Pat Bermingham, CEO of B2B digital payment specialist Adflex, asks what impact will Artificial Intelligence really have on B2B payments?

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:

  1. 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.
  2. 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:
  3. AI systems that are used in products falling under the EU’s product safety legislation, including toys, aviation, cars, medical devices and lifts.
  4. 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?

AI will transform payment data analysis

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.

Quicker, more accurate invoice reconciliation

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.

Adflex has 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.”

  • Artificial Intelligence in FinTech
  • Digital Payments

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

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.

  • Artificial Intelligence in FinTech

Financial institutions are increasingly turning to artificial intelligence (AI) to gain a competitive edge. AI tools streamline operations, improve customer…

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.

  • Artificial Intelligence in FinTech

Data analysis is critical for predicting risks and returns. The ever-growing size of data has overwhelmed human capacity. This is where artificial intelligence (AI) comes in.

AI is transforming the financial sector by automating routine tasks and efficiently analysing large and complex data sets. It can analyse vast amounts of data with unprecedented speed. The instant but comprehensive insights that this capability provides lead to significantly improved accuracy.

Introduction to AI in Financial Forecasting

Financial forecasting can be challenging for smaller businesses. They often rely on assumptions and human judgement. This can result in inaccuracy, especially when unexpected events occur.

AI can analyse massive amounts of data to find hidden patterns that drive revenue. It automates routine tasks and enables a more detailed analysis than humans can achieve on their own.

Predictive Analytics

By automating data processing and interpretation, AI empowers financial teams to make informed decisions based on a strong analytical foundation. It goes beyond basic analysis by employing advanced algorithms and machine learning (ML) to extract valuable insights from data.

This not only improves the accuracy of forecasts but also unlocks a deeper understanding of market complexities that were previously out of reach.

Risk Assessment

AI algorithms use advanced data processing to spot patterns, unusual activities, and connections that traditional methods might miss. 

By training ML models on past data, AI can learn to identify patterns associated with fraud. These models then analyse new transactions, compare features, and flag potential problems in real-time.

Real-time Data Analysis

Slow reporting and analysis have hindered companies’ ability to adapt. AI-powered systems overcome these issues by enabling real-time analysis and decision-making.

AI’s ability to process massive amounts of real-time market data helps financial experts identify opportunities and adapt to market shifts quickly. This translates to increased resilience and competitiveness for businesses.

Case Studies

Financial institutions are increasingly using AI to improve their forecasting and data analysis for managing operational risk. This trend is likely to continue as IndustryARC expects the AI market to reach US$400.9 billion by 2027, growing at a compound annual growth rate (CAGR) of 37.2% during the forecast period of 2022–2027.

Deutsche Bank‘s collaboration with NVIDIA on “Financial Transformers” shows the potential of AI for early risk detection. These models can identify warning signs in financial transactions and speed up data retrieval, helping banks address potential problems quickly and ensure data quality.

AI also plays a key role in anti-money laundering (AML) efforts. By analysing transaction patterns, customer behaviour, and risk indicators, AI can identify suspicious activities for investigation. This not only improves detection rates but also streamlines the process. Google Cloud’s AML AI is a prime example; it helped HSBC find many more real risks while significantly reducing false positives, saving them time and resources.

Future Prospects

AI in finance is expected to significantly reshape financial forecasting. Analysts and executives will see widespread AI adoption for tasks like data analysis, pattern recognition, and automation. This trend is driven by the projected growth of global AI in the finance market. A report by Research and Markets predicts it will reach $26.67 billion by 2026, growing at a rate of 23.1% each year. 

For investment firms, AI can make highly accurate forecasts and execute complex trading strategies, optimising investment decisions and returns. Banks will also benefit from AI’s capabilities. AI-powered data analysis can give banks a deeper understanding of their customers, enabling personalised financial services. Chatbots and robo-advisors used for customer service and financial planning will continue to evolve, becoming more advanced and even more human-like in their interactions.

  • Artificial Intelligence in FinTech

The banking industry is slowly adopting artificial intelligence (AI) technology. It offers many benefits for financial institutions, from upgrading customer…

The banking industry is slowly adopting artificial intelligence (AI) technology. It offers many benefits for financial institutions, from upgrading customer experience to automating menial tasks. However, many are still cautious about using AI in certain areas, such as regulatory compliance management.

