At U.S. Bank, the Real AI Bottleneck Is Decision Design

In the rush to adopt artificial intelligence, many organizations have focused first on tools, frameworks, and pilots. At U.S. Bank, a different problem sits at the center of its AI strategy: how decisions are designed and improved, not just how technology is deployed.

In a conversation on CXOTalk, Prashant Mehrotra, the bank’s Chief AI Officer, described that focus. Rather than launching technologies without a clear view of value, the bank starts by questioning whether an existing process is worth transforming at all, and if so, how to do it responsibly and at scale.

Across the banking industry, adoption has accelerated quickly. Surveys show that a growing share of U.S. banks report deploying or actively using generative AI in some capacity. Mehrotra’s emphasis, however, is not on adoption alone. It is on measurable outcomes, repeatable processes, and decisions that can move from pilot to production without stalling.

Build, Measure, and Decide Faster

Mehrotra said ideas for AI initiatives emerge from across the bank, both top-down and bottom-up. “We evaluate these ideas not just for the value they bring, but also for things like technical feasibility, managing the risk, aligning with the risk posture of the bank, and ensuring that we are doing these things in a transparent and communicative manner,” he said.