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For an industry long known for its caution with unproven technologies, the race to
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My company, Codat, recently had the opportunity to speak with several of the largest banks in the world, and we asked them to rank how they think about the priority and impact of several trending topics. The result was unanimous: AI stood out as the clear leader, ranking highest in both priority and impact.
The opportunities that AI capabilities bring to the banking and finance world are truly exciting. From Fargo, the virtual assistant app by Wells Fargo, to JPMorgan’s ChatCFO, leading U.S. banks are already proving they are at the forefront of cutting-edge technology. One of the most established financial institutions in the world, Morgan Stanley, has even rolled out its own in-house generative AI tools, showing that the appetite to implement this technology reaches far beyond nimble fintech startups.
It makes sense because the reality is that AI is a natural ally to banks. The most critical element in developing any in-house AI technology is having stores and stores of data — which is where incumbent banks have an advantage over newer fintechs and neobanks.
Here’s the issue: AI is not necessarily a golden ticket to higher profits. While a more efficient operation might look great on a balance sheet, what good does it do if customers don’t feel the benefits?
Without a focus on client value, banks risk implementing high-cost AI systems that may improve efficiency but fail to make a tangible difference to the customer. There’s already a widespread belief among consumers that banks are using AI to prioritize their own interests over those of customers,
I’ve seen that some banks are beginning to pilot AI large language models in customer-facing roles. With that comes a growing concern from customers that they might lose the ability to speak to a real person, fearing that service will deteriorate as AI takes over.
This skepticism extends beyond consumers to finance managers and other stakeholders who increasingly scrutinize whether AI initiatives prioritize customer outcomes. Without a deliberate focus on client value, banks risk investing in high-cost AI systems that improve back-office efficiency but fail to make a tangible difference in customers’ lives. Worse, this misalignment could erode brand trust and customer loyalty over time.
Don’t fall for the productivity illusion.
Productivity metrics — while important — can be deceptively comforting, creating the illusion of success while ignoring the core value proposition of banking: serving customers.
These metrics must be coupled with customer-centric success metrics like retention rates, satisfaction scores and lifetime value. These are the indicators that reveal whether customers actually benefit from AI-driven transformations.
The best use case for AI in banking is to create a human-led customer experience empowered by AI intelligence, rather than letting AI intelligence lead the interaction. Traditional banks are known for their stellar customer service — and part of maintaining that service is ensuring a human touch remains at the forefront, supported by the efficiencies and insights that AI brings.
Take Capital One. They’ve positioned their AI and machine learning with the phrase “banking with humans at the center,” which tells me they understand the fears that customers have around AI and are showcasing the fact that their service is still led by humans.
AI isn’t inherently good or bad; its value depends entirely on how it’s used. For financial institutions, this means shifting the focus from internal productivity gains to customer outcomes. AI should be a tool for building trust and loyalty, not just trimming costs or speeding up internal processes.
The future of banking will be shaped by how well-financial institutions leverage AI to serve their clients, not just their bottom lines. The banks that succeed will be those that understand this simple truth: Customer satisfaction, not numbers on a spreadsheet, is the ultimate measure of success.