We’re now seeing banks transforming into digitally driven enterprises akin to big tech firms, building capabilities that enable a relentless focus on customers. So how can banks and finance institutions make the most of AI, and what are the key use cases in practice?

Benefits across the business

Many financial services firms had already adopted AI and ML prior to the pandemic. However, many had difficulties identifying which key functions benefit most from AI, and so the technology did not always deliver the returns expected. This is set to change in the coming months: increased AI and ML deployment will be at the heart of the economic recovery from COVID-19, and the pandemic has highlighted particular areas where AI should be applied. These range from informing credit decisions and preventing fraud, to improving the customer experience through frictionless, 24/7 interactions.

Some specific financial services processes that can be improved by AI include:

Document processing with intelligent automation

Intelligent and robotic process automation optimise various functions, enhance efficiency, and improve the overall speed and accuracy of core financial processes, leading to substantial cost-savings. One area that has risen in prominence is e-KYC, or ‘electronic know-your-customer’. This is a remote, paperless process that reduces the bureaucratic costs of crucial ‘know-your-customer’ protocols, such as verification of client identities and signatures.

This task once involved repetitive, mundane actions with considerable effort required just to keep track of document handling, loan disbursement and repayment, as well as regulatory reporting of the entire process. However, this year, organisations are embracing intelligent automation platforms that manage, interpret and extract unstructured data, including text, images, scanned documents (handwritten and electronic), faxes, and web content. Running on an NLP (natural language processing) engine, which identifies any missing, unseen, and ill-formed data, these platforms offer near-perfect accuracy and higher reliability. Average handling time is reduced, and firms gain a significant competitive advantage through an improved customer experience.

Efficient and thorough customer support

Virtual assistants can respond to customer needs with minimal employee input. A straightforward  means of increasing productivity, the time and effort spent on generic customer queries is reduced, freeing up teams to focus on longer-term projects that drive innovation across the business.

We’re all familiar with chatbots on e-commerce sites, and such solutions will become increasingly common in the financial services industry, with organisations such as JP Morgan now making use of these bots to streamline their back-office operations and strengthen customer support. The platforms involve COIN, short for ‘contract intelligence’, which runs on an ML system powered by the bank’s private cloud network. As well as creating appropriate responses to general queries, COIN automates legal filing tasks, reviews documents, handles basic IT requests such as password resets, and creates new tools for both bankers and clients with greater proficiency and less human error. 

Risk management analytics

Estimating creditworthiness is largely based on the likelihood of an individual or business repaying a loan. Determining the chances of default underpins the risk management processes at all lending organisations. Even with impeccable data, assessing this has its difficulties, as some individuals and organisations can be untruthful about their ability to pay their loans back.

To combat this, companies such as Lenddo and ZestFinance are using AI for risk assessment, and to determine an individual’s creditworthiness. Credit bureaus such as Equifax also use AI, ML and advanced data and analytical tools to analyse alternate sources in the evaluation of risk, and gain customer insight in the process.

Lenders once used a limited set of data, such as annual salaries and credit scores, for this process. However, thanks to AI, organisations are now able to consider an individual’s entire digital financial footprint to determine the likelihood of default. In addition to traditional data sets, the analysis of this alternative data is particularly useful in determining the creditworthiness of individuals without conventional records of loan or credit history.

The time to adopt is now

The way that businesses and clients interact with each other has changed irreversibly this year, and the finance industry is no different. Before the urgency demanded by the pandemic, financial institutions had been experimenting with AI and ML on a limited scale – mainly as a tick-box exercise in an effort to ‘keep up with the Joneses’. The widespread adoption that has been taking place this year stems from the need to truly innovate and increase resilience across the sector.

Banks and financial institutions are now aware of the key areas that benefit from AI, such as greater efficiency in back office operations, and significant improvements in customer engagement. A transformation process that was in its infancy prior to Covid-19 has accelerated and is fast becoming the standard approach. What’s more, financial organisations that are embracing AI now and prioritising its full implementation will be best placed to reap its rewards in the future.

Photo by Jeffrey Blum on Unsplash

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Algorithmia’s 2021 Enterprise Trends in Machine Learning report features the views of 403 business leaders involved with machine learning initiatives.

