Generative AI, or GenAI, is taking the enterprise world by storm, supercharging digital transformation by enabling higher levels of human-machine collaboration than ever before. In the financial services world this can stretch from using AI-powered chatbots to enhance customer support, unlocking data insights to improve decision-making or automating processes to improve employees’ quality of work.
But adoption is not without its hurdles and demonstrating the return on investment of such nascent technology can be a challenge. It was this topic that we sought to explore in a recent roundtable hosted by Cognizant and Microsoft that brought together thought leaders from several leading financial services institutions.
Proving the business case
A common problem holding back adoption is that many of the benefits of GenAI – such as improved efficiency, productivity and time-savings – aren’t easy to measure in traditional organisational frameworks and may not be comparable across teams or departments. This complicates the ROI conversation within boardrooms where risk appetite is low.
St. James’s Place, the wealth management firm, is deploying AI to augment its 4,800 advisors in their face-to-face relationships with clients. “We’re trying to […] free up advisors to have more time to build trusted relationships, doing the emotional aspect of financial planning, while using GenAI to support the overall client experience,” says Ian MacKenzie, the company’s Chief Operations and Technology Officer. “One of our key measures is actually the amount of time we give back to advisor businesses.”
Of course, GenAI might be able to offer new ways of working, new sources of inspiration or different starting points to solving a problem. But it can be very difficult to translate those things into something like a cost saving that would then justify an investment.
“It’s not dissimilar to the problem we had with RPA [robotic process automation] ten years ago, when our team would tell me that we’ve saved 1.7 million man-hours of time – that’s a completely meaningless number,” says Jamie Ovenden, chief technology officer at Schroders.
Cutting through the hype
Proving ROI is nonetheless possible; it often just requires a different approach. What many tech leaders are finding is that the hype around GenAI means that executive-level interest in the tech is already high. But to cut through the hype, the C-suite needs to truly understand and experience the benefits of GenAI first-hand.
“The number-one topic [among the C-suite] is not about positive financial ROI; it’s to increase the level of predictability in your numbers and your delivery systems,” says Gabriel de Montessus, executive vice president of Worldpay’s Global Enterprise unit. “GenAI gives you the ability to drastically improve predictability – for you and your customers. And when you’re more predictable, you can better understand the future for your company.”
For Worldpay, the most immediate application of GenAI has been to increase the processing performance of payments. The reality is that, out of every 100 legitimate transactions globally, around 15 are mistakenly blocked by merchants or banks due to suspicions of fraud. This holds back commerce and produces frustration for all concerned.
“By trying to fight against fraud, we still block a huge number of valid transactions,” de Montessus explains. “But using these types of technologies allows us to be way more accurate in our analysis and massively reduce the number of false positives, improving performance for our merchants.”
The business case here is clear – and measurable: not only are merchants able to sell more products, payment providers like Worldpay (who take a fee on each transaction) benefit too, along with the positive outcome for the end-consumer.
Pioneers and sceptics
Growing boardroom interest in GenAI is no surprise. But it will be those businesses in the financial services sector that are willing and able to innovate that will be those most likely to see success.
“Financial services businesses around the world have shared characteristics – like risk profile, regulatory scrutiny, competitive intensity. But one of the differentiating factors is organisational culture and the mindset of innovation and experimentation,” says Piers Marais, chief product officer, Currencycloud, a Visa solution. “It’s that level of organisational-wide desire to go and experiment with new technologies that really has to be ingrained in the entire business. That, to me, is the big differentiator.”
But it’s not just internal people that need convincing. For David Fearne, global head of generative AI at Cognizant, speaking to a new client often involves a healthy amount of time “debunking the FUD”, and helping them to understand and delineate between consumer-grade GenAI (like ChatGPT) and enterprise-grade GenAI.
“Many people view generative AI with apprehension, perceiving it as an enigmatic and exotic technology,” says Fearne. “Yet, once you grasp its workings and learn how to align it with your enterprise’s risk and regulatory frameworks, it becomes a profoundly powerful tool. This understanding can liberate and expand how people think about incorporating it into their use.”
Convincing sceptics in the workplace, then, is key to realise ROI. Leveraging internal data can have a multiplier effect on adoption as it both back up business use cases but also demonstrate the impact the change is having, says Edward Achtner, head of HSBC’s Office of Applied AI division.
“The emphasis for us at HSBC is not to innovate for the sake of innovation, but to actually create value, on a global scale. Leveraging world class research, highly skilled multi-discipline teams that fundamentally understand customer need and organisation strategy is key,” says Achtner.
Workforce transformation also plays a vital role, across the entire bank, at HSBC. “We’ve introduced AI literacy pathways to ensure we have an engaged and informed workforce,” says Achtner. “To guide our thinking, we published our principles and standards for AI and big data use to help us establish our ‘North Star’, ensuring what we build, where we build and how we build not only delivers for our customers but adheres to these standards in a responsible and ethical way.’’
Such company-wide ambassador programmes can give employees a pathway to upskill themselves for the future, helping to get them on board with tools like GenAI. However, many financial institutions continue to employ analogue operating models that can pose challenges to the implementation and adoption of GenAI tools.
Visualising at the board level what a more digital operating model might look like with these technologies in play, and how this may create an end-to-end value stream, is a critical first-step for any bank looking to successfully navigate this generational disruption and deliver value to its end consumers.
To find out more about GenAI and its capacity to transform in financial institutions, click below
Generative AI, or GenAI, is taking the enterprise world by storm, supercharging digital transformation by enabling higher levels of human-machine collaboration than ever before. In the financial services world this can stretch from using AI-powered chatbots to enhance customer support, unlocking data insights to improve decision-making or automating processes to improve employees’ quality of work.
But adoption is not without its hurdles and demonstrating the return on investment of such nascent technology can be a challenge. It was this topic that we sought to explore in a recent roundtable hosted by Cognizant and Microsoft that brought together thought leaders from several leading financial services institutions.
Proving the business case
A common problem holding back adoption is that many of the benefits of GenAI – such as improved efficiency, productivity and time-savings – aren’t easy to measure in traditional organisational frameworks and may not be comparable across teams or departments. This complicates the ROI conversation within boardrooms where risk appetite is low.