Given the continuously evolving legal requirements, good regulatory compliance management is crucial for banks. AI solutions can help effectively manage compliance by automating repetitive tasks, detecting suspicious activity, and providing real-time insights.

Automated compliance monitoring with AI

Artificial intelligence allows banks to perform continuous tasks around the clock with automated compliance monitoring. The previously labour-intensive work can be done more efficiently to ensure the bank follows all regulatory obligations.

The bank’s compliance teams usually handle monitoring processes, but AI automation can reduce costs. The compliance team can also focus on more important tasks rather than repetitive work.

The increased efficiency also means reduced compliance risk and non-compliance damage like fines.

Risk management

Financial institutions face regulatory compliance risks in various areas, which can lead to legal sanctions, financial loss, or a bad reputation. Advanced AI solutions can aid in risk management by identifying and mitigating risks more effectively.

AI-powered solutions can develop more accurate risk models and provide real-time responses. Many banks use this technology to help streamline compliance while improving the security of sensitive financial data. Furthermore, AI can detect compliance gaps and ensure adherence to laws and regulations.

Data analysis

AI can quickly analyse large volumes of data, a novel capability in the industry. A data analysis system can be designed to keep track of the latest regulatory changes and ensure the bank remains compliant.

Machine learning models can identify suspicious patterns and detect anomalies to report any breach of regulation. They can also analyse historical data and predict compliance risks. These allow banks to mitigate risks and address compliance issues before they escalate.

Case studies

Several banks have successfully used AI for regulatory compliance solutions. HSBC, for instance, uses AI-powered Know Your Customer (KYC) verification. This system can analyse customer data quickly, identify potential risks, and alert compliance officers for investigation. This bank also used Google Cloud’s Anti Money Laundering (AML) AI to combat and detect fraudulent activities in real-time. With these, HSBC has reduced the verification time by 80 percent and experienced a significant reduction in false positives.

Meanwhile, Danske Bank has also earned benefits from using fraud detection AI. The bank witnessed a 60 percent reduction in false positives and a notable decrease in fraudulent activities.

Future outlook for AI in regulatory in compliance

AI solutions are predicted to fundamentally change financial institution compliance management in the next five years, according to McKinsey. In the future, implementation for regulatory compliance in banks will be more widespread. Over 80 percent of C-level executives who participated in an Accenture survey planned to commit 10 percent of their AI budget by 2024 to address regulatory compliance.

AI offers many benefits, and as accessibility to this financial technology increases, more financial institutions will be inclined to adopt it, according to the Financial Stability Review.

Technology will evolve, giving better automation capabilities, more extensive data analysis, and enhanced interpretation. This could further reduce the manual effort required in the banking industry.

As adoption increases, ensuring the AI systems used are ethical and unbiased is necessary. Thus, banks need to provide transparency for AI in banking and adherence to guidelines.

  • Artificial Intelligence in FinTech

Expert analysis of the tech trends set to make waves this year

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

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

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. 

3d rendering cute artificial intelligence robot with empty note

“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


In today’s fast-moving world, technology doesn’t sleep. Through the help of experts, we’ve compiled a need-to-know list of 23 predictions for 2023

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

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

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

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

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

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

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

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

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

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


Tricentis CEO, Kevin Thompson

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

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


Michal Salat, Threat Intelligence Director at Avast

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

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.”

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

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.

Read the latest issue here!

Wells Fargo: customer-centric banking

Fleur Twohig, Wells Fargo

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
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.

Enjoy the issue!

Dan Brightmore, Editor

Two-thirds of accounting departments still process invoices manually: only 15% are fully paperless

Despite the increasing need to process invoices remotely as more employees are urged to work from home, the majority of companies are still lagging behind in automation implementation. Accounts payable departments are still largely processing invoices manually, according to a survey of accounting and finance professionals released today by Ephesoft, Inc.


The survey gathered responses from 200 accounting and finance professionals from 26 countries. Key findings include:

Distributing or processing paper documents


Businesses are shifting to automation of their processes – especially for high-value, high-volume documents such as invoices. However, the survey results indicate that companies are slow to change when it comes to digitally transforming invoice processing and other financial documents. 

●       Only 15% of respondents said that their organisation is fully paperless, which means the majority of businesses (85%) are not. 

●       Of those who are not, just slightly over 50% are actively pursuing a paperless environment.

●       One-third (33%) of companies are predominantly paper-heavy, still far from intelligent automation.

