to be. What do you think will play a key role in the next 12 months to push the digital conversation further to take procurement to the next level?
SW: “Looking forward, one thing that procurement needs to do and continue to do is attract the best people. Ultimately, people are what makes an organisation, and it is what makes a function successful. I think procurement has often not looked for the right skills in the people that it employs. Traditionally, it’s looked for people with procurement experience and while they are valuable and required, we also need leadership potential. People who think a bit more outside the box and aren’t so process driven. A lot of what procurement has done in previous years has been process driven, so if you’re just limiting your search of people to those that have had procurement experience, you’re inevitably going to end up with a lot of people who are process driven.
“I think being bolder and recruiting people from different backgrounds with different skill sets is the way to go. If procurement can ‘own’ the ESG space, that will help with the younger generation see procurement make a difference. I think that’s one thing that will be key to success going forward.”
Check out the latest issue of CPOstrategy Magazine here.
Paul Farrow, Vice President of Hilton Hotels’ Supply Management, sits down with us to discuss how his organisation’s procurement function has evolved amid disruption on a global scale
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The hospitality industry has endured a rough ride over the past few years.
Following the COVID-19 pandemic which stopped the world in its tracks and now with millions facing a cost-of-living crisis, it’s been a period of unprecedented disruption for those involved in the space and beyond.
But it’s a challenge met head-on by Paul Farrow, Vice President of Supply Management at Hilton Hotels, and his team who have been forced to respond as the world continues to shift before their eyes.
Farrow gives us a closer look into the inner workings of his firm’s procurement function and how he has led the charge during his time with Hilton Hotels.
Could we start with you introducing yourself and talking a little about your role at Hilton Hotels?
Paul Farrow (PF): “I’m the Vice President of Hilton’s Supply Management, or HSM as we call it. I’ve been with Hilton Hotels for 12 and a half years, and my role is to head the supply chain function for our hotels across Europe, the Middle East and Africa.
“Over the past few years, Hilton has grown rapidly and has now got 7,000 hotels in over 125 countries globally. What is really exciting is Hilton Supply Management doesn’t just supply Hilton Hotels and the Hilton Engine because we also now supply our franchisees and competitive flags. While we have 7,000 hotels globally, Hilton Supply Management actually supplies close to 13,000 hotels. That’s an interesting business development for us, and a profit earner too.”
You’re greatly experienced, I bet you’ve seen supply chain management and procurement change a lot in recent years?
PF: “The past two to three years have been tremendously challenging on so many industries but I’d argue that hospitality got hit more than most as a result of the Covid pandemic. Here at Hilton, supply management was really important just to keep the business operational throughout that tough time, but I’m delighted to say we’re fully recovered now.
“Looking back, it was undoubtedly difficult, and you only have to look at the media to see that we’re now going through a period of truly unprecedented inflation. On top of the normal day job, it’s certainly been a very busy time.”
Hospitality must have been under an awful lot of pressure during the pandemic…
PF: “Most of our teams as a business and all functions have worked together far more collaboratively than ever before through the use of technology and things like Microsoft Teams and Zoom. Trying to work remotely as effectively as possible changed the way we all had to think and the way we had to do. Now we’re back in the workplace and in our offices, we’re actually looking to take advantage of that new approach.”
Inflation, rising costs, energy shortages, as well as drives towards a circular economy means it’s quite a challenging time for CSCOs and CPOs right now, isn’t it?
PF: “Those headwinds have caused and created challenges of the like that we’ve not seen before. The war in Ukraine and Russia has meant significant supply chain disruption and supply shortages of some key ingredients and raw materials. China is a significant source of materials and they’re still having real challenges to get their production to keep up with demand.
“All the local and short-term challenges are around energy and fuel pricing, so throughout the supply chain that’s been a major factor to what we’ve had to deal with. On top of that is the labour shortages. We rely heavily throughout the supply chain and within our business to utilise labour from around the world. In my region, particularly from say Eastern Europe as well as other businesses all fighting for a smaller labour pool than we had before. We are fighting with the likes of the supermarkets, Amazon’s, not just other hotel companies to capture the labour pool we need both in our properties but also within our supply chain supplies themselves.
Hilton operates a rather unique procurement function, doesn’t it?
PF: “We trade off the Hilton name because our brand strength is something that we are able to utilise and we’re very proud of, but we’ve also got additional leverage by having that group procurement model.
“We’ve got essentially two clients. We’ve got our managed estate which is when an owner chooses to partner with Hilton, they’re signing a management agreement because they want the benefit and value of the Hilton engine. That could be revenue management, how we manage onboarding clients and customers through advertising, as well as the other support we give in terms of finance, HR, marketing and sales as well as procurement.”
HSM is a profit centre and revenue driver through its group procurement model but how does this work?
PF: “Our secret sauce is our culture. It’s our people and that filters across all of our team members and indeed all of our functions. The key strategic pillars are the same for health and supply management around culture, maximising performance and so on as they are across the overall global business.
“Across our 7,000 plus hotels, the majority are actually franchised hotels because that’s the legacy of what still is the model in the US. When I joined Hilton 12 and a half years ago, the reverse is true where nearly all of our hotels in Europe, Middle East and Africa, and indeed in Asia Pacific, were and are managed. In the Europe, Middle East and Africa regions right now we’re building up close to a 50/50 split between managed, leased and franchised.”
What has pleased you most about the roll-out of the HSM?
PF: “It’s certainly not been easy because we’ve got 70 countries that sit within our region here in EMEA and Hilton’s penetration in those individual countries is very different. We may have 100 hotels in one of those markets and only one or two in specific countries. Our scale and our ability to get logistics solutions is different by market.
“Getting everyone on board to what we want to achieve to our guests and to our owners means we have to pull different levers. We have very effective brand standards. If you’re signing up to Hilton, you’re signing up to delivering against those brand standards that we believe are right for our organisation.”
What kind of feedback have you had from your clients?
PF: “Integrity is in our DNA, and we work very closely with our suppliers who we value as partners. These are long-term relationships, and we work hand in hand because we have to see that they’re successful so that we can be successful – it’s really important to what we do and we constantly look for feedback.
“With our internal and our external customers, we’ll have quarterly business reviews and so we’ll get that feedback through surveys where we are asking them to tell us what we do well and what we could do better. Our partners are now asking what additional value can you do to bring support to our organisation through ESG? So that’s what’s on the table now when it wasn’t before. But it’s not just that – it’s about the security of supply competitiveness, competitiveness of pricing, and a whole bunch of other very important things as well.”
Looking to the future, what’s on the agenda for the next few years?
PF: “We’re out there meeting and greeting people in person and there’s always new opportunities that make things exciting in what we do and how we work. Innovation’s very high on our agenda and we’re very proud of what we do in food and beverage. In non-food categories, it’s about how we support our owners and our hotel general managers to find that competitive edge and do the next big thing ahead of our competitors.”
Anything else important to know?
PF: “One thing we’ve been able to take full advantage of is how we’ve been able to grow our business by bolting on new customers. I think it’s fantastic that our competitors choose to use Hilton Supply Management because they benchmarked what our capabilities are and how competitive we are.
“Another key part of the agenda is environmental, social and governance (ESG) sustainability. Responsible sourcing and everything that sits within that is front and centre of what we do. Within that you’ve got human rights, animal welfare, single use plastics as well as general responsible sourcing like managing food waste. The list is very long, but they’re all very important.”
Check out the latest issue of CPOstrategy Magazine here.
Here are 10 of the most important leadership skills that CEOs need to demonstrate in 2023.
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In today’s world, a CEO needs to be lots of things to different people. The importance of having the leadership skill to being able to lead through unprecedented disruption was highlighted by the COVID-19 pandemic and helped to define what makes a good CEO.
Here are 10 of the most important leadership skills that CEOs need to demonstrate in 2023.
1. Clear communication
Communicating effectively with employees is one of the most vital skills any leader can have. By adopting a transparent mindset, it leaves little room for miscommunication or misunderstandings. But rather than just being eloquent, CEOs should deliver meaningful content too. A CEO needs to be able to communicate the essence of the business strategy and the methodology for achieving it.
2. Strong talent management strategy
People are the most important component of all businesses. CEOs who are able to recruit and retain key employees have a greater chance of increasing productivity and efficiency. After recruiting good people, the key to retaining them is by harnessing a positive work environment that empowers employees to succeed.
3. Decision-making
As a leader, thinking strategically to make effective decisions is vital to the success of an organisation. Making decisions is a key part of leadership as well as having the conviction to stand by decisions or agility to adapt when those decisions don’t have the required outcome. While all decisions might not be favourable, making unpopular but necessary calls are important characteristics of a good leader.
4. Negotiation
Negotiation is a fundamental part of being a CEO. In a top leadership position, almost every business conversation will be a negotiation. Good negotiations are important to an organisation because they will ultimately result in better relationships, both with staff inside the company and externally. An effective leader will also help find the best long-term solution by finding the right balance and offering value where both parties feel like they ‘win’.
5. Creativity and innovation
Being quick-thinking and ready to explore new options are great skills of a CEO. Creative leadership can lead to finding innovative solutions in the face of challenging and changing situations. It means in the midst of disruption, of which it has been increasingly prevalent, leaders can still find answers for their teams. Creative CEOs are those who take risks and empower employees to drop outdated and overused practices to innovate and try new things that could lead to greater efficiency.
6. Agility
Without agility over the past few years, businesses would have failed. CEOs were forced to embrace remote working following the advent of the COVID-19 pandemic whether they liked it or not. Now, faced against a potential recession, these macroeconomic events are unavoidable and have to be managed carefully. Effective leaders will have their fingers on the pulse and ready to respond to changes.
7. Strategic forecasting
Creating a clear path forward is essential to achieving uninterrupted success. The ability to look into the future and identify trends and issues to then react to is vital. Good CEOs are able to plan strategically and make informed decisions to set goals and plan for the future easily.
8. Delegation
CEOs can’t do everything. A leader tends to be pulled in a number of different ways every day and it is impossible to be on top of everything. This means the importance of bringing in a team of people who are trusted and skilled in their respective areas of expertise. Successful CEOs are expert delegators because they recognise the value of teamwork and elevating those around them.
9. Approachability
An approachable CEO who welcomes conversation and is an active listener will help employees feel at ease raising issues or concerns. This approach will help build strong relationships with staff and customers and encourage a healthy culture which is beneficial to employee retention. Leaders with strong, trusting and authentic relationships with their teams know that investing time in building these bonds which makes them more effective as a leader and creates a foundation for success.
10. Growth mindset
If a CEO arms themselves with a growth mindset it allows them to meet challenges head-on and evolve. This shines a light on improving through effort, learning and persistence. As others may back down in the face of adversity and upheaval, successful CEOs will strive to move forward with confidence. Those with a growth mindset are unlikely to be swayed as they have the tools needed to reframe challenges as opportunities to grow.
In McKinsey’s latest report ‘Actions the best CEOs are taking in 2023’, we examine three of the biggest trends on the c-level agenda
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Anyone can sail a ship when things are going well. But it takes a strong, robust and characterful CEO to steer a business through choppy waters and out the other side.
In McKinsey’s latest report ‘Actions the best CEOs are taking in 2023’, the research and advisory firm uncovered which trends are set to have the biggest impact on how CEOs lead their business throughout the year.
McKinsey’s CEO Excellence Survey surveyed 200 of the best corporate CEOs of the past 15 years. This was completed by whittling down a list of all the current and former CEOs of the 1,000 largest public companies during that timeframe. The list was subsequently filtered based on tenure, including only those who had completed at least six years in the role. From there, the CEOs were continuously shortlisted until the best 200 were determined.
Each CEO was asked to identify the top three trends that are set to determine how leaders tackle the future. Here is an insight into those findings.
1. Actions to deal with digital disruption
CEOs are targeting digital trends in three key ways: developing advanced analytics, enhancing cybersecurity and automating work. OpenAI’s launch of ChatGPT has accelerated the demand of companies looking to embrace advanced analytics for a competitive advantage. Improving cybersecurity is another key action for CEOs with the importance of guarding against external threats paramount amid strengthening and more mature cyberattacks. Lastly, automating work is another key priority to scale efficiency and eliminate boring and manual tasks which free up people’s time.
2. Actions to deal with the risk of high inflation and economic downturn
One CEO who is worried about economic uncertainty told McKinsey: “Act early to lower costs and protect the balance sheet so that you are stronger and leaner when the economy begins to turn more favourably.” McKinsey found that companies that outperformed the 2008 financial crisis cut operating costs by 1% before the downturn while the others expanded costs by the same percentage. The best performers reduced their debt by $1 for every $1 of book capital before the downturn. This can be done by reducing operating expenses, redesigning products and services as well as reassessing strategic and economic assumptions.
3. Actions to deal with the escalation of geopolitical risk
According to McKinsey, there are three actions to help manage the escalation of global and national crises. CEOs are targeting building robust compliance capabilities, creating resilience in supplier networks and investing in monitoring and response capabilities. These actions come following the challenges presented by COVID-19, the war in Ukraine and now inflation concerns. Many firms are choosing to build their trade compliance organisations and improve how they screen different customers and companies. While a defensive approach is the way forward for many, some companies see the turbulent times as an opportunity.
What does today’s CEO need to do to accelerate an organisation’s digital transformation journey?
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Digital transformation journeys are no one-size-suits-all. There is no singular way to welcome a new wave of technology into operations.
Since the turn of the century, digitalisation has had an increasingly influential impact on the way CEOs make decisions. Today’s world is full of disruption and potential risk. And with technology growing in complexity it can be challenging to lead such a revolution against a backdrop of economic uncertainty.