Diego Oppenheimer, CEO of Algorithmia, says:

“COVID-19 has caused rapid change which has challenged our assumptions in many areas. In this rapidly changing environment, organisations are rethinking their investments and seeing the importance of AI/ML to drive revenue and efficiency during uncertain times.

Before the pandemic, the top concern for organisations pursuing AI/ML initiatives was a lack of skilled in-house talent. Today, organisations are worrying more about how to get ML models into production faster and how to ensure their performance over time.

While we don’t want to marginalise these issues, I am encouraged by the fact that the type of challenges have more to do with how to maximise the value of AI/ML investments as opposed to whether or not a company can pursue them at all.”

The main takeaway is that AI budgets are significantly increasing. 83 percent of respondents said they’ve increased their budgets compared to last year.

Despite a difficult year for many companies, business leaders are not being put off of AI investments—in fact, they’re doubling-down.

In Algorithmia’s summer survey, 50 percent of respondents said they plan to spend more on AI this year. Around one in five even said they “plan to spend a lot more.”

76 percent of businesses report they are now prioritising AI/ML over other IT initiatives. 64 percent say the priority of AI/ML has increased relative to other IT initiatives over the last 12 months.

With unemployment figures around the world at their highest for several years – even decades in some cases – it’s at least heartening to hear that 76 percent of respondents said they’ve not reduced the size of their AI/ML teams. 27 percent even report an increase.

43 percent say their AI/ML initiatives “matter way more than we thought” and close to one in four believe their AI/ML initiatives should have been their top priority sooner. Process automation and improving customer experiences are the two main areas for AI investments.

While it’s been all good news so far, there are AI deployment issues being faced by many companies which are yet to be addressed.

Governance is, by far, the biggest AI challenge being faced by companies. 56 percent of the businesses ranked governance, security, and auditability issues as a concern.

Regulatory compliance is vital but can be confusing, especially with different regulations between not just countries but even states. 67 percent of the organisations report having to comply with multiple regulations for their AI/ML deployments.

The next major challenge after governance is with basic deployment and organisational challenges. 

Basic integration issues were ranked by 49 percent of businesses as a problem. Furthermore, more job roles are being involved with AI deployment strategies than ever before—it’s no longer seen as just the domain of data scientists.

However, there’s perhaps some light at the end of the tunnel. Organisations are reporting improved outcomes when using dedicated, third-party MLOps solutions.

While keeping in mind Algorithmia is a third-party MLOps solution, the report claims organisations using such a platform spend an average of around 21 percent less on infrastructure costs. Furthermore, it also helps to free up their data scientists—who spend less time on model deployment.

You can find a full copy of Algorithmia’s report here (requires signup)

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The cloud-based solution aims to help accelerate the delivery of software in a rapidly-changing world while maintaining a high bar of quality.

Gareth Smith, CTO of Eggplant, said:

“The launch of our cloud platform is a significant milestone in our mission to rid the world of bad software. In our new normal, delivering speed and agility at scale has never been more critical.

Every business can easily tap into Eggplants’ AI-powered automation platform to accelerate the pace of delivery while ensuring a high-quality digital experience.” 

Enterprises have accelerated their shift to the cloud due to the pandemic and resulting increases in things such as home working.

Recent research from Centrify found that 51 percent of businesses which embraced a cloud-first model were able to handle the challenges presented by COVID-19 far more effectively.

Eggplant’s Digital Automation Intelligence (DAI) Platform features:

• Cloud-based end-to-end automation: The scalable fusion engine provides frictionless and efficient continuous and parallel end-to-end testing in the cloud, for any apps and websites, and on any target platforms. 
• Monitoring insights: The addition of advanced user experience (UX) data points and metrics, enables customers to benchmark their applications UX performance. These insights, added to the UX behaviour helps improve SEO. 
• Fully automated self-healing test assets: The use of AI identifies the tests needed and builds and runs them automatically, under full user control. These tests are self-healing, and automatically adapt as the system-under-test evolves.   

The solution helps to support the “citizen developer” movement—using AI to enable no-code/low-code development for people with minimal programming knowledge.