With an average cost to process per invoice at about £11, a lack of automation is likely to keep company growth limited, leaving room for a significant increase in productivity. Modern automation has been proven to cut costs significantly, often by 80% or more, which can be reinvested in other areas.

Current technologies

When asked whether their businesses currently have document management, workflow, AP automation, RPA or artificial intelligence technologies in place, a majority of companies report having some type of document management and workflow tools system in place, but AI applications are still under-utilised. Here’s the breakdown, further showing a lack of current automation tools:

●       Less than one-third (30%) employ accounts payable automation.

●       Only 12% utilise RPA tools and just slightly less (11%) report using AI.

While these findings are understandable and relatable, Ephesoft predicts that new AI-powered low-code/no-code, cloud technology, which is evolving at a rapid pace, will remove barriers to entry into AI.

The AI Journey


When the question was posed, “What is your organisation’s location on the AI journey?” responses were split, with 42% saying they were in the planning stage and 40% saying they were not planning on implementing AI tools at all. 

We can conclude from the data that AI has still not been widely adopted, but many organisations have plans to invest in it. 

“This survey confirms that the accounting profession has lagged in adoption of newer technologies such as AI/ML, cloud and low-code/no-code architecture likely impacted by traditionally long implementation cycles and complex integrations,” said Naren Goel, chief financial officer, Ephesoft. “The accounts payable space is an ideal example where manual steps like entering invoices into an ERP system can greatly impact efficiency, so it’s exciting that we are finally starting to see innovation in this space with point solutions that are up and running in hours, eliminate manual tasks and allow accounting professionals to focus on higher value-add functions.”

The survey on digital transformation, AI, technology and automation was conducted on Nov. 5, 2020, by Accounting Today on behalf of Ephesoft. Responses are from 200 accounting and finance professionals from 26 countries, including CEOs, CFOs Partners, CIOs, CTOs, CPAs, accountants, controllers, auditors and consultants in a variety of industries, including banks, energy, government, healthcare, technology, accounting services, airlines, auto, education, large global consultancies and many others.

Gurpreet Purewal, Associate Vice President, Business Development, iResearch Services, explores how organisations can overcome the challenges presented by AI in 2021.

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.

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…

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.

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

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.

AI is no longer science-fiction writers dream, it’s being implemented in industries all over the world. We look at 5…

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.

https://www.marksandspencer.com/
https://twitter.com/marksandspencer
https://www.facebook.com/MarksandSpencer

John Lewis/Waitrose

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.

https://www.johnlewis.com/
https://twitter.com/JLandPartners
http://www.facebook.com/johnlewisretail

Walmart

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.

https://www.walmart.com/
https://www.facebook.com/walmart

Amazon

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.

amazon.co.uk

https://twitter.com/amazon
https://www.facebook.com/AmazonUK/

Morrisons

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.

groceries.morrisons.com
https://www.twitter.com/morrisons
http://www.facebook.com/Morrisons

The uptake of artificial intelligence by industry will drastically change the UK job market in the coming years – with…

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.

The findings come from a new report – Harnessing the Power of AI: The Demand for Future Skills – from global recruiter Robert Walters and market analysis experts Vacancy Soft.

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

  1. Agriculture
  2. Business Support
  3. Customer Experience
  4. Energy
  5. Healthcare
  6. Intellectual Property
  7. IT Service Management
  8. Manufacturing
  9. Technical Support
  10. Retail
  11. 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.”

To download a copy of the report click here.

By Robert Douglas, Europe Planning Director at Adaptive Insights, a Workday company Now, more than ever, agility is the currency…

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…

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?”

To find out more about how MDM can turn data into business value through actionable insights, forming a solid foundation to AI and BI, visit https://www.stibosystems.com/solution/embedded-analytics-platform.

In today’s market expectations are growing and the stakes are high, with one mistake potentially costing a retailer their reputation….

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.

By Amyn Jaffer, Head of Intelligent Automation, Ultima Most businesses now recognise they will need to embrace intelligent automation to…

By Amyn Jaffer, Head of Intelligent Automation, Ultima

Most businesses now recognise they will need to embrace intelligent automation to gain competitive advantage. From improving business processes and customer experience, to using ‘cobots’ to work alongside their workforce, AI offers companies huge scope to improve their business efficiency and drive innovation.