Embracing digital
According to KPMG 2022 CEO Outlook, which draws on the perspectives of 1,325 global CEOs across 11 markets, 72% of CEOs agree they have an aggressive digital investment strategy intended to secure first-mover or fast-follower status.
Advancing digitalisation and connectivity across the business is tied (along with attracting and retaining talent) as the top operational priority to achieve growth over the next three years. This digital transformation focus could be driven as a result of increasingly flexible working conditions and greater focus on cybersecurity threats.
However, the prospect of recession is threatening to halt digital transformation in the short-term. KPMG research found that four out of five CEOs note their businesses are pausing or reducing their digital transformation strategies to prepare for the anticipated recession.
This is reinforced further when 70% say they need to be quicker to shift investment to digital opportunities and divest in those areas where they face digital obsolescence.
When a company’s digital transformation ambition is mismatched to its readiness, it is the CEO’s responsibility to close the gap. According to Deloitte, in order to do this successfully, the CEO must assess the current level of organisational readiness for change.
This covers four key pillars that are mixed together to work out an organisation’s overall readiness: leadership, culture, structure and capabilities.
How CEOs can close the gap
Leadership: CEOs need to ensure their c-suite and other key executives are motivated and equipped to execute the vision. CEOs interviewed by Deloitte in a recent study emphasised the importance of the leadership team supporting the transformation vision and having a positive attitude and willingness to transform.
Culture: A large potential barrier to readiness in the organisation is down to culture. Low cultural readiness takes the form of bureaucratic, reactive and risk-averse ways of working that are at against the collaborative, proactive learning mindset needed for ambitious transformation.
Structure: If a company hopes to operate differently, it could mean the need for organising in an alternative way. CEOs will often need to lead the reorganisation of teams, assignment of new roles, revision of incentives, strategies to collapse organisational hierarchies or layers to increase agility.
Capabilities: CEOs need to equip their organisation with four key capabilities to harness digital for a superior capacity for change. These are nimbleness, scalability, stability and optionality which are often enabled or supercharged by digital technologies which are critical factors for competing in an increasingly disrupted world.
For now, one of the CEOs most important roles when steering the ship through disruption is to be ahead of the latest trends and tackle change head-on. By embracing a new digital future that will provide the company with long-lasting benefits, it will help create a brighter and future-proofed firm for years to come even after the CEO is gone.
Expert analysis of the tech trends set to make waves this year
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Digital transformation is a continuing journey of change with no set final destination. This makes predicting tomorrow a challenge when no one has a crystal ball to hand.
After a difficult few years for most businesses following a disruptive pandemic and now battling a cost-of-living crisis, many enterprises are increasingly leveraging new types of technology to gain an edge in a disruptive world.
With this in mind, here are what experts predict for the next 12 months…
1. Process Mining
Sam Attias, Director of Product Marketing at Celonis, expects to see a rise in the adoption of process mining as it evolves to incorporate automation capabilities. He says process mining has traditionally been “a data science done in isolation” which helps companies identify hidden inefficiencies by extracting data and visually representing it.
“It is now evolving to become more prescriptive than descriptive and will empower businesses to simulate new methods and processes in order to estimate success and error rates, as well as recommend actions before issues actually occur,” says Attias. “It will fix inefficiencies in real-time through automation and execution management.”
2. The evolution of social robots
Gabriel Aguiar Noury, Robotics Product Manager at Canonical, anticipates social robots to return this year. After companies such as Sony introduced robots like Poiq, Aguiar Noury believes it “sets the stage” for a new wave of social robots.
“Powered by natural language generation models like GPT-3, robots can create new dialogue systems,” he says. “This will improve the robot’s interactivity with humans, allowing robots to answer any question.
“Social robots will also build narratives and rich personalities, making interaction with users more meaningful. GPT-3 also powers Dall-E, an image generator. Combined, these types of technologies will enable robots not only to tell but show dynamic stories.”
3. The rebirth of new data-powered business applications
Christian Kleinerman, Senior Vice President of Product at Snowflake, says there is the beginning of a “renaissance” in software development. He believes developers will bring their applications to central combined sources of data instead of the “traditional approach” of copying data into applications.
“Every single application category, whether it’s horizontal or specific to an industry vertical, will be reinvented by the emergence of new data-powered applications,” affirms Kleinerman. “This rise of data-powered applications will represent massive opportunities for all different types of developers, whether they’re working on a brand-new idea for an application and a business based on that app, or they’re looking for how to expand their existing software operations.”
4. Application development will become a two-way conversation
Adrien Treuille, Head of Streamlit at Snowflake, believes application development will become a two-way conversation between producers and consumers. It is his belief that the advent of easy-to-use low-code or no-code platforms are already “simplifying the building” and sharing of interactive applications for tech-savvy and business users.
“Based on that foundation, the next emerging shift will be a blurring of the lines between two previously distinct roles — the application producer and the consumer of that software.”
He adds that application development will become a collaborative workflow where consumers can weigh in on the work producers are doing in real-time. “Taking this one step further, we’re heading towards a future where app development platforms have mechanisms to gather app requirements from consumers before the producer has even started creating that software.”
5. The Metaverse
Paul Hardy, EMEA Innovation Officer at ServiceNow, says he expects business leaders to adopt technologies such as the metaverse in 2023. The aim of this is to help cultivate and maintain employee engagement as businesses continue working in hybrid environments, in an increasingly challenging macro environment.
“Given the current economic climate, adoption of the metaverse may be slow, but in the future, a network of 3D virtual worlds will be used to foster meaningful social connections, creating new experiences for employees and reinforcing positive culture within organisations,” he says. “Hybrid work has made employee engagement more challenging, as it can be difficult to communicate when employees are not together in the same room.
“Leaders have begun to see the benefit of hosting traditional training and development sessions using VR and AI-enhanced coaching. In the next few years, we will see more workplaces go a step beyond this, for example, offering employees the chance to earn recognition in the form of tokens they can spend in the real or virtual world, gamifying the experience.”
6. The year of ESG?
Cathy Mauzaize, Vice President, EMEA South, at ServiceNow, believes 2023 could be the year that environmental, social and corporate governance (ESG) is vital to every company’s strategy.
“Failure to engage appropriate investment in ESG strategies could plunge any organisation into a crisis,” she says. “Legislation must be respected and so must the expectations of employees, investors and your ecosystem of partners and customers.
“ESG is not just a tick box, one and done, it’s a new way of business that will see us through 2023 and beyond.”
7. Macro Trends and Redeploying Budgets for Efficiency
Ulrik Nehammer, President, EMEA at ServiceNow, says organisations are facing an incredibly complex and volatile macro environment. Nehammer explains as the world is gripped by soaring inflation, intelligent digital investments can be a huge deflationary force.
“Business leaders are already shifting investment focus to technologies that will deliver outcomes faster,” he says. “Going into 2023, technology will become increasingly central to business success – in fact, 95% of CEOs are already pursuing a digital-first strategy according to IDC’s CEO survey, as digital companies deliver revenue growth far faster than non-digital ones.”
8. Organisations will have adopted a NaaS strategy
David Hughes, Aruba’s Chief Product and Technology Officer, believes that by the end of 2023, 20% of organisations will have adopted a network-as-a-service (NaaS) strategy.
“With tightening economic conditions, IT requires flexibility in how network infrastructure is acquired, deployed, and operated to enable network teams to deliver business outcomes rather than just managing devices,” he says. “Migration to a NaaS framework enables IT to accelerate network modernisation yet stay within budget, IT resource, and schedule constraints.
“In addition, adopting a NaaS strategy will help organisations meet sustainability objectives since leading NaaS suppliers have adopted carbon-neutral and recycling manufacturing strategies.”
9. Think like a seasonal business
According to Patrick Bossman, Product Manager at MariaDB corporation, he anticipates 2023 to be the year that the ability to “scale out on command” is going to be at the fore of companies’ thoughts.
“Organisations will need the infrastructure in place to grow on command and scale back once demand lowers,” he says. “The winners in 2023 will be those who understand that all business is seasonal, and all companies need to be ready for fluctuating demand.”
10. Digital platforms need to adapt to avoid falling victim to subscription fatigue
Demed L’Her, Chief Technology Officer at DigitalRoute, suggests what the subscription market is going to look like in 2023 and how businesses can avoid falling victim to ‘subscription fatigue’. L’Her says there has been a significant drop in demand since the pandemic.
“Insider’s latest research shows that as of August, nearly a third (30%) of people reported cancelling an online subscription service in the past six months,” he reveals. “This is largely due to the rising cost of living experienced globally that is leaving households with reduced budgets for luxuries like digital subscriptions. Despite this, the subscription market is far from dead, with most people retaining some despite tightened budgets.
“However, considering the ongoing economic challenges, businesses need to consider adapting if they are to be retained by customers in the long term. The key to this is ensuring that the product adds value to the life of the customer.”
11. Waking up to browser security
Jonathan Lee, Senior Product Manager at Menlo Security, points to the web browser being the biggest attack surface and suggests the industry is “waking up” to the fact of where people spend the most time.
“Vendors are now looking at ways to add security controls directly inside the browser,” explains Lee. “Traditionally, this was done either as a separate endpoint agent or at the network edge, using a firewall or secure web gateway. The big players, Google and Microsoft, are also in on the act, providing built-in controls inside Chrome and Edge to secure at a browser level rather than the network edge.
“But browser attacks are increasing, with attackers exploiting new and old vulnerabilities, and developing new attack methods like HTML Smuggling. Remote browser isolation is becoming one of the key principles of Zero Trust security where no device or user – not even the browser – can be trusted.”
12. The year of quantum-readiness
Tim Callan, Chief Experience Officer at Sectigo, predicts that 2023 will be the year of quantum-readiness. He believes that as a result of the standardisation of new quantum-safe algorithms expected to be in place by 2024, this year will be a year of action for government bodies, technology vendors, and enterprise IT leaders to prepare for the deployment.
“In 2022, the US National Institute of Standards and Technologies (NIST) selected a set of post-quantum algorithms for the industry to standardise on as we move toward our quantum-safe future,” says Callan.
“In 2023, standards bodies like the IETF and many others must work to incorporate these algorithms into their own guidelines to enable secure functional interoperability across broad sets of software, hardware, and digital services. Providers of these hardware, software, and service products must follow the relevant guidelines as they are developed and begin preparing their technology, manufacturing, delivery, and service models to accommodate updated standards and the new algorithms.”
13. AI: fewer keywords, greater understanding
AI expert Dr Pieter Buteneers, Director of AI and Machine Learning at Sinch, expects artificial intelligence to continue to transition away from keywords and move towards an increased level of understanding.
“Language-agnostic AI, already existent within certain AI and chatbot platforms, will understand hundreds of languages — and even interchange them within a single search or conversation — because it’s not learning language like you or I would,” he says. “This advanced AI instead focuses on meaning, and attaches code to words accordingly, so language is more of a finishing touch than the crux of a conversation or search query.
“Language-agnostic AI will power stronger search results — both from external (the internet) and internal (a company database) sources — and less robotic chatbot conversations, enabling companies to lean on automation to reduce resources and strain on staff and truly trust their AI.”
14. Rise in digital twin technology in the enterprise
John Hill, CEO and Founder of Silico, recognises the growing influence digital twin technology is having in the market. Hill predicts that in the next 20 years, there will be a digital twin of every complex enterprise in the world and anticipates the next generation of decision-makers will routinely use forward-looking simulations and scenario analytics to plan and optimise their business outcomes.
“Digital twin technology is one of the fastest-growing facets of industry 4.0 and while we’re still at the dawn of digital twin technology,” he explains. “Digital twins will have huge implications for unlocking our ability to plan and manage the complex organisations so crucial for our continued economic progress and underpin the next generation of Intelligent Enterprise Automation.”
15. Broader tech security
With an exponential amount of data at companies’ fingertips, Tricentis CEO, Kevin Thompson says the need for investment in secure solutions is paramount.
“The general public has become more aware of the access companies have to their personal data, leading to the impending end of third-party cookies, and other similar restrictions on data sharing,” he explains. “However, security issues still persist. The persisting influx of new data across channels and servers introduces greater risk of infiltration by bad actors, especially for enterprise software organisations that have applications in need of consistent testing and updates. The potential for damage increases as iterations are being made with the expanding attack surface.
“Now, the reality is a matter of when, not if, your organisation will be the target of an attack. To combat this rising security concern, organisations will need to integrate security within the development process from the very beginning. Integrating security and compliance testing at the upfront will greatly reduce risk and prevent disruptions.”
16. Increased cyber resilience
Michael Adams, CISO at Zoom, expects an increased focus on cyber resilience over the next 12 months. “While protecting organisations against cyber threats will always be a core focus area for security programs, we can expect an increased focus on cyber resilience, which expands beyond protection to include recovery and continuity in the event of a cyber incident,” explains Adams.
“It’s not only investing resources in protecting against cyber threats; it’s investing in the people, processes, and technology to mitigate impact and continue operations in the event of a cyber incident.”
17. Ransomware threats
As data leaks become increasingly common place in the industry, companies face a very real threat of ransomware. Michal Salat, Threat Intelligence Director at Avast, believes the time is now for businesses to protect themselves or face recovery fees costing millions of dollars.
“Ransomware attacks themselves are already an individual’s and businesses’ nightmare. This year, we saw cybergangs threatening to publicly publish their targets’ data if a ransom isn’t paid, and we expect this trend to only grow in 2023,” says Salat. “This puts people’s personal memories at risk and poses a double risk for businesses. Both the loss of sensitive files, plus a data breach, can have severe consequences for their business and reputation.”
18. Intensified supply chain attacks
Dirk Schrader, VP of security research at Netwrix, believes supply chain attacks are set to increase in the coming year. “Modern organisations rely on complex supply chains, including small and medium businesses (SMBs) and managed service providers (MSPs),” he says.
