Before chasing AI, Bank of America wants banks to fix their data first

A lot of the conversations around artificial intelligence tend to circle back to one question: which institution has built the smartest, most capable model?

Bank of America argues the model quality isn’t the point; an AI feature is only as reliable as the data behind it. That reliability determines whether AI can be trusted to support decisions or should be kept out of high-stakes ones entirely. 

Bank of America’s thinking on AI comes into focus through three perspectives: Matthew Davies, Head of Global Payments Solutions at Bank of America’s warning about fragmented data, EricaAssist’s role as an employee copilot grounded in years of client history, and CEO Brian Moynihan’s caution toward frontier models. All point to the same philosophy: AI should be treated as a supportive step in the process, not the one making high-stakes decisions. 

Capital misallocation is the real risk

Bank of America has built its AI strategy to avoid the trap of treating AI as a shortcut to efficiency, an approach that often results in costly, misguided AI investments. Davies notes that the pressure every company now feels to adopt AI fast or risk falling behind is itself the real danger. “The biggest risk and challenge is misinvestment rather than underinvestment,” he says. 

Bank of America has traced that misinvestment risk back to fragmented data scattered across ERP systems, treasury platforms, bank portals, and acquired businesses. Because these systems speak different languages, even the most advanced AI layered on top struggles to reconcile the underlying information. Davies emphasizes that without high-quality, standardized data, there’s no real foundation for automation, forecasting, or any AI solution built on top of it. Tearsheet’s recent reporting on Intuit Credit Karma echoes the same idea. Rather than building standalone AI assistants, the company built them on a shared view of a customer’s financial life, recognizing that better AI starts with better data and context.

For Bank of America, improving data quality delivers value long before any AI enters the picture. Standardized data alone reduces the manual reconciliation, duplicate entries, and reporting errors that consume hours of finance teams’ time every week. Davies outlined the bank’s order of operations: standardize the data first, automate repetitive tasks next, and only then pursue bigger AI initiatives.

EricaAssist: Inside Bank of America’s own data-first playbook

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