Yet, while many companies are excited about the potential of this new technology, the very concept of AI often evokes fear of the unknown for others – especially for businesses that, understandably, don’t know where to start on their Intelligent Automation journey. As with most daunting tasks, the best approach is to take incremental steps.

RPA: a good place to start

An ideal first step on the road to digital transformation is the introduction of RPA (robotic process automation), which uses robots to handle high-volume, repeatable tasks that previously required humans to perform them. These tasks can include queries, calculations and maintenance of records and transactions.

As well as being relatively simple to implement, using software robots is both affordable and effective; and the potential benefits are impressive.

As an example, RPA can be used by HR teams to ensure each company department has the same information about every employee without the typical challenges of running multiple system records and repetitive re-entry of information. It can also be used for absence management and for processing applications, saving time for your employees to focus on more strategic work. As a second phase, organisations can then make HR information more accessible by implementing chatbots.

Any large-scale activities or groups of repetitive tasks that draw on or feed information into multiple systems are also candidates for intelligent automation. In practice, this could mean using cognitive services such as text and sentiment analysis to process and respond to natural language text within formats such as emails, documents and live webchats. The aim is to extract data from these sources without the need for human intervention.

One training provider which takes up to 400,000 first line calls annually is using speechbots to answer calls and leverage RPA to verify the caller. This has resulted in reduced operational expenditure in the call centre by 50% and increased efficiency.

Similarly, cognitive services can also be used to improve business efficiency through visual recognition. One company is using this technology to tag information in photographs – a task that would take hundreds of man-hours to do, but just seconds with cognitive services.

At Ultima, we have been using RPA technology to automate our own back-end operations and we’ve seen productivity rise by a factor of two since implementing the technology across five processes. For example, we automated our forecasting and planning tasks. Software robots collate real-time sales and marketing information and process all the information they collect during the day to produce detailed forecasts and business intelligence for the next morning. Usually this took eight to ten hours per day of staff time. As a result, the business has improved business intelligence to plan with, and staff have more time to spend on customer service and strategic thinking. 

The next level

Taking care of mundane tasks, RPA frees companies to explore more complex AI-based automation – using visual and cognitive intelligence that draws information from multiple sources and interprets it to deliver improved business intelligence.

By automatically collecting and sifting through vast amounts of data and then training robots to make sense of the data by asking the data pertinent questions, businesses can start to solve the problems that have been keeping them up at night. For example, analysing customer data to establish insights into how different things affect their purchasing decisions can give real business benefits and drive innovations in how a business might supply and market its goods.

However, before taking this next step, it’s important for any organisation to look practically at their infrastructure, workforce and security, and consider what might need to change to enable their businesses to be set on a positive path to digital transformation.

Ready for the future

Ultimately, we’re all likely to have a ‘virtual worker’ by our sides helping us to do our jobs, cutting out mundane, repetitive tasks and freeing us up to be more creative and focus on business goals and innovation. To reach this stage the right foundations need to be in place, and the adoption of RPA is the best place to start.

Automated machines will collate vast amounts of data and AI systems will understand it. By coupling two different systems – one capable of automatically collecting vast amounts of data, the other that can intelligently make sense of that information – individuals and businesses will become more powerful. Take a deep breath, jump in and get ready to realis

Technology is becoming a tool for expanding human senses and abilities. This requires intelligent and immersive interfaces. Will voice, gesture…

Technology is becoming a tool for expanding human senses and abilities. This requires intelligent and immersive interfaces. Will voice, gesture and thought control soon replace keyboards and touchscreens?

Reply’s study, conducted with the trend platform SONAR, examines trend-setting concepts for interfaces between humans and computers – Human-Machine Interfaces – which are now becoming real possibilities for communication between humans and machines. For companies, there is significant potential for more personalised and emotional customer interaction as well as new possibilities for the visualisation and analysis of information.

Voice assistance

20 million people worldwide already use voice assistants daily to search for information, make purchases or play music. Also, in the corporate environment, voice assistants enable a completely new way of using technology and automate many tasks. The smart assistants perform entire tasks, record things or make calls without any human intervention. This increases productivity and leaves employees with more time for challenging tasks. Through voice interfaces devices can be controlled using voice input, and smart software agents will be able to perform an increasing number of services in the future. What’s more, electronic in-ear devices, so-called hearables, can be used for a wide range of applications, from wireless data transmission to communication services.