“Adversaries will increasingly target these suppliers rather than the larger enterprises knowing that they provide a path into multiple partners and customers. To address this threat, organisations of all sizes, while conducting a risk assessment, need to take into account the vulnerabilities of all third-party software or firmware.”
19. A greater need to manage volatility
Paul Milloy, Business Consultant at Intradiem, stresses the importance of managing volatility in an ever-moving market. Milloy believes bosses can utilise data through automation to foresee potential problems before they become issues.
“No one likes surprises. Whilst Ben Franklin suggested nothing can be said to be certain, except death and taxes, businesses will want to automate as many of their processes as possible to help manage volatility in 2023,” he explains. “Data breeds intelligence, and intelligence breeds insight. Managers can use the data available from workforce automation tools to help them manage peaks and troughs better to avoid unexpected resource bottlenecks.”
20. A human AI co-pilot will still be needed
Artem Kroupenev, VP of Strategy at Augury, predicts that within the next few years, every profession will be enhanced with hybrid intelligence, and have an AI co-pilot which will operate alongside human workers to deliver more accurate and nuanced work at a much faster pace.
“These co-pilots are already being deployed with clear use cases in mind to support specific roles and operational needs, like AI-driven solutions that enable reliability engineers to ensure production uptime, safety and sustainability through predictive maintenance,” he says. “However, in 2023, we will see these co-pilots become more accurate, more trusted and more ingrained across the enterprise.
“Executives will better understand the value of AI co-pilots to make critical business decisions, and as a key competitive differentiator, and will drive faster implementation across their operations. The AI co-pilot technology will be more widespread next year, and trust and acceptance will increase as people see the benefits unfold.”
21. Building the right workplace culture
Harnessing a positive workplace culture is no easy task but in 2023 with remote and hybrid working now the norm, it brings with it new challenges. Tony McCandless, Chief Technology Officer at SS&C Blue Prism, is well aware of the role organisational culture can play in any digital transformation journey.
“Workers are the heart of an organisation, so without their buy in, no digital transformation initiative stands a chance of success,” explains McCandless. “Workers drive home business objectives, and when it comes to digital transformation, they are the ones using, implementing, and sometimes building automations. Curiosity, innovation, and the willingness to take risks are essential ingredients to transformative digitalisation.
“Businesses are increasingly recognising that their workers play an instrumental role in determining whether digitalisation initiatives are successful. Fostering the right work environment will be a key focus point for the year ahead – not only to cultivate buy-in but also to improve talent retention and acquisition, as labor supply issues are predicted to continue into 2023 and beyond.”
22. Cloud cover to soften recession concerns
Amid a cost-of-living crisis and concerns over any potential recession as a result, Daniel Thomasson, VP of Engineering and R&D at Keysight Technologies, says more companies will shift data intensive tasks to the cloud to reduce infrastructure and operational costs.
“Moving applications to the cloud will also help organisations deliver greater data-driven customer experiences,” he affirms. “For example, advanced simulation and test data management capabilities such as real-time feature extraction and encryption will enable use of a secure cloud-based data mesh that will accelerate and deepen customer insights through new algorithms operating on a richer data set. In the year ahead, expect the cloud to be a surprising boom for companies as they navigate economic uncertainty.”
23. IoT devices to scale globally
Dr Raullen Chai, CEO and Co-Founder of IoTeX, recognises a growing trend in the usage of IoT devices worldwide and believes connectivity will increase significantly.
“For decades, Big Tech has monopolised user data, but with the advent of Web3, we will see more and more businesses and smart device makers beginning to integrate blockchain for device connectivity as it enables people to also monetise their data in many different ways, including in marketing data pools, medical research pools and more,” he explains. “We will see a growth in decentralised applications that allow users to earn a modest additional revenue from everyday activities, such as walking, sleeping, riding a bike or taking the bus instead of driving, or driving safely in exchange for rewards.
“Living healthy lifestyles will also become more popular via decentralised applications for smart devices, especially smart watches and other health wearables.”
The digital landscape is changing day by day. Ideas like the metaverse that once seemed a futuristic fantasy are now…
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The digital landscape is changing day by day. Ideas like the metaverse that once seemed a futuristic fantasy are now coming to fruition and embedding themselves into our daily lives. The thinking might be there, but is our technology really ready to go meta? Domains and hosting provider, Fasthosts, spoke to the experts to find out…
How the metaverse works
The metaverse is best defined as a virtual 3D universe which combines many virtual places. It allows users to meet, collaborate, play games and interact in virtual environments. It’s usually viewed and accessed from the outside as a mixture of virtual reality (VR), (think of someone in their front room wearing a headset and frantically waving nunchucks around) and augmented reality (AR), but it’s so much more than this…
These technologies are just the external entry points to the metaverse and provide the visuals which allow users to explore and interact with the environment within the metaverse.
This is the ‘front-end’ if you like, which is also reinforced by artificial intelligence and 3D reconstruction. These additional technologies help to provide realistic objects in environments, computer-controlled actions and also avatars for games and other metaverse projects.
So, what stands in the way of this fantastical 3D universe? Here are the six key challenges:
Technology
The most important piece of technology, on which the metaverse is based, is the blockchain. The blockchain is essentially a chain of blocks that contain specific information. They’re a combination of computers linked to each other instead of a central server which means that the whole network is decentralised. This provides the infrastructure for the development of metaverse projects, storage of data and also allows them the capability to be compatible with Web3. Web3 is an upgraded version of the internet which will allow integration of virtual and augmented reality into people’s everyday lives.
Sounds like a lot, right? And it involves a great deal of tech that is alien to the vast majority of us. So, is technology a barrier to widespread metaverse adoption?
Jonothan Hunt, Senior Creative Technologist at Wunderman Thompson, says the tech just isn’t there. Yet.
“Technology’s readiness for the mass adoption of the metaverse depends on how you define the metaverse, but if we’re talking about the future vision that the big tech players are sharing, then not yet. The infrastructure that powers the internet and our devices isn’t ready for such experiences. The best we have right now in terms of shared/simulated spaces are generally very expensive and powered entirely in the cloud, such as big computers like the Nvidia Omniverse, cloud streaming, or games. These rely heavily on instancing and localised grouping. Consumer hardware, especially XR, is still not ready for casual daily use and still not really democratised.
“The technology for this will look like an evolution of the systems above, meaning more distributed infrastructure, better access and updated hardware. Web3 also presents a challenge in and of itself, and questions remain over to what extent big tech will adopt it going forward.”
Storage
Blockchain is the ‘back-end’, where the magic happens, if you will. It’s this that will be the key to the development and growth of the metaverse. There are a lot of elements that make up the blockchain and reinforce its benefits and uses such as storage capabilities, data security and smart contracts.
Due to its decentralised nature, the blockchain has far more storage capacity than the centralised storage systems we have in place today. With data on the metaverse being stored in exabytes, the blockchain works by making use of unutilised hard disk space across the network, which avoids users within the metaverse running out of storage space worldwide.
In terms that might be a bit more relatable, an exabyte is a billion gigabytes. That’s a huge amount of storage, and that doesn’t just exist in the cloud – it’s got to go somewhere – and physical storage servers mean land is taken up, and energy is used. Hunt says: “How long’s a piece of string? The whole of the metaverse will one day be housed in servers and data centres, but the amount or size needed to house all of this storage will beentirely dependent on just how mass adopted the metaverse becomes. Big corporations in the space are starting to build huge data centres – such as Meta purchasing a $1.1 billion campus in Toledo, Spain to house their new Meta lab and data centre – but the storage space is not the only concern. These energy-guzzlers need to stay cool! And what about people and brands who need reliable web hosting for events, gaming or even just meeting up with pals across the world, all that information – albeit virtual – still needs a place to go.
“The current rising cost of electricity worldwide could cause problems for the growth of data centres, and the housing of the metaverse as a whole. However, without knowing the true size of its adoption, it is extremely difficult to truly determine the needed usage. Could we one day see an entire island devoted to data centre storage? Purely for the purposes of holding the metaverse? It seems a little ‘1984’, but who knows?”
Identity
Although the blockchain provides instantaneous verification of transactions with identity through digital wallets, our physical form will be represented by avatars that visually reflect who we are, and how we want to be seen.
The founder of Saxo Bank and the chairman of the Concordium Foundation, Lars Seier Christensen, argues, “I think that if you use an underlying blockchain-based solution where ID is required at the entry point, it is actually very simple and automatically available for relevant purposes. It is also very secure and transparent, in that it would link any transactions or interactions where ID is required to a trackable record on the blockchain.”
Once identity is established, it is true that it could potentially become easier to assess creditworthiness of parties for purchasing and borrowing in the metaverse due to the digital identity and storage of each individual’s data and transactions on the blockchain. However, although it sounds exciting, there must be considerations into how it could impact privacy, and how this amount of data will be recorded on the blockchain.
Security
There are also huge security benefits to this set up. The decentralised blockchain helps to eradicate third-party involvement and data breaches, such as theft and file manipulation, thanks to its powerful data processing and use of validation nodes. Both of these are responsible for verifying and recording transactions on the blockchain. This will be reassuring to many, given the widespread concerns around data privacy and user protection in the metaverse.
To access the blockchain all we will need is an internet connection and a device, such as a laptop or smartphone, this is what makes it so great as it will be so readily available. However, to support the blockchain, we’re relying on a whole different set of technologies. Akash Kayar, CEO of web3-focused software development company Leeway Hertz, had this to say on the readiness of the current technology available: “The metaverse is not yet completely mature in terms of development. Tech experts are researching strategies and
testing the various technologies to develop ideas that provide the world with more feasible and intriguing metaverse projects.
“Projects like Decentraland, Axie Infinity, and Sandbox are popular contemporary live metaverse projects. People behind these projects made perfect use of notable metaverse technologies, from blockchain and cryptos to NFTs.
“As envisioned by top tech futurists, many new technologies will empower the metaverse in the future, which will support the development of a range of prolific use cases that will improve the ability of the metaverse towards offering real-life functionalities. In a nutshell, the metaverse is expected to bring extreme opportunities for enterprises and common users. Hence, it will shape the digital future.”
Currency & Payments
Whilst it’s only considered legal tender in two countries, cryptocurrency is currently a reality and there is a strong likelihood that it will eventually be mass adopted. However, the metaverse is arguably not yet at the same maturity level, meaning cryptocurrency may have to wait before it can finally fully take off.
Golden Bitcoin symbol and finance graph screen. Horizontal composition with copy space. Focused image.
There is no doubt that cryptocurrency and the metaverse will go hand-in-hand as the former will become the tender of the latter with many of the current metaverse platforms each wielding its native currency. For example Decentraland uses $MANA for payments and purchases. However, with the volatility of crypto currencies and the recent collapse of trading platform FTX indicating security lapses, we may not yet be ready for the switch to decentralised payments.
Energy
Some of the world’s largest data centres can each contain many tens of thousands of IT devices which require more than 100 megawatts of power capacity – this is enough to power around 80,000 U.S. households (U.S. DOE 2020) and is equivalent to $1.35bn running cost per data centre with the cost of a megawatt hour averaging $150.
According to Nitin Parekh of Hitachi Energy, the amount of power which takes to process Bitcoin is higher than you might expect: “Bitcoin consumes around 110 Terawatt Hours per year. This is around 0.5% of global electricity generation. This estimate considers combined computational power used to mine bitcoin and process transactions.” With this estimate, we can calculate that the annual energy cost of Bitcoin is around $16.5bn.
However, some bigger corporations are slowly moving towards renewable energy to power their projects in this space, with Google signing close to $2bn worth of wind and solar investments in order to power its data centres in the future and become greener. Amazon has also followed in their footsteps and have become the world’s largest corporate purchaser of renewable energy.
They may have plenty of time yet to get their green processes in place, with Mark Zuckerberg recently predicting it will take nearly a decade for the metaverse to be created: “I don’t think it’s really going to be huge until the second half of this decade at the earliest.”
About Fasthosts
Fasthosts has been a leading technology provider since 1999, offering secure UK data centres, 24/7 support and a highly successful reseller channel. Fasthosts provides everything web professionals need to power and manage their online space, including domains, web hosting, business-class email, dedicated servers, and a next-generation cloud platform. For more information, head to www.fasthosts.co.uk
John MClure, CISO at Sinclair Group – a diversified media company and America’s leading provider of local sports and news – talks about the evolution of cybersecurity and the cultural shift placing it at the forefront of business change
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This month’s cover story explores how Sinclair Broadcast Group is embracing the evolution of cybersecurity and placing the role of the CISO at the forefront of business transformation.
Welcome to the latest issueof Interface magazine!
Communication, secure and at speed, is a vital component of the transformation journey for both the modern enterprise and its relationship with stakeholders, be they customers or partners. Putting the right building blocks in place to deliver successful change management is at the heart of the inspiring stories in the latest issue of Interface.
Our cover star John McClure progressed from a career in the military and work as a consultant in the intelligence industry to fight a new kind of foe… As CISO for Sinclair Broadcast Group, a diversified media company and America’s leading provider of local sports and news, he talks about the evolution of cybersecurity, the battle to meet the rising velocity and sophistication of cyber-attacks and the cultural shift of the role of CISO placing it at the forefront of business change.
“Sinclair is unique in terms of its different business units and how it operates. It’s my job as CISO leading our cyber team not to be an obstacle for the business; we’re here to help it move faster to keep up with market forces, and to move safely. We’re here to engineer solutions that work for the enterprise but also help us maintain a positive security posture.”
