Deploying AI Inside a Bank Is Not an Engineering Problem — Paden Gayle, Applied AI Engineers
Banking AI deployments fail not from engineering flaws but from governance, data ownership, and audit gaps
“who will check the false exception before it gets to the next system?”
Paden Gayle argues that AI systems in regulated industries like banking routinely fail to reach production not because of model quality but because stakeholders—security, data owners, risk—surface blocking questions that were never resolved during design. She presents a four-category framework (data ownership, model risk, third-party dependencies, audit reproducibility) for surfacing these blockers in the first week before significant engineering investment is made. The insight is practically useful for any team deploying AI in compliance-heavy environments, though it is practitioner advice rather than a novel research or product announcement.