Extended Reality (XR)

The technologies combined under XR enable barrier-free interaction between man and machine and eliminate geographical distances. They revolutionise people interaction with the environment: Augmented, Virtual and Mixed Reality support consumer decision, reduce costs, increase efficiency and a more productive environment. Other emerging trends include gesture control and 3D displays, which create a virtual three-dimensional image of an object and offer interactive possibilities. Smart glasses, which provide the wearer with additional information about what they are seeing, are also among the XR trends.

Full Immersion

Full immersion technologies allow the direct exchange of information between man and machine. Advances in fully immersive technologies and neurosciences show that a world in which people are fully connected to computers is coming. Scientific research in medicine is leading the way into a future in which the human brain can control computers with mere thoughts and exchange ideas via headsets or brain implants. Companies are already working on neurally controlled interfaces. They offer direct communication channels between a networked brain and external devices. Another trend technologies are in the area of augmented bodies, which aim to strengthen the human body and its performance using things such as implants or electronic tattoos.

Furthermore, the study also identifies four visions that could soon become reality:

  • Sending thoughts: ideas, feelings and memories to be shared directly with other people.
  • Human enhancement: by directly connecting the brain with computers, AI-controlled assistants and the Internet, know-how can be downloaded into the brain or expanded with super-intelligent AI systems.
  • Neural healthcare: immersive technologies may enable people to recover from diseases that are still incurable today, such as Parkinson’s or paralysis.
  • Virtual copies: by connecting to computers, a person’s thoughts, memories and feelings can be stored as data and, one day, may even make a complete virtual copy of the brain possible.

“Communication between man and machine is one of the most exciting topics of our time. Technologies at the interface between us and intelligent systems will enable a paradigm shift in all areas of life in the near future. The resulting new products and services will offer completely new solutions for telling stories and visualising information. The three trends identified by SONAR and the four visions provide companies with guidance on their journey towards digital transformation,” says Filippo Rizzante, CTO Reply.

The Human Machine Interfaces report is part of a series published on the following topics AI, Retail Revolution and Consumer-IoT.

To read the full study, please click here.

IPsoft has introduced 1Bank, the first conversational banking solution featuring virtual agent Amelia. It has been rated the top virtual…

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…

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…

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.

The technology landscape is always changing, that much is a given, but since the week has started there have been…

The technology landscape is always changing, that much is a given, but since the week has started there have been significant shifts in regulation and application. In the United States, it was revealed that President Trump had signed an executive order to boost investment into artificial intelligence (AI). The order came about amidst concerns about competition with China – a report released by the United Nations revealed that while the US was still in the AI lead, China was catching up fast.

Trump said: “Continued American leadership in Artificial Intelligence is of paramount importance to maintaining the economic and national security of the United States.” [as reported by The Register

In the UK, a new £8 million facility has been proposed to form part of Nottingham Trent University’s dual-site Medical Technologies Innovation Facility. The project has yet to be approved, but the goal is to accelerate the development of medical technologies that could transform health and innovation in a field that is currently seeing significant global investment.

Technology innovation is also being used by some of the world’s largest brands as a way of measuring their impact on deforestation and look for a way to harvest palm oil responsibly. Nestle, Unilever, and Mondelez are working with new satellite technology that provides them with a ‘big brother’ bird’s eye view that can potentially help them police tree felling more effectively.

While on the topic of legalities and compliance, it was revealed that Karan Bhatia, the vice president of global public policy and government relations at Google, has asked for there to be increased ‘convergence’ around global technology regulation. While the one size fits all mantra has never worked for either technology or clothing, there is a need to develop more ‘common rules of the road’ that allowed for improved collaboration, protection and compliance.

On the flip side, Apple is currently being sued by Fundamental Innovation Systems for its infringement on multiple patents that pertain to USB charging and communication technologies. Apple has stated that it believes the patents to be invalid, saying as much in a latter to the U.S. Patent Trial and Appeal Board. In spite of potential licensing deal meetings, Apple filed a declaratory judgement action on February 07. Where to from here, nobody yet knows.

A quick roundup of news also includes technology being used by the Kremlin to force self-employed tax payers to cough up, a new technology that could potentially stop school shootings before they start, a new technology that can protect drinking water from the toxins present in Lake Erie, engineers developing a room temperature, two-dimensional platform from quantum technology, and how technology can potentially have a negative impact on domestic violence victims.