Both cloud and AI ranked highly in a recent study (PDF) by Deloitte of the most relevant technologies “to operate in the new normal”. Cloud and cybersecurity were joint first with 80 percent of respondents, followed by cognitive and AI tools (73%) and the IoT (65%).

Eggplant’s combination of AI and cloud technologies should help businesses to deal with COVID-19’s unique challenges and beyond.

(Photo by CHUTTERSNAP on Unsplash)

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The figure, which comes from the analyst firm’s latest Worldwide Artificial Intelligence Spending Guide, calculates a CAGR of 20.1% as adopting AI becomes a ‘must’ in the enterprise.

In particular, companies will utilise AI to deliver a better customer experience, as well as help employees to become better at their jobs. Automated customer service agents, sales process recommendation and automation, as well as automated threat intelligence and prevention, are the primary use cases outlined by IDC.

Retail and banking are the two industries most likely to splurge in the coming years. The former, unsurprisingly, will focus more on customer experience, while the latter will invest on fraud analysis and investigation, as well as program advisors and recommendation systems.

Other industries have hit something of a proverbial wall, primarily as a result of Covid-19. Transportation, as well as the services industry – including leisure and hospitality – have already struggled with the pandemic. Naturally, IDC argued, AI investments will be on the back burner here in 2020. Yet the pandemic has seen some innovation; the research specifically noted hospitals who were using AI to speed up Covid-19 diagnosis and testing.

“Companies will adopt AI – not just because they can, but because they must,” said Ritu Jyoti, program vice president for artificial intelligence at IDC. “AI is the technology that will help businesses to be agile, innovate, and scale. The companies that become ‘AI powered’ will have the ability to synthesise information, the capacity to learn, and the capability to deliver insights at scale.”

In other words, leading organisations will be able to use AI to convert data into information and insights, understand those relationships and apply those insights to business problems, and then support decisions and bring through automation.

Sounds simple when it’s put like that.

Photo by Fabian Blank on Unsplash

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The research, titled AI Skills in the UK, shines a spotlight on some concerning issues.

For its UK report, Microsoft used data from a global AI skills study featuring more than 12,000 people in 20 countries to see how the UK is doing in comparison to the rest of the world.

Most notably, compared to the rest of the world, the UK is seeing a higher failure rate for AI projects. 29 percent of AI ventures launched by UK businesses have generated no commercial value compared to the 19 percent average elsewhere in the world.

35 percent of British business leaders foresee an AI skills gap within two years, while 28 percent believe there already is one (above the global average of 24%).

However, it seems UK businesses aren’t helping to prepare employees with the skills they need. Just 17 percent of British employees have been part of AI reskilling efforts (compared to the global figure of 38 percent.)

Agata Nowakowska, AVP EMEA at Skillsoft, said:

“UK employers will have to address the growing digital skills gap within the workforce to ensure their business is able to fully leverage every digital transformation investment that’s made. With technologies like AI and cloud becoming as commonplace as word processing or email in the workplace, firms will need to ensure employees can use such tools and aren’t apprehensive about using them.

Organisations will need to think holistically about managing reskilling, upskilling and job transitioning. As the war for talent intensifies, employee development and talent pooling will become increasingly vital to building a modern workforce that’s adaptable and flexible. Addressing and easing workplace role transitions will require new training models and approaches that include on-the-job training and opportunities that support and signpost workers to opportunities to upgrade their skills.” 

Currently, a mere 32 percent of British employees feel their workplace is doing enough to prepare them for an AI-enabled future (compared to the global average of 42%)

“The most successful organisations will be the ones that transform both technically and culturally, equipping their people with the skills and knowledge to become the best competitive asset they have,” comments Simon Lambert, Chief Learning Officer for Microsoft UK.

“Human ingenuity is what will make the difference – AI technology alone will not be enough.”

AI brain drain

It’s well-documented that the UK suffers from a “brain drain” problem. The country’s renowned universities – like Oxford and Cambridge – produce globally desirable AI talent, but they’re often swooped up by Silicon Valley giants who are willing to pay much higher salaries than many British firms.

In one example, a senior professor from Imperial College London couldn’t understand why one of her students was not turning up to any classes. Most people wouldn’t pay £9,250 per year in tuition fees and not turn up. The professor called her student to find out why he’d completed three years but wasn’t turning up for his final year. She found that he was offered a six-figure salary at Apple. 

