The chief executive of Barclays says artificial intelligence is changing financial operations at a structural level — but warns that lasting results won’t come from shortcuts.
In a new interview with Bloomberg, Barclays CEO C.S. Venkatakrishnan responds to a question about an MIT study showing that 95% of firms adopting AI have failed to deliver profits.
According to Venkatakrishnan, integrating AI across large institutions requires not just new tools but also the conviction to reshape a firm’s workflows.
“So there’s technology at one side, and there’s making it available to our staff, which we have done. And then there is changing end-to-end business processes and doing them through the lens of AI. That is the difficult part. And that’s the part that requires a lot of discipline and commitment. And that’s what we’re trying to do.”
Venkatakrishnan warns against the industry’s rush to deploy models without redesigning the systems that support them.
“You have to be selective and patient. And I think you have to be realistic. So I think that comes into the selectivity. You just can’t take a quarter of a process, make it AI-enabled and the rest of it not.”
He says experimentation alone won’t deliver meaningful gains unless employees understand how AI can improve their work. Venkatakrishnan adds that success depends on reshaping legacy systems, not layering AI on top of outdated workflows.
“You have to give it in the arms of people so that they understand what the potential is, and they can, in their own small ways, improve their lives, like we’ve all done with the internet, like we’ve all done with a personal computer. There are giant processes, which are true in big banks, that you have to fundamentally reshape.”
Venkatakrishnan’s sentiments about the enterprise use of AI echo the findings of the MIT study. According to the research, enterprise adoption initiatives fail to generate returns because companies use AI tools that are not suitable for their specific needs.
The study also finds that nearly two-thirds of projects never make it beyond the pilot phase, and the third reason is that companies are often deploying AI in the wrong places.
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