The banker is coming back, and AI is the reason

Yahoo Finance ·

JPMorgan Chase is raising its 2026 technology budget to nearly $20bn, with a meaningful share going to AI – and peers like Bank of America are on their heels at $14bn. While community and regional banks never had this kind of capital to invest, the technology shift underway presents an unprecedented opportunity that rewards relationships – after 30 years banks spent focusing on scale. Banks spent years chasing scale by cutting out the very thing customers want back: the relationship. Reports Robotics in Banking - Thematic Intelligence Reports Virtual Assistant for CaixaBank, S.A. - Use Case The gold standard of business intelligence. Experience unmatched clarity with a single platform that combines unique data, AI, and human expertise. The focus over the past 3 decades has been on modernizing banking via automation and stripping humans out. The branch manager – who knew your name, understood your family’s finances and picked up the phone when something went wrong – was reframed as a cost. The industry cut them out in favour of faster apps and smaller headcounts, until neobanks eventually killed the entire branch. Being frictionless by design, however, turned out to be a trade-off. It is fast and efficient – until you need local knowledge, something goes wrong, or you just want someone to talk to. It’s like designing a hotel that perfects the digital check-in but removes the concierge. This affected the bottom line: Even though neobanks capture 40% of new account openings , they still generate just $45 in annual revenue per user against $350 at traditional banks . Most neobank customers keep their savings, mortgages and investments at traditional banks, keeping a door open to return to the institution where there is a relationship the moment something big happens. As banks strived to digitalize more of their services and channels for years, the number of relationship managers steadily dropped. RMs, meanwhile, started losing more time into system toggling, documentation and internal process management. More than half of their time disappeared with work that has nothing to do with actual client dialogue. Most banks saw AI as a means to take some burden off the shoulders of the RMs. They layered it onto the same fragmented systems rather than rebuilding the operating model underneath. Now AI is just one more tab RMs have to keep open. A 22-year-old is no different to their grandparents here; they still want to speak to a human. The channel preference has changed, but human need hasn’t. And RMs need the same thing customers do: their time back. Fortunately, AI can enable the shift back to the relationship in a way previous technology hasn’t been able to. Every technological development before stripped the human out, reduced the touch and cut the cost. AI runs the other way. It automates the admin that turned relationship bankers into form-fillers and gives that time back to the customer. When AI commoditizes pricing, rates, and basic service – and it will – those stop being a reason to pick one bank over another. Customers will choose based on the quality of the relationship and the values of the institution instead. Fixing this starts with the plumbing, not the model. Every tool the RM touches needs to draw from the same source of truth. That means the core system, the CRM, the origination platform, and the risk engine all reading from one place. Only once that foundation exists should banks introduce AI, so it inherits one full picture instead of five fragmented ones. Do that, and the RM’s screen changes. Instead of five logins and five partial answers, they see one view. It shows the customer’s full portfolio, their recent transactions, and what they’re likely to need next. The RM walks into every conversation already knowing what the client will ask. The system remembers it, so the RM doesn’t have to. That kind of foresight used to be a private banking luxury. A small client roster justified an army of analysts pulling the picture together by hand. A unified architecture makes it standard, not bespoke. This technology no longer requires a JPMorgan-sized budget. Modern, cloud-based architecture is modular, so regional and community banks can build the same foundation piece by piece. For the first time, they don’t need a $20bn budget to get there. The RM already knows what the customer needs. What’s been missing is the full picture. Context got lost somewhere between the core system, the CRM, the origination platform, and the risk tools. So, the banker had to improvise. It’s the same fragmentation that buried them in open tabs in the first place. That’s the foundation this piece keeps coming back to: not a smarter model, but one unified view. Give every RM and every system that touches a customer the same picture: who they are, what they hold, what they’ve said, and what’s already been decided on their behalf. Once that picture exists, the RM walks into every interaction already knowing what the customer will ask. Agents can act on it. Compliance can audit it. For the first time, the customer feels genuinely known. None of this requires a transformation program. It’s a choice about where to start. Map one customer journey: a mortgage application, a scam complaint, or an account review. Fix every system handoff that loses context along the way. This isn’t a grand AI strategy. It’s one broken journey, rebuilt properly. JPMorgan and Bank of America can spend $20bn modernising their technology. That budget buys speed and infrastructure. It doesn’t buy back the relationships the industry gave up over 30 years of chasing scale. It gave up the branch manager who knew your name. It gave up the RM who had time for the client instead of the systems behind them. It gave up the human who picked up the phone when something went wrong. Community and regional banks that move now can still write a different ending. They can rebuild the foundation, unify the data, and layer AI on top of it. Done right, AI frees up the RM instead of becoming one more tab on one of their three screens. It gives every banker the context they need. It makes the relationship the product again, and it will define retail banking for the next decade. Jouk Pleiter is Founder and CEO of Backbase

AI 시장 분석

JPMorgan Chase가 2026년 기술 예산을 거의 20억 달러로 높이고, AI에 의미 있는 비중을 할애하고 있다. 이에 따라 은행은 이전과 같이 고객과 관계를 구축하는 것을 중시하게 될 것이다. 이는 고객이 빠른 앱과 작은 인원으로 인해 잃어버린 고객 관계를 다시 찾을 수 있게 해주며, 고객이 원하는 것을 다시 제공할 수 있게 해준다.

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DYAX 전담 분석

JPMorgan Chase가 2026년 기술 예산을 거의 20억 달러로 높이는 것은 AI에 대한 투자를 의미한다. 이는 은행이 이전과 같이 고객과 관계를 구축하는 것을 중시하게 될 것이라는 것을 의미한다. 이전에는 은행이 고객과 관계를 구축하는 것을 중시하지 않았고, 대신 빠른 앱과 작은 인원으로 고객을 유치하려고 했다. 그러나 이러한 접근 방식은 고객이 원하는 것을 제공하지 못했고, 고객이 은행에 대한 신뢰를 잃었다.

AI가 생성한 분석으로 투자 자문이 아닙니다.

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