This problem also applies to teachers who are needed to pass their knowledge onto the future generations. Many are lured away from academia to work on groundbreaking projects with almost endless resources, less administrative duties, and be paid handsomely for it too.

Some companies, Microsoft included, have taken measures to address the brain drain problem. After all, a lack of AI talent harms the entire industry.

Dr Chris Bishop, Director of Microsoft’s Research Lab in Cambridge, said:

“One thing we’ve seen over the past few years is: because there are so many opportunities for people with skills in machine learning, particularly in industry, we’ve seen a lot of outflux of top academic talent to industry.

This concerns us because it’s those top academic professors and researchers who are responsible not just for doing research, but also for nurturing the next generation of talent in this field.”

Since 2018, Microsoft has funded a program for training the next generation of data scientists and machine-learning engineers called the Microsoft Research-Cambridge University Machine Learning Initiative.

Microsoft partners with universities to ensure it doesn’t steal talent, allows employees to continue roles in teaching, funds some related PhD scholarships, sends researchers to co-supervise students in universities, and offers paid internships to work alongside teams at Microsoft on projects.

You can find the full AI Skills in the UK report here.

(Photo by William Warby on Unsplash)

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The round was led by new investors ICONIQ Capital, Kleiner Perkins and Sapphire Ventures. Along with these new investors, existing investors like Lightspeed Venture Partners, Bain Capital Ventures, and Comerica Bank. The fundraiser also received personal investment from John W. Thompson, partner at Lightspeed Venture Partners and chairman of Microsoft.

With this financing round, the cloud-based AI platform provider has raised a total of $105m to date.

Moveworks will be using the new capital to expedite the product innovation process for its natural language understanding (NLU) and advanced conversational AI technology to meet global demand for the platform for new and existing customers.

Moveworks CEO Bhavin Shah said: “Building Moveworks over the past three years has been an exercise in discipline and focus. The possibilities of AI are so vast that many start-ups get trapped by the allure of solving every problem their customers present to them. We chose to focus on a single problem that’s been holding IT support back for the last 30 years: resolving IT tickets, quickly and with minimal disruption to employees’ day-to-day jobs. We focused AI on deeply understanding enterprise IT support tickets to solve this very difficult problem. And we’ve succeeded.”

Montreal-based Stradigi AI also closed a Series A funding round where it raised $40.3m, which will accelerate the company’s expansion in North America and support continuous product innovation of its AI platform, Kepler. The funding round was led by Fonds De Solidarité FTQ and Investissement Québec, collectively investing $20m.

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The study argued that the largest technology companies were winning at artificial intelligence (AI) – not surprising, given they can hoover up all the best talent – but more importantly, business intelligence (BI) vendors wanting to ensure their future going forward needed to beef up their skillsets with AI. “Expect all modern BI vendors to release an AutoML product or buy a startup by [the] end of next year,” the report noted.

While that may not be entirely true, one company which is certainly ahead of the curve in this instance is data analytics platform provider Qlik. The company is focusing on what it calls ‘augmented intelligence’ – of which more shortly – with Qlik Insight Bot focusing on AI-powered conversational analytics.

At the forefront of this is Elif Tutuk (above), Qlik’s head of research. Ahead of her appearance at AI & Big Data Expo in Silicon Valley in November, AI News caught up with Tutuk to discuss Qlik’s research initiatives and concerns around artificial intelligence among others.

—

AI News: Hi Elif. What research initiatives are you and your team currently working on and how is this benefiting both Qlik and the wider industry at large?

Elif Tutuk: The Qlik Research team currently focuses on ‘augmented intelligence.’ Augmented intelligence is an approach that brings together the best of machine intelligence and human intuition to speed up time-to-insight, surface new and unexpected discoveries, and drive data literacy for users in any role and at any skill level.

These innovations lead new technologies that create a multiplier effect, where the human-machine collaboration outpaces anything either the human or the machine could do on its own. They help to decrease bias in human decisions and ultimately provide more adoption of analytics and the insights they provide.

