Deploying AI Inside a Bank Is Not an Engineering Problem — Paden Gayle, Applied AI Engineers

This post details a practical framework for deploying AI systems within regulated environments like banks, focusing on the non-engineering hurdles that prevent production. It emphasizes identifying and engaging with decision-makers (security, data owners, risk reviewers) early in the process, shadowing their work to understand existing controls, and iteratively drafting and shrinking workflows to meet their requirements. The core contribution is a structured approach to gaining approval by starting with the minimum necessary authority for an AI agent, building a comprehensive evidence pack (intended use, data lineage, failure modes, segment performance), and establishing clear ownership for unresolved approval decisions. This method aims to de-risk AI deployments by making the approval process an integral part of the engineering lifecycle, rather than an afterthought.