State of Florida: digital government services
We also hear from CIO Jamie Grant who is leading the State of Florida’s Digital Service (FL[DS]) on its charge to transform and modernise the way government is accessed and consumed. He is building a team of talented, goal-oriented and customer-obsessed individuals to drive a digital transformation with innovation at its heart. “Leadership is really about developing the team and investing in the people. And it turns out that when you get their backs, they appreciate it and then you can achieve anything.”
ResultsCX: putting people first
Jamie Vernon, SVP for IT & Infrastructure at AI-powered customer experience solution specialist ResultsCX, discusses what drives customer care in the 21st century, and the part technology has to play.
“We are the custodians of our customers’ customers,” says Vernon. “In this increasingly tenuous relationship with their customers, they trust us. My leadership takes that responsibility very seriously, and charges each of us with doing everything we can to provide a perfect call, or email, or chat, every time, thousands of times a minute, around the clock and around the calendar.”
Jamie Vernon, SVP for IT & Infrastructure at AI-powered customer experience solution specialist ResultsCX, discusses what drives customer care in the 21st century, and the part technology has to play.
“We are the custodians of our customers’ customers,” says Vernon. “In this increasingly tenuous relationship with their customers, they trust us. My leadership takes that responsibility very seriously, and charges each of us with doing everything we can to provide a perfect call, or email, or chat, every time, thousands of times a minute, around the clock and around the calendar.”
Also this month, Sarita Singh, Regional Head & Managing Director for Stripe in Southeast Asia, talks about how the fast-growing payments platform is driving financial inclusion across Asia and supporting SMEs with end-to-end services putting users first, and we get expert advice for the modern CEO from the University of Oxford’s Saïd Business School.
Our cover story this month investigates how Fleur Twohig, Executive Vice President, leading Personalisation & Experimentation across Consumer Data & Engagement Platforms, and her team are executing Wells Fargo’s strategy to promote personalised customer engagement across all consumer banking channels
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This month’s cover story follows Wells Fargo’s journey to deliver personalised customer engagement across all its consumer banking channels.
Welcome to the latest issue of Interface magazine!
Partnerships of all kinds are a key ingredient for organisations intent on achieving their goals… Whether that’s with customers, internal stakeholders or strategic allies across a crowded marketplace, Interface explores the route to success these relationships can help navigate.
Our cover story this month investigates the strategy behind Wells Fargo’s ongoing drive to promote personalised customer engagement across all consumer banking channels.
Fleur Twohig, Executive Vice President, leading Personalisation & Experimentation across the bank’s Consumer Data & Engagement Platforms, explains her commitment to creating a holistic approach to engaging customers in personalised one-to-one conversations that support them on their financial journeys.
“We need to be there for everyone across the spectrum – for both the good and the challenging times. Reaching that goal is a key opportunity for Wells Fargo and I have the pleasure of partnering with our cross-functional teams to help determine the strategic path forward…”
IBM: consolidating growth to drive value
We hear from Kate Woolley, General Manager of IBM Ecosystem, who reveals how the tech leader is making it easier for partners and clients to do business with IBM and succeed. “Honing our corporate strategy around open hybrid cloud and artificial intelligence (AI) and connecting partners to the technical training resources they need to co-create and drive more wins, we are transforming the IBM Ecosystem to be a growth engine for the company and its partners.”
Kate Woolley, IBM
America Televisión: bringing audiences together across platforms
Jose Hernandez, Chief Digital Officer at America Televisión, explains how Peru’s leading TV network is aggregating services to bring audiences together for omni-channel opportunities across its platforms. “Time is the currency with which our audiences pay us, so we need to be constantly improving our offering both through content and user experiences.”
Portland Public Schools: levelling the playing field through technology
Derrick Brown and Don Wolf, tech leaders at Portland Public Schools, talk about modernising the classroom, dismantling systemic racism and the power of teamwork.
Also in this issue, we hear from Lenovo on how high-performance computing (HPC) is driving AI research and report again from London Tech Week where an expert panel examined how tech, fuelled by data, is playing a critical role in solving some of the world’s hardest hitting issues, ranging from supply chain disruptions through to cybersecurity fears.
Conventional robots, like giant industrial robots used in the car industry, are set to reach $14.9bn value this year, up from $12bn in 2018.
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Robotics play a huge role in the manufacturing landscape today. A growing number of businesses use manufacturing robots to automate repetitive tasks, reduce errors, and enable their employees to focus on innovation and efficiency, causing the entire sector’s impressive growth.
According to data presented by AksjeBloggen.com, the global market value of conventional and advanced robotics in the manufacturing industry is expected to continue rising and hit $18.6bn in 2021, a 40% increase in three years.
Market Value Jumped by $5.4B in Three Years
Robots have numerous roles in manufacturing. They are mainly used for high-volume, repetitive processes where their speed and accuracy offer tremendous advantages. Other manufacturing automation solutions include robots used to help people with more complex tasks, like lifting, holding, and moving heavy pieces.
Companies turn to robotics process automation to cut manufacturing costs, solve the shortage of skilled labor and keep their cost advantage in the market.
In 2018, the global market value of conventional and advanced robotics in the manufacturing industry amounted to $13.2bn, revealed the BCG survey. In 2019, this figure rose to $14.8bn and continued growing. Statistics show the market value of manufacturing robots hit $16.6bn in 2020. This figure is expected to jump by $2bn and hit $18.6bn in 2021.
Conventional robots, like giant industrial robots used in the car industry, are set to reach $14.9bn value this year, up from $12bn in 2018.
The market value of advanced manufacturing robots, which have a superior perception, adaptability, and mobility, tripled in the last three years and is expected to hit $3.7bn in 2021. Combined with big data analytics, advanced manufacturing robots allow companies to make intelligent decisions based on real-time data, which leads to lower costs and faster turnaround times.
The BCG survey also showed most manufacturers believe advanced robotic systems will have a massive role in the factory of the future and plan to increase their use. More than 70% of respondents defined robotics as a significant productivity driver in production and logistics.
European and Asian Companies Lead in the Use of Advanced Manufacturing Robots
Analyzed by regions, European and Asian companies lead in the use of advanced robots, while manufacturers from North America lag behind. However, the survey showed 80% of respondents from the US plan to implement advanced robotics in the next few years.
The survey also revealed that manufacturers in emerging markets, especially China and India, are more enthusiastic about using advanced robots than those in industrialized countries. These companies may be looking to automation as a way to overcome a skilled labor shortage and improve their ability to compete in international markets.
Germany had the largest robot density in the manufacturing industry among European countries, with 346 installations per 10,000 employees in 2019. Sweden, Denmark, and Italy followed with 277, 243, and 212 installations per 10,000 employees, respectively.
Statistics also show that companies in the transportation and logistics and technology sector lead in implementing advanced robotics, with 54% and 53% of manufacturers who already use such solutions. The automotive industry and consumer goods sector follow with 49% and 44% share, respectively.
Manufacturers in the engineered products, process, and health care industries lag behind, with 42%, 41%, and 30% of companies that use advanced manufacturing robots. However, around 85% of manufacturers in these sectors plan to start using advanced robotic systems by 2022.
Gurpreet Purewal, Associate Vice President, Business Development, iResearch Services, explores how organisations can overcome the challenges presented by AI in 2021.
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2020 has been a year of tumultuous change and 2021 isn’t set to slow down. Technology has been the saving grace of the waves of turbulence this year, and next year as the use of technology continues to boom, we will see new systems and processes emerge and others join forces to make a bigger impact. From assistive technology to biometrics, ‘agritech’ and the rise in self-driving vehicles, tech acceleration will be here to stay, with COVID-19 seemingly just the catalyst for what’s to come. Of course, the increased use of technology will also bring its challenges, from cybersecurity and white-collar crime to the need to instil trust in not just those investing in the technology, but those using it, and artificial intelligence (AI) will be at the heart of this.
1. Instilling a longer-term vision
New AI and automation innovations have led to additional challenges such as big data requirements for the value of these new technologies to be effectively shown. For future technology to learn from the challenges already faced, a comprehensive technology backbone needs to be built and businesses need to take stock and begin rolling out priority technologies that can be continuously deployed and developed.
Furthermore, organisations must have a longer-term vision of implementation rather than the need for immediacy and short-term gains. Ultimately, these technologies aim to create more intelligence in the business to better serve their customers. As a result, new groups of business stakeholders will be created to implement change, including technologists, business strategists, product specialists and others to cohesively work through these challenges, but these groups will need to be carefully managed to ensure a consistent and coherent approach and long-term vision is achieved.
2. Overcoming the data challenge
AI and automation continue to be at the forefront of business strategy. The biggest challenge, however, is that automation is still in its infancy, in the form of bots, which have limited capabilities without being layered with AI and machine learning. For these to work cohesively, businesses need huge pools of data. AI can only begin to understand trends and nuances by having this data to begin with, which is a real challenge. Only some of the largest organisations with huge data sets have been able to reap the rewards, so other smaller businesses will need to watch closely and learn from the bigger players in order to overcome the data challenge.
3. Controlling compliance and governance
One of the critical challenges of increased AI adoption is technology governance. Businesses are acutely aware that these issues must be addressed but orchestrating such change can lead to huge costs, which can spiral out of control. For example, cloud governance should be high on the agenda; the cloud offers new architecture and platforms for business agility and innovation, but who has ownership once cloud infrastructures are implemented? What is added and what isn’t?
AI and automation can make a huge difference to compliance, data quality and security. The rules of the compliance game are always changing, and technology should enable companies not just to comply with ever-evolving regulatory requirements, but to leverage their data and analytics across the business to show breadth and depth of insight and knowledge of the workings of their business, inside and out.
In the past, companies struggled to get access and oversight over the right data across their business to comply with the vast quantities of MI needed for regulatory reporting. Now they are expected to not only collate the correct data but to be able to analyse it efficiently and effectively for regulatory reporting purposes and strategic business planning. There are no longer the time-honoured excuses of not having enough information, or data gaps from reliance on third parties, for example, so organisations need to ensure they are adhering to regulatory requirements in 2021.
4. Eliminating bias
AI governance is business-critical, not just for regulatory compliance and cybersecurity, but also in diversity and equity. There are fears that AI programming will lead to natural bias based on the type of programmer and the current datasets available and used. For example, most computer scientists are predominantly male and Caucasian, which can lead to conscious/unconscious bias, and datasets can be unrepresentative leading to discriminatory feedback loops.
Gender bias in AI programming has been a hot topic for some years and has come to the fore in 2020 again within wider conversations on diversity. By only having narrow representation within AI programmers, it will lead to their own bias being programmed into systems, which will have huge implications on how AI interprets data, not just now but far into the future. As a result, new roles will emerge to try and prevent these biases and build a more equitable future, alongside new regulations being driven by companies and specialist technology firms.
5. Balancing humans with AI
As AI and automation come into play, workforces fear employee levels will diminish, as roles become redundant. There is also inherent suspicion of AI among consumers and certain business sectors. But this fear is over-estimated, and, according to leading academics and business leaders, unfounded. While technology can take away specific jobs, it also creates them. In responding to change and uncertainty, technology can be a force for good and source of considerable opportunity, leading to, in the longer-term, more jobs for humans with specialist skillsets.
Automation is an example of helping people to do their jobs better, speeding up business processes and taking care of the time-intensive, repetitive tasks that could be completed far quicker by using technology. There remain just as many tasks within the workforce and the wider economy that cannot be automated, where a human being is required.
Businesses need to review and put initiatives in place to upskill and augment workforces. Reflecting this, a survey on the future of work found that 67% of businesses plan to invest in robotic process automation, 68% in machine learning, and 80% investing in perhaps more mainstream business process management software. There is clearly an appetite to invest strongly in this technology, so organisations must work hard to achieve harmony between humans and technology to make the investment successful.
6. Putting customers first
There is growing recognition of the difference AI can make in providing better service and creating more meaningful interactions with customers. Another recent report examining empathy in AI saw 68% of survey respondents declare they trust a human more than AI to approve bank loans. Furthermore, 69% felt they were more likely to tell the truth to a human than AI, yet 48% of those surveyed see the potential for improved customer service and interactions with the use of AI technologies.
2020 has taught us about uncertainty and risk as a catalyst for digital disruption, technological innovation and more human interactions with colleagues and clients, despite face-to-face interaction no longer being an option. 2021 will see continued development across businesses to address the changing world of work and the evolving needs of customers and stakeholders in fast-moving, transitional markets. The firms that look forward, think fast and embrace agility of both technology and strategy, anticipating further challenges and opportunities through better take-up of technology, will reap the benefits.
With virtually all companies looking at AI, what are some of the key risks they need to consider before implementation?
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Today virtually all companies are forced to innovate and many are excited about AI. Yet since implementation cuts across organisational boundaries, shifting to an AI-driven strategy requires new thinking about managing risks, both internally and externally. This blog will cover “the seven sins of enterprise AI strategies”, which are governance issues at the board and executive levels that block companies from moving ahead with AI. by By Jeremy Barnes, Element AI
1- Disowning the AI strategy
This is probably the most important sin. In this case, a CEO and board will say that AI is a priority, but delegate it to a different department or an innovation lab. However, success is not based on whether or not a company uses an innovation lab—it’s whether they are truly invested in it. The bottom line is that the CEO and board need to actively lead an AI strategy.
2- Ignoring the unknowns
This happens when companies say they believe in AI, but don’t reach a level of proficiency where it’s possible to identify, characterise and model the threats that emerge with new advances. Even if it is decided not to go all-in on AI innovation, it’s still important that there is a hypothesis for how to address AI within a company and an early warning system so the decision can be re-evaluated early enough to act. Being a fast follower requires as much organizational preparation and lead time as leadership.