AI: You wrote a piece last month exploring the issues with regards to bias in AI. What other concerns do you see organisations facing and encountering when they try to introduce AI/ML/automation into their workflows?

ET: There are niche applications for artificial intelligence that rely completely on machine automation, but more complex business problems require interaction and human capabilities. At Qlik, we do not believe that the ultimate purpose of the AI is to replace the user-driven analysis with a black box approach. Instead we argue that machine intelligence and automation must be combined with human-centred analysis. In this way, all users, even if with different roles and abilities, have access to data literacy.

A new school and culture is therefore needed to increase data literacy. Data literacy is the ability to read, work, analyse and discuss with data. It enables people to correctly interpret analytics results and insights generated by the machine.

AI: Which areas do businesses need to concentrate on first with AI/ML initiatives? Are there any quick wins available short-term?

ET: One of the biggest challenges that companies are facing in this fourth industrial revolution, as I mentioned before, is the lack of data-literate talent. The Data Literacy Index compiled by Qlik shows how the leaders of large global companies almost unanimously believe that it is important to have data-literate employees – this is perhaps not surprising, given that having data literate employees increases corporate value by 3%-5%. Two thirds plan to implement data literacy in the company.

AI: One of your speaking sessions at AI & Big Data Expo is around the third generation of business intelligence (BI) and how BI is evolving with AI. Aside from big data, what other technologies do you see converging with AI and why?

ET: AI will push forward the ideas of transparency, of seamless interaction with devices and information, making everything personalised and easy to use. We will be able to harness that sensor data and put it into an actionable form, at the moment when we need to make a decision.

AI: Are BI vendors more generally at serious risk of being left behind if they don’t embrace AI technologies quickly?

ET: Augmented intelligence and applications of AI drive less-biased decisions and more impartial contextual awareness, transforming how users interface with data, make decisions, and act on insights. It expands who has access to insights from analytics by delivering analytics anywhere and to everyone in the organisation, and does so with less time, skill and interpretation bias than current manual approaches. The goal of BI is to enable fact-based decisions – and the application of AI is a key enabler for that.

AI: What message are you looking to get across to the audience from your sessions? What are you hoping they will take away from your talk?

ET: During my session, I will talk about how artificial intelligence is not purely ‘artificial’, and about the ideal approach amplifying human intuition with machine intelligence to create a powerful multiplier effect. I will use analogies between how the brain processes information and data analytics and talk about the need for new data structure approaches to decrease bias in AI. The audience will also learn about what to consider when evaluating AI in analytics.

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Put in such a context, the research offers little comfort for businesses considering AI investments. On the other hand, it means half of AI projects are successful for three out of four companies; a far more incentivising statistic.

The two leading reasons for an AI project failing are:

1. A lack of required skills.
2. Unrealistic expectations.

IDC’s research found that just 25 percent of the businesses using AI have deployed an ‘enterprise-wide’ strategy.

The researchers surveyed 2,473 organisations that are using AI for their study. Half of the firms see AI as a priority, while two-thirds are establishing an ‘AI-first’ culture.

Ritu Jyoti, Program VP of Artificial Intelligence Strategies at IDC, said:

“Organisations that embrace AI will drive better customer engagements and have accelerated rates of innovation, higher competitiveness, higher margins, and productive employees.

Organisations worldwide must evaluate their vision and transform their people, processes, technology, and data readiness to unleash the power of AI and thrive in the digital era.”

More than 60 percent of the businesses report changing their business model in response to adopting AI technologies. IT operations is the main area where AI is being adopted followed by customer service and fraud/risk management.

“For many organisations, the rapid rise of digital transformation has pushed AI to the top of the corporate agenda. However, as AI accelerates toward the mainstream, organisations will need to have an effective AI strategy aligned with business goals and innovative business models to thrive in the digital era,” noted Jyoti.

You can find a full copy of IDC’s report here (paywall).

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Cyrille Bataller (left), managing director of emerging technology innovation at Accenture, has been involved with artificial intelligence in some capacity for the better part of two decades. Following the 9/11 attacks in 2001, Bataller was part of the team which focused on the then-emerging area of biometrics for travel documents. These initiatives can be seen today as a forerunner to the passport e-gates used at many airports around the world.

