The data: JPMorganChase retained the top spot in the Evident AI Index, leading an annual ranking of 50 global banks' artificial intelligence capabilities for the fifth year in a row. 

Capital One and Royal Bank of Canada held second and third place.

How we got here: The index scores banks on AI talent, research and patent activity, leadership communication, and transparency around deployment.

Rankings are also tied to whether AI spending produces measurable business results. Banks that can’t demonstrate ROI risk falling further behind their competitors because they can’t justify ongoing or growing AI budgets, Evident told Bloomberg. 

Why it matters: The focus of bank AI investment is shifting from building capabilities to putting them to work in business processes where banks can demonstrate a return. 

Wells Fargo illustrates what that looks like: It moved up 11 spots in AI leadership and into fifth spot from sixth last year. 

• The bank trained AI agents on 15 years of past loan decisions so they could learn how its underwriters evaluate applications, per Bloomberg. 
• Its agents can then analyze new applications and recommend a decision, while a human underwriter retains final approval. 
• That allows Wells Fargo to automate part of a labor-intensive process without handing the entire lending decision over to AI. 

Banks are shifting their focus from building the technical foundations for AI toward putting it to work. Across the banks Evident studied, roles that help deploy AI within banks grew 21% over the past year across the 50 banks, the fastest growth of any AI job category. Data engineering roles fell 3% over the same period, while AI development and software implementation roles grew more slowly. 

Implications for banks: JPMorganChase, Capital One, and RBC may have held onto their leading positions, but competitors can still gain ground.

The next competitive divide will be less about who has rolled out AI capabilities and more about how deeply banks can embed the tech into everyday operations. Banks that can redesign underwriting, servicing, marketing, and other workflows around AI have a clearer path to turning that technology into faster decisions or lower operating costs. 

Doing that at scale requires talent that can bridge the gap between the technology and the business. 

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