Hello and welcome to Eye on AI. In this newsletter…JPMorgan Chase is the top AI bank (again)...Microsoft debuts its AI agents…while tensions between it and OpenAI grow…and the worrisome results of war gaming AI competition.
Lots of companies make vague claims about how much they're investing in AI or the benefits they're seeing from deploying the technology. An increasing number of public companies have also begun pointing to AI as a risk factor in their SEC filings. But much of this is either marketing spin or boilerplate legal CYA. It’s not easy to tell, from the outside, how well any particular business is doing when it comes to AI deployment.
That’s why it's always refreshing to see Evident Insights’ annual AI Index, which ranks 50 of the world’s largest banks on their AI prowess. Evident uses 90 publicly available data points, drawn from job postings, LinkedIn profiles, patents, academic papers, financial filings, executives’ public statements, and more, to come up with its ranking. While it’s not a perfect methodology, it is a lot more objective than most other gauges.
Running harder to stand still
JPMorgan Chase tops Evident’s ranking, as it did last year and the year before. In fact, all of the top four—which is rounded out by Capital One, Royal Bank of Canada, and Wells Fargo—have maintained their positions from last year’s ranking. But this fact belies what is actually going on, Alexandra Mousavizadeh, CEO of Evident Insights, tells me. Most of the banks in the index improved their overall scores, and the average scores have climbed significantly over time. NatWest, the 18th bank in the Index, for example, scored more points this year than the number 10 bank in last year’s ranking.
“The leaders are leading more, but the pace of growth has doubled since last year,” Mousavizadeh says. Those that made the investment to have their data cleaned up and ready for AI applications and that have invested in hiring AI talent and deploying AI solutions are moving much faster than those who are further behind on these tasks.
AI's 'Flywheel'
This is evidence of AI’s “flywheel” effects. And it’s why those companies that have wanted to take a wait-and-see approach to the AI boom, wary of reports about how difficult it is to generate return on investment from AI projects, may be making a big mistake. It can take time and significant investment for AI projects to begin to pay off, but once they do, these AI deployments can create a self-perpetuating and accelerating cycle of benefits. That flywheel effect means that it can be impossible for late-movers to ever close the gap.
Or nearly impossible. A few banks have managed to jump up in the rankings this year—HSBC, Canada’s TD Bank, and Morgan Stanley all managed to break into the top 10 for the first time. In the case of HSBC, tying its AI efforts more tightly together and making some key hires helped, Mousavizadeh says. For Morgan Stanley, partnerships with OpenAI and Nvidia helped boost its position.
But JPMorgan Chase remains well ahead of its peers largely because it started investing in AI much earlier than others. It hired Manuela Veloso, a top machine learning researcher from Carnegie Mellon University, back in 2018 and stood up its own advanced AI research lab. In 2019, its then-chief data and analytics officer championed a centralized data and AI platform to move information into its own AI models much faster than it could before. It was an early adopter of generative AI models too and is now pushing a bespoke generative AI tool out to 140,000 employees. It is also making all its employees complete an AI course designed to equip them to use the technology effectively. Critically, it says it's starting to see value from this investment—and unlike most companies, it is putting some hard numbers against that claim. The company is currently projecting it will see $2 billion of “business value” from AI deployments this year.
Putting numbers behind ROI
While “business value” may still seem a bit wishy-washy—it's not exactly as concrete a term as ROI, after all—putting actual dollar figures out there matters, Mousavizadeh says. That’s because once a bank puts numbers out, financial analysts, investors, and regulators will push for further transparency into those numbers and also hold the bank accountable for meeting them. That, in turn, should up the pressure on other global banks to start doing the same. (One other bank, DBS, has said it had seen $370 million in “economic value” from a combination of additional revenue, cost savings, and risk avoidance, thanks to AI.)
While currently Evident Insights only ranks financial institutions, these patterns—with today’s winners, continuing to win, and increasingly publishing real stats—will likely be repeated in other industries, too. Those waiting on the sidelines for AI to mature or prove itself may find that by the time the evidence of ROI is clear, it is already too late to act.
With that, here’s more AI news.
Jeremy Kahn
jeremy.kahn@fortune.com
@jeremyakahn