Bataller refers to this as AI because it was ‘an example of a machine recognising people and performing an activity that was human in nature beforehand.’ Since then his work has encompassed further aspects of computer vision, natural language processing, as well as deep and machine learning. Prior to his speaking appearance at the AI & Big Data Expo in Amsterdam on June 19-20, AI News caught up with Bataller to discuss Accenture’s three-pronged vision for AI, ethical concerns, as well as the true depth of transformation available to organisations.

—

AI News: Hi Cyrille. Tell us about Accenture’s vision for AI.

Cyrille Bataller: Firstly, when we talk about AI, we talk about in a broader context, which is applied intelligence. Applied intelligence is combining AI, analytics, and automation, in order to transform businesses.

An example of how we transform what we do today is through automation, augmentation, and then innovation. Automation is quite straightforward and intuitive – we are able to automate repetitive, monotonous activities that at the moment we’ve had to do manually because there were no alternatives. We have come to a point where we can avoid having people work like robots; by using robots instead to do these repetitive tasks and let people focus on the higher value activities where creativity, empathy, and depth of understanding and context is required.

If we talk about augmentation, this means we can assist people with AI solutions to help them make the right decisions faster, to crunch large volumes of data and identify patterns that they would not have been able to otherwise, and to help them benefit from the collective experience of the organisations. You can take the 10 or 100 best experts, the best decisions, over the last year, and you can train a machine learning model that will assist junior employees to help them make the right decision benefiting from that collective experience.

We’re able to therefore free up budget and resources from the repetitive – insurance claims processing, trade settlement, customer service queries, and so on. What’s most common can be automated, and while we see the budget is not as much to handle these activities, we’re actually improving the outcomes on these activities as well because there’s a faster response time, it’s more accurate, more consistent, and that we can handle better peaks in demand. We can redirect both the people and the budget to new strategic activities – that will be innovation.

AI: How would this third leg of innovation work in a given use case?

CB: If AI systems behave somewhat like people, like employees – they can handle emails or business processes – suddenly you have access to a low cost and near infinite digital workforce that can complement the human workforce. For instance, passport control – you can call these robotic border guards and you can have as many guards as you like at the immigration hall and therefore handle peaks in volumes. You could have robotic video surveillance agents. When you have cities with 30,000 cameras, nobody’s watching them – they just bring out the right camera when a call comes in about an incident, or they bring out the right recording when there’s an investigation.

The reality is if you can have robotic video surveillance agents watch all cameras at once, you can have 30,000 cameras watched 24/7, and look for things that we as humans would look for, such as counting people, counting vehicles. Then suddenly you have much better situational awareness and radically new use cases.

AI: There are of course ethical concerns with regards to this, and plenty of examples where things haven’t gone right – for instance a lady in China was accused of jaywalking when the automated facial recognition system spotted her image on the side of a bus. How does this need to be resolved?

CB: I think AI is a technology at the service of people; it needs to be used intelligently, and systems need to be designed with people in mind, and with the best interests of people in mind. It is certainly not a technology that replaces people, or anything like that, but rather supports and augments them.

The example you gave is exactly why you need people in charge. AI systems automate lots of things, but final decisions need to be taken by people who have an understanding and a context that these machines are lacking, and will be lacking for the long term.

AI: You’re speaking at the AI & Big Data Expo around ‘responsible AI transformation’. Tell us a bit about what the session will hold both from a business and personal context.

CB: There are two key words in the title, which are ‘transformation’ and ‘responsible.’ Transformation because, as I said before, I’m very optimistic about this opportunity which holds enormous potential for society, for humanity in general, as well as for business, and organisations, and governments. At the same time it needs to be done responsibly – designing solutions in a way that are articulated earlier, freeing up people and money to focus on other value added activities rather than reducing head count and cutting costs. That’s an example of not doing it responsibly.

We try always to articulate our projects in a broader context that benefits organisations, customers, and employees. Everyone benefits from these solutions.

AI: What developments can we expect around artificial intelligence for the rest of this year?

CB: I think we’re at an interesting point. There are many organisations that are doing tests, pilots, proof of concepts – but in spite of such proof of concepts showing such promising results, there is a little bit of an education needed to scale and rationalise. We see some promising results when we test the pilot, but there’s a certain organisational resistance to move to scale. When it is moved to scale there aren’t as many examples as you would think when you look at the potential business cases of these solutions.