3- Not enabling the culture
The ability to implement AI is about an experimentation mindset. That and an openness to failure need to be adopted across the company. Organisations need to keep in mind that AI doesn’t respect organisational boundaries. Most companies want high-impact, low-risk solutions that could simply lead to optimising, rather than advancing new value streams. It is hard to accept increased risk in exchange for impact but it will come as part of the continuous cultural enablement of an experimental mindset.
4- Starting with the solution
This is the most common sin. It’s important to be able to understand the specific problems you’re trying to solve, because AI is unlikely to be a solution for all of them, and especially not blindly implementing a horizontal AI platform. Have the conversation at board level to ensure that an overarching AI strategy, and not simply quick-fix solutions, is the priority.
5- Lose risk, keep reward
As mentioned in the third sin, it is natural for companies to want to implement AI without any risk. But there is no reward without risk. A vendor motivated to decrease risk will also decrease innovation and ultimately impact by making successes small and failures non-existent. AI creates differentiation only for companies that are willing to learn from both their successes and their failures. A company that doesn’t effectively balance risk in AI will ultimately increase its risk of disruption.
6- Vintage accounting
Attempting to fit AI into traditional financial governance structures causes problems. It doesn’t fit nicely into budget categories and it’s hard to value the output. The link between what you put in and what you get out can be less tangible or predictable, which often makes it harder to square with existing plans or structures. Model the rate of return on AI activities and all data-related activities. This demands that these activities affect profit (not just loss) and assets (not just liabilities).
7- Treating data as a commodity
The final sin concerns data and its treatment as a commodity. Data is fundamental to AI. If data is poorly handled, it can lead to negative impacts on decision-making. Data should be treated as an asset. The stronger, deeper and more accurate the dataset, the better models that you can train and more intelligent insights you can generate. But, at the same time, when personally identifiable information is stored about customers, it can be stolen, risking heavy penalties in some jurisdictions. You need to build towards data from a use case rather than invest blindly in data centralisation projects. So, now you know what not to do. Here are some of the simple things that you can do to move ahead. First, talk to your board about how long it will take to become an AI innovator, modelling it out, rather than simply discussing it conceptually.
Second, prepare for change and put in place monitoring. AI shifts all the time, so you’ll want to regularly check in to adjust and pivot your strategy. It’s important to develop a basic skill set so you can redo planning exercises with your board. Third, model out risks in both action and inaction. But don’t model them in a traditional approach, which is to push risk down to different business units and then compensate those units for reducing risk rather than managing trade-offs. Instead, view those trade-offs in terms of risks and rewards, and start to think about how you are accounting for the assets and liabilities of AI. Ultimately, you want to start to model what is the actual rate of return for all these activities that you are doing. Then benchmark it against what you see in other companies from across the industry, and that will give you a good picture of the current situation and where to go.
Understanding what it isn’t is just as important as understanding what it is, says Jim Logan who has nearly three decades of experience in financial services and technology…
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I’ve been working in the financial services space for close to thirty years now. I’ve seen many trends and technologies emerge. Some take hold, several are just a flash in the pan. Regardless of how long a concept sticks around, one thing remains: Terminology plays a material role in shaping perceptions. In a world where messaging tends to over complicate things, too many acronyms and too many buzzwords all work against what should be the primary objective: clearly illustrating value. I’ve found this to be equally true when it comes to artificial intelligence or ‘AI’.
Generally speaking, the word artificial doesn’t readily call to mind a positive image, does it? By definition, the word “artificial” has listed meanings of, “insincere or affected” and “made by humans as opposed to happening naturally.” It is the second part of this definition I’d like to explore a bit further.
Artificial Intelligence is, in fact, created by humans. And it isn’t a new fad or concept. Many don’t realize that the term was first coined by John McCarthy, Ph.D. and Stanford computer and cognitive scientist, back in 1955. AI has continued to evolve as a material concept, with practical applications across many industries, ever since.
For financial service professionals, particularly those of us involved with fighting financial crime and preventing money laundering, AI can have tremendous impact and practical application. Before we dive a bit deeper, I feel it’s important to first understand what AI isn’t.
AI is not intended to simply be a digital worker, certainly not within financial services and fighting financial crime. Yes, AI can automate various functions. We’re all familiar with the concept of ‘bots’ and virtual assistants. However, those are rudimentary examples of robotic process automation. True AI is human led and a continuous, instantaneous learning process that drives tangible value. AI is not merely a play to cut costs or replace human capital. Rather, AI enhances the bottom line by keeping compliance staff costs flat in the immediate term and enables our human experts to more appropriately manage their time, by focusing talent on investigations that matter the most.
One of the most valuable aspects of AI, in the context of anti money laundering and compliance, is the speed by which it can be deployed. We’re talking about time to market and time to value in a matter of weeks. Not months, not multiple quarters – simply weeks. But I don’t mean a generic, black box concept. I’m specifically referring to a highly precise, tailored AI solution that has extensive proof points and, more importantly, far-reaching global regulatory approval.
AI shouldn’t simply be an extension of legacy rules-based routines, nor a way to further automate the process of scoring or risk weighted alert suppression. That simply dilutes the true value of AI, and does not maximize the cost and efficiency benefits.
The cost of compliance continues to grow at a staggering pace, particularly for financial institutions and insurance companies. Equally of concern, the impact of fines for non-compliance has also skyrocketed in the last decade. Specifically to the tune of $8.4 billion last year across North America alone.
What if you could literally solve every single name screen, sanction, and transaction alert? What if you could achieve this without sacrificing any aspect of control and security? What if you could increase the throughput, efficiency and accuracy of your compliance operations without adding a single dollar of staff expense to your budget?
Let’s stop talking in terms of what if and have a meaningful conversation regarding how. I’m helping clients achieve all of these measures today and that is from a perspective proven in production. Here at Silent Eight we’re a team founded by engineers and data scientists, solving real world challenges in the anti money laundering and financial compliance market.
Artificial Intelligence isn’t scary…it isn’t a black box…and it isn’t the futuristic world of tomorrow – it is the here and now, and it’s battle tried and tested.
Temenos, the banking software company, partners with Microsoft to offer AI-driven Financial Crime Mitigation solution to help banks combat surge cybercrime during Covid-19 outbreak.
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Temenos, the banking software company, announced today a joint effort with Microsoft to enable access to its AI-powered, Financial Crime Mitigation (FCM) SaaS solution to allow banks to protect both their customers and their organization from financial crime increase during the pandemic, particularly as banks have moved to remote working to protect their staff. Temenos AI-powered, Financial Crime Mitigation SaaS solution based on Microsoft’s fast, scalable and secure Azure cloud platform can be deployed within weeks.
Temenos and Microsoft are opening up access to banks for a 14-day trial, available until 30 of June. As part of the collaboration with Microsoft, Temenos is offering system access and online tutorials for users to familiarize themselves with navigation of the system and learn how it can support them in a revised operating landscape. Temenos unveiled the open access initiative of its FCM software at its virtual event Temenos Community Forum Online, 29-30 April.
Temenos FCM provides enterprise-wide financial crime protection for a highly regulated and fast-changing environment. It allows banks’ operators to respond to alerts and collaborate with team members while working remotely. Throughout the Covid-19 crisis, Temenos customers from Tier 1 banks to regional banks and neobanks have continued to benefit from Temenos FCM’s comprehensive coverage regardless of the fact that their teams are working remotely.
Financial regulators worldwide and organizations such as the European Central Bank are warning that the Covid-19 pandemic may result in an increase in financial crime and other misconduct due to market disruptions, reduced staff, and other factors, as has been the case during past global crises. Opportunistic fraudsters and criminals are adapting their methods of targeting people and countries in distress as new threat vectors open up.
The Financial Actions Task Force (FATF), the global standard setter for combating money laundering and terrorism financing, warns businesses to remain vigilant for emerging money laundering and terrorist financing risks as criminals may seek to exploit gaps and weaknesses in Anti-Money Laundering/Combating the Financing of Terrorism (AML/CFT) systems under the assumption that resources are focused elsewhere. Fraudsters have already been very quick to adapt well-known fraud schemes to target individual citizens, businesses and public organizations. These include various types of adapted versions of telephone fraud schemes, supply scams and decontamination scams.
Jean-Michel Hilsenkopf, Chief Operating Officer, Temenos, said:“We are proud to be able to offer our cloud-native and AI technology to support banks in the fight against financial crime, which has increased as a result of the pandemic. As a strategic global banking software partner of Microsoft, we are pleased to join efforts to deliver Temenos Financial Crime Mitigation as SaaS on Microsoft Azure’s resilient, secure and proven cloud platform. We are committed to providing robust and up-to-date sanction screening, AML, KYC and fraud management protection combined with powerful AI-driven transaction monitoring and sanction screening to help banks worldwide.”
Marianne Janik, Country General Manager, Microsoft Switzerland, said: “We have been pioneering with Temenos in the cloud for a decade. We are proud to join forces to help banks use the power of Temenos’ market-leading Financial Crime Mitigation solution based on our secure, scalable and resilient global Azure cloud platform to combat financial crime surge due to Covid-19.”
More than 200 banks use Temenos FCM SaaS solution, which covers watch-list screening, anti-money laundering, fraud prevention – suspicious activity prevention – and KYC, delivering industry-leading levels of detection and false positives of under 2% vs industry average of 7% and above. Temenos FCM can be deployed as a standalone, or integrated into any banking or payments platform including cloud-native, cloud-agnostic Temenos Transact and Temenos Infinity. It provides unrivalled levels of detection and resilience against financial crime and Total Cost of Ownership (TCO) savings of more than 50%. Temenos FCM provides banks with the next generation of AI-driven FCM capabilities that can run on any public cloud, as a service or on premise.
The global developer of artificial intelligence solutions is releasing a free search platform to help clinical and scientific researchers find answers and patterns in research papers
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Information on COVID-19 is evolving fast and this AI-powered platform leverages a semantic search model that will allow users to quickly connect disparate information. The platform can execute searches based on specific inquiries, along with critical paragraphs copied from a relevant paper. Unlike keyword searches, the queries do not need to be specifically structured, and actually perform better in longer form. This initial version is configured to work with the COVID-19 Open Research Dataset (CORD-19) corpus. Element AI is looking for users and organizations from various groups to test the platform and suggest other data sets and features that could best fit their needs.
The group’s Element AI is looking to work with include:
Clinical researchers who need to incorporate many phenomena to make a rich model of the pandemic and its impacts.
Government, Public Safety and Public Health authorities looking to find best practices across different countries.
Pharmaceutical companies working on new therapies or vaccine trials, as well as identifying existing therapies that could provide immediate help.
-Scientific researchers and data scientists who are working on novel ways to connect research across the body of knowledge already available for COVID-19.
“Research data and reports are being published at an unprecedented pace as organizations scale up their efforts to respond to COVID-19. We want to contribute, and this free platform is our way to help the community locate and gather knowledge to find answers and patterns,” said Jean-François (JF) Gagné, CEO and Co-founder of Element AI. “We encourage the scientific and healthcare community to use this free platform and engage with our team to quickly ramp up and collaboratively meet the needs of the people working to slow down and contain COVID-19. We hope that their feedback and collaboration will help us quickly add features and datasets on top of what we already have made available” added Gagné.
The COVID-19 platform leverages technology from the Element AI Knowledge Scout product, which uses natural language techniques to tap into structured and unstructured sources of information. The first version will be progressively updated in coming weeks as additional datasets emerge. The site can be accessed at: https://www.elementai.com/covid-research.
Mauro Guillén Zandman, Professor of International Management, The Wharton School, University of Pennsylvania, USA Srikar Reddy, Managing Director and Chief…
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Mauro Guillén Zandman, Professor of International Management, The Wharton School, University of Pennsylvania, USA
Srikar Reddy, Managing Director and Chief Executive Officer, Sonata Software Limited and Sonata Information Technology Limited
Artificial intelligence (AI) relies on big data and machine learning for myriad applications, from autonomous vehicles to algorithmic trading, and from clinical decision support systems to data mining. The availability of large amounts of data is essential to the development of AI. But the scandal over the use of personal and social data by Facebook and Cambridge Analytica has brought ethical considerations to the fore. And it’s just the beginning. As AI applications require ever greater amounts of data to help machines learn and perform tasks hitherto reserved for humans, companies are facing increasing public scrutiny, at least in some parts of the world. Tesla and Uber have scaled down their efforts to develop autonomous vehicles in the wake of widely reported accidents. How do we ensure the ethical and responsible use of AI? How do we bring more awareness about such responsibility, in the absence of a global standard on AI?
The ethical standards for assessing AI and its associated technologies are still in their infancy. Companies need to initiate internal discussion as well as external debate with their key stakeholders about how to avoid being caught up in difficult situations.
Consider the difference between deontological and teleological ethical standards. The former focuses on the intention and the means, while the latter on the ends and outcomes. For instance, in the case of autonomous vehicles, the end of an error-free transportation system that is also efficient and friendly towards the environment might be enough to justify large-scale data collection about driving under different conditions and also, experimentation based on AI applications.
By contrast, clinical interventions and especially medical trials are hard to justify on teleological grounds. Given the horrific history of medical experimentation on unsuspecting human subjects, companies and AI researchers alike would be wise to employ a deontological approach that judges the ethics of their activities on the basis of the intention and the means rather than the ends.
Another useful yardstick is the so-called golden rule of ethics, which invites you to treat others in the way you would like to be treated. The difficulty in applying this principle to the burgeoning field of AI lies in the gulf separating the billions of people whose data are being accumulated and analyzed from the billions of potential beneficiaries. The data simply aggregates in ways that make the direct application of the golden rule largely irrelevant.
