According to Forbes, a new study from Cornerstone Advisors and Klarivis reveals most banks are completely unprepared for AI success. The research on Data EQ (execution quality) shows only a quarter of financial institutions qualified as “high performers.” That means 75% of banks are building AI strategies on fundamentally flawed data foundations. This comes despite previous research showing banks scored respectably on Data IQ measures like governance and centralized warehouses. The gap between having good data and actually using it effectively is massive, and it’s where AI initiatives are failing.
The brutal data EQ reality check
Here’s the thing: AI doesn’t create insights from nothing. It amplifies what you already have. Feed it messy, incomplete, or siloed data, and you’ll get exactly what you deserve—messy, incomplete, siloed results. The study identifies five functional areas where Data EQ matters, and if you’re scoring low in any of them, your AI initiatives related to that function will fail. It’s that simple. Banks are excited about chatbots and machine learning models, but nobody wants to talk about their data mess. And that’s exactly why so many AI projects are doomed before they even start.
It’s culture, not just technology
What really separates the high Data EQ performers? It’s not the size of their AI budget or the sophistication of their tech stack. It’s culture. High performers have executives who actually believe data is a strategic asset. They foster environments where people use data daily to make decisions. You can’t “AI” your way out of a culture problem, and you definitely can’t buy your way out with the latest vendor solution. This is where many organizations hit a wall—they think technology will solve what’s fundamentally a people and process problem.
How to actually fix this
The report offers five practical steps, but here’s what really matters. First, create a unified performance model across your entire institution. Without this common data language, your AI agents are flying blind. Second, deploy operational intelligence platforms that let business users ask questions and get answers—not wait weeks for reports. Third, tie data quality directly to business outcomes. Show executives how poor Data EQ leads to missed growth goals and compliance risks. Fourth, plan AI use cases around your actual data maturity. Don’t build an AI copilot if your product mapping still uses codes from 2003. And fifth—this is crucial—deal with the people problem. Identify the “impeders” who don’t believe in data’s strategic value. They need to change or be changed.
An expensive lesson coming
Community banks and credit unions rushing into AI without improving their Data EQ are about to learn the oldest lesson in computing: garbage in, garbage out. Only now it’s happening at faster speeds and greater scale. The institutions that will win in the AI era won’t be the ones with the biggest AI budgets. They’ll be the ones who did the unsexy, unglamorous work of getting their data house in order. Before you ask your board to approve that shiny AI strategy, you better know your Data EQ score. If you don’t, you’re basically setting money on fire. The full research is available in Improving Your Financial Institution’s Data Execution Quality (EQ) and builds on their earlier Data IQ research.