I think and I hope that we reduce this chasm over the next year. We’ll see more and more broad transformations happening and organisations reaping the benefits of these broad transformations – and that will see more organisations leverage the digital, AI-powered workforce.

 Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, , and Cyber Security & .

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A report from KPMG last month explored how artificial intelligence would look to influence business. Among millennial respondents, AI came out on top as the biggest technological priority, ahead of IoT and 5G. Yet among those polled who said they were business leaders, it was less of a priority compared with robotic process automation (RPA). The report noted, however, that investing in RPA can be seen as a smoother route to future AI investment.

IT giant Infosys offers many opportunities for companies looking to invest in artificial intelligence to improve workplace productivity as well as enhance the customer experience. The company notes that customers are at different stages of their journeys depending on sector and use case; and that therefore a more nuanced approach is required.

Ahead of the AI & Big Data Expo event in Amsterdam later this month, AI News caught up with Dr. N. R. Srinivasa Raghavan, Chief Data Scientist, Data and Analytics at Infosys (left), to discuss the initiatives the company is putting together, as well as the industries set to benefit most long-term.

—

AI News: Tell us about the initiatives Infosys is putting together in the realm of artificial intelligence – and what have customers been saying?

NR: Infosys is developing several industry-oriented AI solutions, frameworks, and workbenches that can help solve business problems. Specifically, AI is being positioned for enhancing the productivity of day to day operations of our clients through automation, enable decision making at tactical and operational levels, and for better customer experience.

Our customers are at different stages of embracing AI. Depending on their needs, Infosys is able to craft solutions driven by a global team of experts in data sciences and AI, and help deploy these solutions at enterprise scale.

AI: Do you agree that many companies/vendors are using ‘AI-washing’ (much in the same way as cloud-washing years ago) and overplaying their artificial intelligence capabilities? If so – what does this mean for users and the industry at large?

NR: We believe it is not as easy to be deceptive in AI as in cloud. AI is more of an outcome heavy tech than cloud, which is more of an infrastructure play. Therefore, one can verify if indeed pattern matching and predictive science of AI is behind any software. Also, the expertise of resources working on AI projects is discernible in the quality of the output.

Nonetheless, there is possibly AI-washing in some very narrow areas like automation. It calls for better validations and governance to be put in place to weed these out.

AI: Which industries do you think are going to benefit most long-term from these kinds of technologies? How important is it that several emerging technologies – artificial intelligence, edge computing, blockchain – can all talk to each other and make each other better to provide greater business outcomes?

NR: Banking, financial services and insurance (BFSI), retail, consumer product goods and logistics (RCL), and services, utilities, resources and energy (SURE), in that order.

While AI and edge computing need to work in synchronization for cases like real time predictions, integration with blockchain will be essential in the data layers of AI. Especially where the reliability, traceability and ‘sovereignty’ of data that is feeding AI is concerned.

AI: What is the most exciting use case you have seen with artificial intelligence to date? (can be business or consumer)

NR: AI for consumer experience, AI in risk/fraud detection, AI for predictive maintenance, AI for security and AI for business functions like HR, Finance etc. These will typically be cases where there is rich and reliable data available for the AI models to work upon, as well as areas where there is executive ownership and sponsorship.

AI: What advice would you give to companies looking to embark upon or modify their digital transformation initiatives?

NR: AI is largely seen as the harbinger for an automated/human-augmented workplace. It is therefore essential to have AI experts to be part of the blue printing for the next-gen digital enterprises. AI cannot be telescoped into digital transformation programs in large enterprises. It is no more a commodity tech, but a strategic one.

AI: What are you looking forward to most at the AI & Big Data Expo and what will you be looking to tell attendees while there?

NR: We are looking forward to hearing from other participants on their success and learnings in implementing AI within Big Data context. We are eager to share our own experience in pushing the frontiers for AI and its adoption within enterprise settings.

Want to learn more about topics like this from thought leaders in the space? Find out more about the Edge Computing Expo, a brand new, innovative event and conference exploring the edge computing ecosystem.

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