The widening AI gap — and what it means for community banks.

The top-10 are accelerating 2.3× faster than the rest.

Year-over-year, the top-10 banks in the Evident Index added an average of +7.0 points to their overall AI maturity score. The bottom-10 added +1.3 points. The Index average was +3.7 points. Said plainly: the institutions already leading are extending their lead disproportionately.

JPMorganChase (79.0) and Capital One (78.1) retain the top two positions for the third consecutive year — and the gap that once separated them has effectively halved. Royal Bank of Canada moved to #3. Commonwealth Bank, Morgan Stanley, Wells Fargo, UBS, HSBC, Goldman Sachs, and Bank of America round out a top-10 that moved as a cohort.

Talent is the flywheel.

Across the 50 banks tracked, the AI talent pool grew +25% year-over-year — the largest spike Evident has recorded. Without exception, the top-5 banks expanding their AI talent were all US players: Capital One (via the Discover acquisition), JPMC, Citigroup, Bank of America, and Wells Fargo.

Talent volume correlates almost linearly with documented use cases. Top-10 banks by talent disclosed roughly twice as many AI use cases as the wider Index average. This is not coincidence. It is the flywheel: talent builds use cases, successful use cases justify more talent, more talent produces more use cases.

Community banks cannot hire their way to parity.

Here is the uncomfortable arithmetic. A community bank with 50 full-time employees cannot win a bidding war with Capital One for an AI engineer. The top 50 banks are absorbing the AI talent pipeline before community institutions get a seat at the table.

But parity on headcount was never the right target. Community banks do not compete with JPMC on scale. They compete on speed, proximity, and accountability to a specific community of members. The right question is not “how do we match Capital One's AI spend?” It is “how do we deploy AI in the Tuesday morning workflow of our five-person operations team before our peer across town does?”

The numbers, in one table.

What community banks should do about it.

Three moves, in order. First, stop benchmarking against the top-10. The maturity distance is now so large that the comparison only produces paralysis. Benchmark against your community-bank peer group, which the FDIC Quarterly Banking Profile publishes every quarter, free.

Second, invest in your existing people first. The bank that teaches its tellers, its loan officers, and its compliance team to use AI for their own workflows will see more operational lift than the bank that hires one expensive AI director. The Evident data confirms it: talent volume drives use cases, and talent does not have to mean new hires.

Third, pick one workflow and measure it. The Evident Index reports that only three banks in the top 50 — BNP Paribas, DBS, and JPMC — can currently report both present and projected ROI across their full AI use case portfolio. The gap between “we are doing AI” and “we can prove AI is working” is enormous even at the top. Community banks that measure will outperform community banks that do not.

Where The AI Banking Institute fits.

The Institute’s approach is community-bank-scaled from the first conversation. Bankers earn certifications that travel with them; institutions enroll a department or build a recurring cohort cadence that every new hire inherits. When a team wants a coach alongside the program, three optional Advisory shapes pair with certification — never replace it. Your bankers stay the builders.

None of this requires you to hire an AI engineer. It requires a free 45-minute Executive Briefing, an honest look at where your institution actually sits, and three specific next steps you can start on Monday.