Consider one last set of ethical standards: cultural relativism versus universalism. The former invites us to evaluate practices through the lens of the values and norms of a given culture, while the latter urges everyone to live up to a mutually agreed standard. This comparison helps explain, for example, the current clash between the European conception of data privacy and the American one, which is shaping the global competitive landscape for companies such as Google and Facebook, among many others. Emerging markets such as China and India have for years proposed to let cultural relativism be the guiding principle, as they feel it gives them an edge, especially by avoiding unnecessary regulations that might slow their development as technological powerhouses.
Ethical standards are likely to become as important at shaping global competition as technological standards have been since the 1980s. Given the stakes and the thirst for data that AI involves, it will likely require companies to ask very tough questions as to every detail of what they do to get ahead. In the course of the work we are doing with our global clients, we are looking at the role of ethics in implementing AI. The way industry and society addresses these issues will be crucial to the adoption of AI in the digital world.
However, for AI to deliver on its promise, it will require predictability and trust. These two are interrelated. Predictable treatment of the complex issues that AI throws up, such as accountability and permitted uses of data, will encourage investment in and use of AI. Similarly, progress with AI requires consumers to trust the technology, its impact on them, and how it uses their data. Predictable and transparent treatment facilitates this trust.
Intelligent machines are enabling high-level cognitive processes such as thinking, perceiving, learning, problem-solving and decision-making. AI presents opportunities to complement and supplement human intelligence and enrich the way industry and governments operate.
However, the possibility of creating cognitive machines with AI raises multiple ethical issues that need careful consideration. What are the implications of a cognitive machine making independent decisions? Should it even be allowed? How do we hold them accountable for outcomes? Do we need to control, regulate and monitor their learning?
A robust legal framework will be needed to deal with those issues too complex or fast-changing to be addressed adequately by legislation. But the political and legal process alone will not be enough. For trust to flourish, an ethical code will be equally important.
The government should encourage discussion around the ethics of AI, and ensure all relevant parties are involved. Bringing together the private sector, consumer groups and academia would allow the development of an ethical code that keeps up with technological, social and political developments.
Government efforts should be collaborative with existing efforts to research and discuss ethics in AI. There are many such initiatives which could be encouraged, including at the Alan Turing Institute, the Leverhulme Centre for the Future of Intelligence, the World Economic Forum Centre for the Fourth Industrial Revolution, the Royal Society, and the Partnership on Artificial Intelligence to Benefit People and Society.
But these opportunities come with associated ethical challenges:
Decision-making and liability: As AI use increases, it will become more difficult to apportion responsibility for decisions. If mistakes are made which cause harm, who should bear the risk?
Transparency: When complex machine learning systems are used to make significant decisions, it may be difficult to unpick the causes behind a specific course of action. Clear explanations for machine reasoning are necessary to determine accountability.
Bias: Machine learning systems can entrench existing bias in decision-making systems. Care must be taken to ensure that AI evolves to be non-discriminatory.
Human values: Without programming, AI systems have no default values or “common sense”. The British Standards Institute BS 8611 standard on the “ethical design and application of robots and robotic systems” provides some useful guidance: “Robots should not be designed solely or primarily to kill or harm humans. Humans, not robots, are the responsible agents; it should be possible to find out who is responsible for any robot and its behaviour.”
Data protection and IP: The potential of AI is rooted in access to large data sets. What happens when an AI system is trained on one data set, then applies learnings to a new data set?
Responsible AI ensures attention to moral principles and values, to ensure that fundamental human ethics are not compromised. There have been several recent allegations of businesses exploiting AI unethically. However, Amazon, Google, Facebook, IBM and Microsoft have established a non-profit partnership to formulate best practices on artificial intelligence technologies, advance the public’s understanding, and to serve as a platform about artificial intelligence.
Peltarion, leading AI innovator and creator of an operational deep learning platform, today announced the findings of a survey of…
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Peltarion, leading AI innovator and creator of an operational deep learning platform, today announced the findings of a survey of AI decision-makers examining what they see as the impact of the skills shortage, and suggestions on how to overcome it. The research, ‘AI Decision-Makers Report: The human factor behind deep learning’, presents the findings of a survey of 350 IT leaders in the UK and Nordics with direct responsibility for shepherding AI at companies with more than 1,000 employees.
The
report finds that many AI decision-makers are concerned about the business
impact of the deep learning skills shortage. 84% of respondents said their
company leaders worry about the business risks of not investing in deep
learning, with 83% saying that a lack of deep learning skills is already
impacting their ability to compete in the market. These companies are exclusively
focusing on recruiting data scientists (71% of AI decision-makers are actively
recruiting to plug the deep learning skills gap), and this is already impacting
their ability to progress with AI projects:
Almost half (49%) say the skills shortage is causing delays to projects
44% believe the need for specialist skills is a major barrier to further investment in deep learning
However, almost half (45%) say they are struggling to hire because they don’t have a mature AI program already in place
“This
report shows that companies can’t afford to wait for data science talent to
come to them to progress their AI projects. The fact is, many organisations are
already starting to lose their competitive edge by waiting for specialised data
scientists. The current approach, which relies on hiring an isolated team of
data scientists to work on deep learning projects, is delaying projects and
putting strain on the talent companies do have,” explains Luka Crnkovic-Friis,
Co-Founder and CEO at Peltarion. “In order to solve the deep learning skills
gap, we need to make use of transferrable talent that can be found right under
companies’ noses. Deep learning will only reach its true potential if we get
more people from different areas of the business using it, taking pressure off
data scientists and allowing projects to progress.”
Less
than half (48%) of respondents said they currently employ data scientists who
can create deep learning models, compared to 94% that have data scientists who
can create other machine learning models. This shortage is having a direct
impact on teams: 93% of AI decision-makers say their data scientists are
over-worked to some extent because they believe there is no one else who can
share the workload. However, with the right tools, others can make a serious
impact on AI projects.
“Organisations
need to move projects forward by bringing on existing domain experts and
investing in tools that will help them input into AI projects. This will reduce
the strain on data scientists and lower deep learning’s barrier to
entry,” concludes Crnkovic-Friis. “We need to make deep learning more
affordable and accessible to all by reducing its complexity. By
operationalising deep learning to make it more scalable, affordable and
understandable, organisations can put themselves on the fast track and use deep
learning to optimise processes, create new products and add direct value to the
business.”
AI is no longer science-fiction writers dream, it’s being implemented in industries all over the world. We look at 5…
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AI is no longer science-fiction writers dream, it’s being implemented in industries all over the world. We look at 5 examples of how AI is revolutionising the retail experience Written by: Dale Benton
Marks and
Spencer
In early 2019, M&S announced a new
Technology Transformation Program, one that will allow M&S to become a
digital-first business and deliver key improvements in customer experience. As
part of this transformation, M&S has partnered with Microsoft to investigate
and test the capabilities of technology and artificial intelligence in a retail
environment. M&S will look to integrate machine learning, computer vision
and AI across every endpoint – both in its stores and behind the scenes. Every
surface, screen and scanner in its stores will create data – and enable
employees to act upon it. Every M&S store worldwide will be able to track,
manage and replenish stock levels in real time – and deal with unexpected
events.
The John Lewis Partnership is currently
partaking in a three-year trial, deploying robots to one of its farms, which
grows produce for its Waitrose & Partners brand. The robots, named Tom, Dick and Harry, are delivered
in partnership with the Small Robot Company. Each will be equipped with a
camera and AI technology to gather topographical data, while autonomously
obtaining accurate, plant-by-plant data in order to enable higher farming
efficiency. The data will also be used
to develop further machine learning capabilities. The trial will also provide
the John Lewis Partnership’s Room Y innovation team with valuable insight to
support innovation and inform how robotics and Artificial Intelligence (AI)
could be used further in other areas of the business.
One of the biggest retail companies in the world has been piloting and implementing artificial intelligence solutions across its stores for a number of years. As part of a technology program, called Missed Scan Detection, Walmart has deployed AI-equipped cameras in more than 1,000 of its stores. These cameras, developed in part with Everseen, tracks and analyses activities at both self-checkout registers and those manned by Walmart employees. If an item isn’t scanned at checkout, the cameras will detect the and notify a checkout attendant of the problem. The AI technology allows Walmart to monitor its inventory product quantities, but also significantly reduce theft across its stores.
Amazon Go represents a whole n era of shipping.
The concept is simple, walk into an Amazon Go store, pick up whatever you want
and walk back out. The idea is to create
a “Just Walk Out” experience. Described as the “most advanced shopping
technology”, customers simply download the Amazon Go app. Powerful machine
learning and AI technology automatically detects when products are taken from
or returned to the shelves, keeping track of them all in a virtual cart. Once
customers leave, Amazon will collate all of the data and produce a receipt and
charge the customer’s Amazon account.
One of the UK’s largest food retailers with more
than 120,000 colleagues in 494 stores serving over 11 million customers every
week, Morrisons turned its attention to AI with JDA Software. Looking to vastly
improve the customer experience, Morrisons looked at reducing queues at
checkouts, and improving on-shelf availability. Morrisons
invested in Blue Yonder – a Demand Forecast & Replenishment solution from JDA,
which uses Artificial Intelligence (AI) technology to improve demand planning
and reinvigorate replenishment based on customer behaviour in every store. Over
a 12-month period, Morrisons was able to generate up to 30% reduction in shelf
gaps and a 2-3 day reduction in stockholding in-store. AI technology has also
enabled Morrisons to close the execution gap, optimizing availability while
reducing wastage, enhancing shelf presentation and meeting stockholding
targets.
By Craig Summers, Managing Director, Manhattan Associates Customer experience can be make or break for retailers. In fact, recent research…
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By Craig Summers, Managing Director, Manhattan Associates
Customer experience can be make or break for retailers. In fact, recent research shows that flawed customer experiences could be costing British retailers up to £102 billion in lost sales each year. This shouldn’t be news to retailers; the modern consumer demands a connected, consistent experience that is personalised to them, whether it’s online or instore. The same research found that running out of stock in-store was the biggest contributor to lost revenue, with 79 per cent of consumers saying they would not return to make a purchase if they found their desired item was out of stock. This frustration is only amplified if an out of stock product is marketed to the consumer.
Personalisation isn’t anything new but if the basics aren’t right, retailers risk not delivering on customer experience. Many retailers still aren’t getting it right – and, explains Craig Summers, Managing Director, Manhattan Associates, inept personalisation is affecting the bottom line.
Misplaced Personalisation
The way in which retailers can engage with customers has changed radically over the past decade, from social media onwards. Add in the compelling appealing of Artificial Intelligence (AI) and the promise of incredibly accurate and timely promotional offers, and personalisation has become a foundation of any retail strategy. Yet while the marketing activity is becoming ever more sophisticated, personalisation cannot be delivered by marketing alone.
Without integrating marketing activity to the core operation, retailers risk repelling rather than engaging customers. Product offers that are out of stock in the customer’s size. Promotions not on offer at the local store. Incentives to buy an item the customer has already purchased – not a problem for a standard food or household item, incredibly annoying if it’s an expensive mountain bike or cashmere jumper. Customers are becoming increasingly familiar with ostensibly personalised offers that fail to deliver a great experience.
What is the thinking behind a promotion that cannot be purchased by the customer? Why set such high expectations when they cannot be met? Enticing a customer to click through an emailed offer may be the measure of marketing success – but when that customer is unable to make a purchase because the desired item is not available in his or her size, that is at least one lost sale and a bottom line retail failure.
Complete Experience
Are retailers listening to what their customers want from personalisation? Great personalised offers will not deliver any value if they are not linked to the rest of the business. Smart technologies, such as AI, without any doubt have a role to play in delivering personalisation – but they are not the foundation. The foundation is getting the basics right. It is ensuring that when a customer wants to buy a product – online or instore – it is available. It is about providing Store Associates with the ability to track stock anywhere in the supply chain, reserve it for a customer to try on instore or have it sent direct to their destination of choice. It is about combining stock availability information with customer insight to make intelligent suggestions, both instore and via marketing promotions.
Bottom line success is, essentially, about the quality of the interaction. And that means considering not just the accuracy of the promotional offer but the complete customer experience. What is achievable today? What can be done well? If a product is being promoted to an individual, is it available in the right size? Is it available locally, or only in flagship outlets? It is these disconnected experiences that are fundamentally undermining customer experience and brand value.
Conclusion
The future of customer personalisation is incredibly exciting. AI promises the ability to predict a customer’s desires before the customer. Fabulous. But only fabulous if that product is available to buy, at a time and place to suit that individual. Right now personalisation is about the retailer; it is about being clever with promotions. It needs to be about the customer; it needs to be about delivering the quality of experience that drives sales.
Retailers need to go back to basics: use technology to recreate the ‘corner shop model’ of the past, at scale. By creating a truly immersive experience for their customers, retailers can find a way to make personalisation profitable again.
The uptake of artificial intelligence by industry will drastically change the UK job market in the coming years – with…
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The uptake of artificial intelligence by industry will drastically change the UK job market in the coming years – with 133 million new jobs expected to be created globally.
In the UK alone, up to a third of jobs will be automated or likely to change as a result of the emergence of AI – impacting 10.5 million workers.
Ollie Sexton, Principal at Robert Walters comments:
“As businesses become ever more reliant on AI, there is an increasing amount of pressure on the processes of data capture and integration. As a result, we have seen an unprecedented number of roles being created with data skill-set at their core.
“Our job force cannot afford to not get to grips with data and digitalisation. Since 2015 the volume of data created worldwide has more than doubled – increasing (on average) by 28% year-on-year.
“Now is the perfect time to start honing UK talent for the next generation of AI-influenced jobs. If you look at the statistics in this report we can see that demand is already rife, what we are at risk of is a shortage of talent and skills.”
