Every bank in America is being told the same thing: adopt AI or get left behind. Boards are asking about it. Vendors are selling it. Regulators are watching it. But here’s what almost no one is talking about—the majority of bank AI initiatives are stalling not because of the technology, but because the data underneath isn’t ready to support it.
This session tackles the uncomfortable truth that most financial institutions face an AI readiness gap—a structural disconnect between their AI ambitions and the state of their data. Banks sit on decades of extraordinarily valuable information: loan officer notes, call recordings, transaction patterns, compliance documentation, and customer interaction histories. Digital channels are generating even more of it every day—mobile session data, chatbot transcripts, onboarding drop-off patterns, and real-time behavioral signals. Fintechs don’t have the institutional depth. Big Tech doesn’t have the regulatory context. But most banks can’t use their own data either, because it’s siloed, ungoverned, and locked in formats that modern AI tools can’t consume.
We will share a practical framework for diagnosing and closing the AI readiness gap. This is not another session about what AI can do. This is the session about what your institution needs to build first.