Demand for Data Professionals
IT professionals dedicated to data management appear to be the fastest growing area within large or global entities, with volumes increasing ten-fold in three years – an increase in vacancies of 160% since 2015.
More generally speaking, data roles across the board have increased by 80% since 2015 – with key areas of growth including data scientists and engineers.
What has been the most interesting to see is the emergence of data scientist as a mainstream profession – with job vacancies increasing by a staggering 110% year-on-year. The same trend can be seen with data engineers, averaging 86% year-on-year job growth.
Professional Services Hiring Rapidly
The rise of cybercrime has resulted in professional services – particularly within banking and financial services – hiring aggressively for information security professionals since 2016, however since then volumes have held steady.
Within professional services, vacancies for data analysts (+19.5%), data manager (+64.2%), data scientist (+28.8), and data engineer (+62%) have all increased year-on-year.
Top Industries Investing in AI
Agriculture
Business Support
Customer Experience
Energy
Healthcare
Intellectual Property
IT Service Management
Manufacturing
Technical Support
Retail
Software Development
Tom Chambers, Manager – Advanced Analytics and Engineering at Robert Walters comments:
“The uptake of AI across multiple industries is bringing about rapid change, but with that opportunity.
“Particularly, we are seeing retail, professional services and technology industries’ strive to develop digital products and services that are digitally engaging, secure and instantaneous for the customer – leading to huge waves of recruitment of professionals who are skilled in implementing, monitoring and gaining the desired output from facial recognition, check-out free retail and computer vision, among other automation technologies.
“Similarly, experimental AI is making huge breakthroughs in the healthcare industry, with the power to replace the need for human, expert diagnoses.
“What we are seeing is from those businesses that are prepared to invest heavily in AI and data analytics, is they are already outperforming their competitors – and so demand for talent in this area shows no signs of wavering.”
In a world awash with a seemingly never-ending list of technology buzzwords such as automation, machine learning and Artificial Intelligence…
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In a world awash with a seemingly never-ending list of technology buzzwords such as automation, machine learning and Artificial Intelligence (AI) to name a few, AI is one such technology that is moving away from simple hype and stepping closer to reality in procurement.
Here, CPOstrategy looks at 5 ways in which AI is being utilised in procurement…
Procurement, by its very nature, is tasked with handling huge quantities of spend and with spend comes spend data. Often described by leading CPOs as a repetitive task, understanding and sorting that spend data is now being achieved through the implementation of AI.
Through the use of AI, procurement teams can remove human error, increase efficiency and realise greater value from spend data.
Chatbots
One of the biggest ways in which AI is being implemented around the world is in the customer interaction space. In telcos, for example, customer support can now be handled via a highly developed AI chatbot that uses legacy data and context to provide real-time, and unique, solutions for customers.
In procurement, chatbots follow a similar path for both internal and external customers. With tailored and context-aware interactions, chatbots create an omni-channel user experience for all stakeholders in the procurement ecosystem.
Supplier risk identification
Procurement and risk go hand in hand and one of the biggest risks is identifying and working with the right partner. Working in partnerships, which ultimately proves to be a failure, can be extremely costly and so AI is now being used to reduce the risk of failure.
Machine Learning technology, powered by AI, captures and analyses large quantities of supplier data, including their spend patterns and any contract issues that have emerged in previous partnerships, and creates a clearer picture of a supplier in order for the procurement teams to be able to identify whether this particular partner is right for them – without spending a penny.
Benchmarking efficiency
Benchmarking is key to any organisation’s ambition to measure and continuously improve its processes, procedures and policies. In procurement, organisations such as CIPS are used as examples of best practice in which procurement functions all over the world can benchmark against and identify any gaps.
Similar to supplier risk identification, AI can be implemented within ERP systems to analyse the entirety of data that passes through procurement and present this key data in easy to digest formats.
Examples include data classification, cluster analysis and semantic data management to help identify untapped potential or outliers in which procurement teams can improve their processes.
Purchase order processing/Approving purchasing
Procurement has evolved from its traditional role as simply managing spend into a strategic driver for a number of organisations all around the world.
As the role of the CPO has changed, technology such as AI has been implemented to free up their time from the menial tasks (such as PO processing and approving purchases), allowing them to spend more time in areas of growth.
AI software can be used to automatically review POs and match them to Goods Receipt Notes as well as combining with Robotics Process Automation (RPA) to capture, match and approve purchases through the use of contextual data. This contextual data allows AI to identify and make decisions based on past behaviour.
By Robert Douglas, Europe Planning Director at Adaptive Insights, a Workday company Now, more than ever, agility is the currency…
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By Robert Douglas, Europe Planning Director at Adaptive Insights, a Workday company
Now,
more than ever, agility is the currency of success. And while agility may be
about responding intelligently to the changing nature of the marketplace, those
responses must be rooted in a plan. Today, many organizations leverage newer
technologies in the cloud for planning, having moved away from manual
spreadsheets. And while the cloud offers greater collaboration and the ability
to easily combine both historical and real-time data, it’s just the beginning. Digital
transformation is changing and will continue to change the definition of best
practice planning in organisations. As such, the next step for business planning
revolves around two key areas—advancements in AI and machine learning, and
increased automation.
The power of ‘what if’
What-if scenarios are already incredibly
powerful for strategic decision-makers. Organisations can model different
versions of the future based on historical information and predictive analytics
before choosing the best path forward. Consolidating executional data within
organisations is the first step in capitalising on future AI opportunities. However,
there is a lot more to come. In fact, compared with what AI is going to make
possible, scenario planning is still in its infancy.
Today’s scenario planning is a good proof of
concept, but as long as humans are driving the creative process—it relies on
people to ask the right questions of the right data—what-if planning is going
to be constrained by available resources. The most advanced decision-making
today is typically supported by a few best-estimate scenarios—maybe four or
five at most. However, in truth, there are many more possible futures to
potentially prepare for, and what looks like best practice now is going to seem
vastly limited in scope before too long.
As the volume and variety of available data
grows, and access to that data gets easier, AI and machine learning algorithms
will make it possible to drill down, consolidate, and leverage incredibly
granular information at the highest levels.
AI and machine
learning use cases
To consider how these AI and machine learning
algorithms will work, let’s look at a use case of a CEO aiming to achieve a 40
percent growth target over a two-year period and wants to model what that looks
like to present at the annual executive offsite. AI and machine
learning-enabled planning could help to quickly and automatically find the
optimal growth path, while accommodating any conditions and assumptions on the fly.
Essentially, the planning system could measure
historical performance and recommend a market segment mix strategy, along with
the associated budget increases in the specific marketing and sales activities
needed to support it. If they then decide they need to cap growth in sales to
smaller businesses in order to also expand into enterprises and international
markets—while also maintaining expenses at a certain increase—an alternative,
optimised model could be quickly created without any manual lifting.
A future with machine
learning
The future of business planning is not just
about thinking bigger—it is about making better decisions and operationalising
them faster. That’s where machine learning comes in. Increased automation,
driven by algorithms, is going to blur the boundaries between planning,
execution, and analysis until planning cycle times have all but evaporated.
Planners will be able to ask deep, complex strategy questions and see the results modelled in real time. As the data becomes more trusted, they will be able to make significant, informed, “just-in-time” decisions, confident in the patterns surfaced in the data. And as the line between planning and transactions systems begins to blur and disappear, plans will automatically cascade down to operational departments—even down to individual workflows—in real time.
‘Strategy’ will become the province of human-driven
innovation while planning becomes an organic, ongoing exercise of continuous
improvement inextricably linked to the transactional systems that execute
plans.
Leading the change
Today finance acts as the central junction within business planning and is, therefore, a natural steward for change, helping normalise new habits and behaviours for the rest of the organisation. As such, there is a strong case to be made for finance teams to double down on their new position as stewards of change by acting as transformation leaders—both for existing processes, and for future, unknown developments.
Finance’s role will change significantly in
order to leverage technology developments in the data-driven, AI future.
Driving collaboration with business partners, breaking down data silos, and
embracing new technologies and processes to keep pace with today’s rapidly
changing business environment will be key. The result will be an augmented,
intelligent planning process that delivers true business agility.
Everyone wants to implement Artificial Intelligence (AI) and Business Intelligence (BI) solutions. AI alone is anticipated to generate $15.7 trillion…
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Everyone wants to implement Artificial Intelligence (AI) and
Business Intelligence (BI) solutions. AI alone is anticipated to generate $15.7
trillion in GDP by globally 2030, and as this market grows, AI and BI will
shift from industry buzzwords, to key market differentiators, before eventually
becoming the new normal in the corporate landscape.
Yet
bringing AI and BI on board is a big leap if it’s your first major data
project. Stibo Systems’ Claus Jensen, Head of Emerging Technology, comments
on the role of MDM as a vital foundation to implement emerging data technology.
Most CEOs
don’t trust their own data.*
Let that
sink in for a moment.
Almost
every business is looking to data solutions to fuel the next phase of growth
and innovation. AI and BI are firmly on the agenda, yet a report by Forbes
Insights and KPMG found 84% of CEOs are concerned with the quality of the data
they’re basing their decisions on.
That’s a
significant disconnect. Businesses at board level want to implement ‘next
generation’ data projects, but don’t trust
the data that will be fed into them. For CDOs and other data leads, this
presents a difficult situation. They need to meet demand for cutting-edge data
projects, knowing that there is a certain level of mistrust in the data at
their disposal.
For many
CDOs, that mistrust isn’t limited to the CEO. Think about the data you are
currently processing: how confident are you that it’s being accurately sourced,
entered, saved, stored, copied and presented? How well do you know that data
journey once it leaves your sphere of control? Are you certain that a
single source of truth is being maintained?
The
data gold rush
It may only
be major data breaches that make the headlines, but in the global gold rush for
data, too many businesses fail to accurately extract, store and interpret data.
Mistakes
are made at every stage in the process – in fact, so bad are we at processing
data, a report by Royal Mail Data Services claims that around 6% of annual
revenue is lost through poor quality data.
It’s
equally bleak in the US, where Gartner’s Data Quality Market Survey puts the
average cost to US business at $15 million per year.
Despite
this, we’re rapidly moving the conversation from data capture to artificial
intelligence (AI), business intelligence (BI) and connected devices (IoT) – and
for good reason.
Putting
aside the issue of bad data (we’ll come back to that), businesses now have
access to more data than they can handle – according to SAS’ Business
Intelligence and Analytics Capabilities Report, 60% of business leaders
struggle to convert data into actionable insights, and 91% of companies feel
that they are incapable to doing it quickly enough to make useful
changes.
Business
Intelligence and Analytics Capabilities Report
In large
businesses, where data streams are blended from many sources, machine learning
can help data scientists monitor figures to flag outliers, irregularities and
noteworthy patterns.
Once
flagged, business leaders can use BI to bring those patterns to life, helping
pave the way for the most appropriate, and profitable, action.
Stibo Systems’ Head of
Emerging Technology, Claus Jensen, believes it’s only a matter of time before
we see AI regularly used within business product features – with machine
learning automating tasks thanks to effective data interpretation.
Jensen and
his team are working at the forefront of data: building master data management
solutions in conjunction with AI and BI. “We’re entering into a new era of data
analytics,” says Jensen. “Data scientists aren’t going away, but they can do
more and more high-level work as certain use cases are solved by AI.”
One of
these use cases is machine learning-based auto classification. “For retailers
onboarding thousands and thousands of new products every month, it’s really
time consuming for them to have the vendor categorise the product into the
vendor taxonomy.
“Machine
learning can automate this based on product description and image.”
Running
before we can walk
As exciting
as this sounds, businesses eager to install new uses for data often face
significant challenges: their data isn’t watertight, or it’s siloed, often
both.
In a piece
penned for the Financial Times, Professor of Economics at Stanford Graduate School
of Business, Paul Oyer, wrote: “Smart managers now know that algorithms are as
good as the data you train them on.” In other words, AI (and analytics for that
matter) can only ever be as good as the date you feed it.
Which
brings us back to the question of trust. What needs to happen for CEOs to trust
their own data?
While
there’s no single answer to this question, a master data management (MDM)
solution is a good place to start.
“You can
think of MDM as the foundation, a layer, that provides a single source of the
truth for data,” explains Jensen. “Analytics and machine learning is only
useful if the data you’re working on is accurate. That’s where MDM comes in; it
ensures information presented, and actions taken, are based on fact and
reality.
“Otherwise,
business analytics is just a nice and colourful way to look at bad data, and
what’s the point in that?”
In today’s market expectations are growing and the stakes are high, with one mistake potentially costing a retailer their reputation….
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In today’s market expectations are growing and the stakes are
high, with one mistake potentially costing a retailer their reputation. Due to
this level of risk, brands find reducing their hands on approach to processes
difficult, but what they don’t realise is that technology such as Artificial
Intelligence and Machine Learning could prove to be their hero, not their
villain. Entrusting their data and brand values to such technologies may seem
like a scary step, but as David Griffiths, Senior Product Marketing &
Strategy Manager, Adjuno, discusses, it’s one that will free up
retail teams to add value and cut costs.
In AI
should we trust?
There is a great deal of obstacles to overcome when it comes to the stigma attached to AI. A key challenge facing the progression of this technology is that individuals simply do not trust it. The fear of the unknown is one concern that pops up most commonly, with people battling a perceived perception that those who use this technology will lack control.
But a new age
of retail is approaching and there is now an even greater need for brands to
define their processes in order to keep up. Consumers want to receive products
that are of a high-quality and they want to receive them now. These
expectations are taking us beyond the traditional methods of retailing and
leading us into a world immersed in technology, a world that benefits from the
helping hand of AI.
Informing
key decisions
With AI,
retailers will be able to gain valuable insights in warehouse management,
logistics and supply chain management, and make more informed and proactive
decisions. This technology makes it easier to analyse huge volumes of data in
an efficient fashion, helping to detect patterns and providing an endless loop
of forecasting. Using this knowledge to identify factors and issues impacting
the performance of the supply chain, such as weather events, retailers will be
able to take a forward-thinking approach to decision-making. An approach that
will lead to reduced costs and delays.
By extending
human efficiency in terms of reach, quality and speed, this technology can also
help to eliminate the more mundane and routine work that’s faced by employees
across the retail spectrum. From tackling flow management by assessing key
products to ensuring there is enough stock available to improving production
planning, a more informed use of time will help equip brands to face every
consumer request and demand.
This is
particularly important for those brands whose product line extends further than
apparel wear, and steps into the realm of hardware. With diversity comes a need
for more proof points and in turn, an extended volume of data. Retailers will
be battling to work across an even greater number of suppliers and distribution
centres, and accommodating the expectations of a larger customer base.
Considering this, it is fundamental that every last bit of data is refined and
utilised to streamline processes. AI is providing retailers with a platform to
do this, offering the potential for significant changes across the entire
product journey.
A data
conundrum
The benefits of
using AI to consolidate data are endless. Traditionally, teams have relied on
spreadsheets to collate information, hindering their ability to forward plan.
With AI this is no longer the case, a much more accurate picture of the hero
products, sizes and colours likely to sell, can be achieved by looking at
multiple scenarios in real time and pulling them together.
This doesn’t
mean that AI will replace creative buying teams. AI doesn’t forecast trends, it
can’t predict what consumers will be buying in 2020, it can only report on the
product lines. It can however help buying teams assess partners, analyse stock
patterns, track costs, enable capacity planning and help optimise shipments.
This data is invaluable to teams, especially for any new buyers who may need
extra guidance.
Conclusion
AI is set to
transform the retail scene as we know it. But in order to make implementation a
success, there shouldn’t just be a focus on the evolution of data management,
there must be an evolution of mindsets too. After all, if a retailer fails to
jump on board with AI and embrace a new era of change, then their customers
will be the ones who suffer.
Companies that use voice plus touch interactions with their products and services are actually seen as less trustworthy and less…
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Companies that use voice plus touch interactions
with their products and services are actually seen as less trustworthy and less
engaging by their users, according to new research from emlyon business school.
The research, conducted by Margherita Pagani,
Director of the AIM Research Center on AI in Value Creation and Professor of
Digital Marketing at emlyon business school, and colleagues from ESSCA School
of Management and Florida State University, College of Business, analysed the
impact and differences between ‘touch’ interaction and ‘touch and voice’
interaction on personal consumer engagement and brand trust.
The researchers created two separate experiments,
focused on a utilitarian product and then a hedonic product, both of which had
over 90 participants belonging to generation Y, which is commonly equipped with
the latest smartphones and frequently use them for business interactions. For
both experiments, participants had to interact with the brand using their
smartphone including a phone call to the company to ask a specific set of
questions.
One group was required to interact with the brand
through the smartphone using a touch-only interaction, and the other used both
touch and voice interaction – either Apple’s Siri or OK Google. After
interacting with the company, participants were asked to rate their customer
experience. The participant’s answers were then measured to evaluate personal
engagement with the tasks, their level of trust with the brand and their
privacy concerns.
The researchers found that participants who used
the touch-only interaction experienced a much higher level of personal
engagement with the brand compared to those who used the touch plus voice
interaction.
Prof. Pagani says,
“Many companies have introduced new AI products
that use voice-activation such as Amazon’s Alexa, Google’s Home Assistant or
Apple’s Siri. These have been introduced in order to increase customer
experiential engagement, stimulate the interaction and collect more data that
allow to better personalise the experience through machine learning.
However, our study shows that in the initial phase of adoption, adding
voice recognition actually has the opposite desired effect. Even if voice may
be considered as a way to develop a much more natural interaction, the level of
cognitive efforts required to the brain using two sensory modes (voice and touch)
are higher. Therefore, consumers find it harder to completely engage with the
product and can easily be distracted”.
The researchers also found that participants who
used the touch-only interaction felt as though they had more control over the
information they shared and therefore had greater confidence in the brand.
Users stated that they found it much simpler to input information using only
one sensory method; touch.
“The lack of familiarity with how these digital
voice interactions actually work is likely to be the reason as to why consumers
are less trusting of brands that use both touch and voice. Whilst the use of
touch also garners much more control for a consumer, as opposed to voice”.
The
study, published in the ‘Journal of Interactive Marketing’ is the first of its
kind to explore the effects of new voice-based interface modes on marketing
relationships. Whilst technology multiplies the way that consumers can interact
with brands, this study shows that too much interaction can actually harm a company,
and offers managers guidance on how to increase personal engagement and brand
trust.
This month’s cover features Gary Steen, TalkTalk’s
Managing Director of Technology, Change, and Security, Gary Steen regarding the
telco’s commitment to thinking, and acting, differently in a highly competitive
marketplace…
TalkTalk is an established telecommunications company that fosters a youthful, pioneering spirit. “I like to think of TalkTalk as a mature start-up,” says Managing Director of Technology, Change and Security, Gary Steen. “We are mature in terms of being in the FTSE 250, with over four million customers, relying on our services every day through our essential, critical national infrastructure. But that said, I definitely think we start our day thinking as a start-up would. What can we do differently? How do we beat the competition? How do we attract great talent? We’ve got to come at this in a different way if we are going to succeed in the marketplace. We are mature, but we think like a start-up.”
Elsewhere we speak to Natalia
Graves, VP Head of Procurement at Veeam Software who reveals the secrets to a
successful procurement transformation. Graves
was tasked with looking at the automating, simplifying, and accelerating of
Veeam’s procurement and travel processes and systems around them, including
evaluating and rolling out a company-wide source-to-pay platform. “It has been
an incredible journey,” she tells us from her office in Boston, Massachusetts.
We also feature exclusive interviews with PTI Consulting and cloud specialists
CSI.
Plus,
we reveal 5 of the biggest AI companies in fintech and list the best events and
conferences around.
IPsoft has introduced 1Bank, the first conversational banking solution featuring virtual agent Amelia. It has been rated the top virtual…
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IPsoft has introduced 1Bank, the first conversational banking solution featuring virtual agent Amelia. It has been rated the top virtual agent in conversational AI by Everest Group.
Chetan Dube, CEO at IPsoft, commented: “With 1Bank we provide the most humanlike digital experience in the marketplace, built from the knowledge we’ve gained serving six of the world’s leading banks with conversational AI. We are giving banks the possibility of providing customers with their own personal banker around the clock.”
1Bank answers FAQs, but also resolves complex customer needs, by understanding customer intent. It can also switch context, mid-conversation. Its machine learning Learning (ML) abilities also mean that 1Bank can improve over time.
Some of the tasks 1Bank can carry out are:
advising on unpaid bills, proactively informing customers of an incoming bill and communicating any insufficient funds, making a money transfer and asking if the customer wants to set up payment for the bills when they are due.
recommending and setting up recurring payments, making payments from different accounts, opening and closing accounts.
helping customers locate transactions.
assisting with individual and potentially fraudulent charges on credit cards and disputing them, getting a new pin, getting a balance transfer or applying for a new credit card.
creating travel alerts after a customer made an airline purchase and proactively recommending the next step, such as, when traveling to exchange and withdrawing cash.
1Bank can integrate with existing tools and interfaces, and it can be added to existing applications to help customers quickly access the information and service they need. This includes mobile apps, desktop or kiosk apps, website modules, or within consumer chat applications, such as Facebook Messenger and Amazon Echo.
It is a measure of how much we take sophisticated technology for granted that the appearance of a pop-up chatbot…
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It is a measure of how much we take sophisticated technology
for granted that the appearance of a pop-up chatbot screen, asking questions
and providing sensible responses, is no longer considered remarkable.
Chatbots today inhabit websites, intranets, apps, and social
media platforms, and have become so ubiquitous as to become almost invisible.
Interacting with a text screen is a natural activity, and most users don’t seem
to care much about whether the other side of the conversation is a human or a
bundle of code.
From a corporate perspective, chatbots can be a win/win.
Increasingly reliable in their responses and cheap to operate, they are
available night and day and are instantly scalable. Whether your site or app
has one visitor a day or thousands, the bot is always eager to help.
We’ve come a long way from the disastrous early attempts at
providing AI assistance – remember Microsoft’s paperclip? – but what a good
chatbot does today is much the same as that much-loathed animated character:
identify what a user is trying to do, and offer appropriate help.
Artifical
intelligence
You will have heard of the Turing Test, which held that if a
computer could provide responses that were indistinguishable from those of a
human, the machine had to be considered intelligent. Are we there yet?
Despite some well-publicised claims, the answer is still,
probably, no. In 2014 a program called Eugene Goostman successfully tricked
Turing Test judges into believing it was a 13-year-old Ukrainian boy. This
controversial victory is still a failure, though, because nobody expects or
wants to find an adolescent behind a real-world help screen.
More relevant is the Loebner Prize for the most convincing
chatbot. This awards bronze medals each year to the best contenders, but has
never made a silver (text) or gold (audio visual) award – the equivalent of a
Turing Test pass.
A glance at the best entries from the 2018 competition shows
why. Eleven bots were asked 20 questions, winning two points for a human-like
answer and one for a plausible response. Out of a maximum of 40 points, the
winner scored 27 and the lowest just 12.
Even simple questions can make the tech fall over. The
winner, a chatbot called Tutor by Ron C Lee, answered “Do you know how to
make toast?” with “No, we haven’t”.
Chatbot limitations
While there remain limits on what a chatbot can convincingly
do, this need not be a problem if it is deployed in the right way. Recent
research from Penn State University found that while many appreciate an
apparently empathetic response from a bot, those who believe machines are
actually capable of consciousness do not.
“The majority of people do not believe in machine
emotion, so took expressions of empathy and sympathy as courtesies,” said researcher
Bingjie Liu. “However, people who think it’s possible that machines could
have emotions had negative reactions from the chatbots.”
The answer is only to use them for things they are good at,
says James Williams, who leads the development of advanced chatbots with
Nottingham-based software company MHR. While chatbots are now common in
consumer interfaces, he notes, there is much potential in the enterprise space.
Business bots
When applied within the company’s flagship human resources (HR) software, Williams says the conversational interface is an excellent way to simplify common transactions. “You’ll hear us talk a lot about reducing friction,” he says, which means anything that slows down a routine interaction.
An example is an employee submitting an expenses claim,
which MHR’s Talksuite does through an AI-driven chatbot. “Taking a picture
of a receipt is a natural thing to do, and the AI will recognise the image,
understanding the content as well as the context. Bots are really good for
processes with lots of rules or lots of steps, and here it just asks a few
questions and saves the employee a lot of hassle. Less friction.”
Knowing when not to deploy a bot can be just as valuable. Williams recounts one client which had deployed a complex chatbot for its newly joining employees, known in HR circles as the onboarding process. “The chatbot went through everything plus the kitchen sink, so the employee was there for 20 minutes or more being interrogated by a machine. It was just awful. A web-based form is a much better interface in this situation.”
His final advice is to consider the image the bot projects.
“Any personality in a chatbot tends to come accidentally, unlike a website
or an app. If you let software developers write the conversation, you might end
up with a bot that’s actually a bit of a dick. People make judgements on things
like language and punctuation. It’s fine to be personable and friendly, but it
should be clear when the user is talking to a bot and when any transition to a
human interaction takes place.”
Quest Solution Inc, provides supply chain and artificial intelligence (AI) based machine vision solutions. It has been awarded a project by…
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Quest Solution Inc, provides supply chain and artificial intelligence (AI) based machine vision solutions. It has been awarded a project by a leading supply chain and logistics provider in the US. The release doesn’t detail who the leading supply chain provider is, but it does reveal that the project is valued at around $US7 million.
A patent
that will allow for a robot to live at your home and handle your deliveries has
been filed by Amazon. The patent outlines plans for a robot that will
completely transform last mile delivery capabilities, even potentially
delivering packages in the early hours between 2am and 6am.
Back to AI, NFI Industries and Transplace are paying attention to
this technology through partnerships with firms that add AI capabilities to
transportation and distribution. Both companies have announced a partnership
with Noodle.ai
with the goal of enhancing logistics services and technology capabilities.
In a video interview with CNBC, Lance
Fritz, the CEO of Union Pacific, is concerned that supply chain
disruption won’t return to normal. He believes the biggest concern lies in
trade and that the challenges with China should be resolved as soon as
possible.
In an interview with Sky News, Peter
Schwarzenbauer, BMW board member responsible for Mini and Rolls
Royce, has said that the firm will need to think about moving production from
the UK in the event of a no-deal Brexit. Remaining would be too costly for the
organisation and some production would move to countries like Austria. Toyota
shares similar concerns with Johan van Zyl, head of Toyota’s European
operations, telling the BBC that Brexit hurdles would ‘undermine Toyota’s
competitiveness’.
Blockchain remains an interesting solution for many in the supply chain and
Blockchain Labs for Open Collaboration (BLOC) has recently started working with
NYK, a Japanese shopping company, and BHP, a mining company, to establish a
sustainable biofuel supply chain using BLOC’s blockchain fuel assurance
platform.
Also in the news: HighJump,
a global supply chain solutions provider, awarded five women in its Top Women
Leaders in Supply Chain awards; Cryptobriefings
Kiana Danial examines whether VeChain can deliver a supply chain solution; Apple
releases a supply chain document that reveals how iPhone, airpods and other
products are all zero waste; and SIGTTO GM, Andrew Clifton, looks to the LNG
supply chain.
