Practical post for engineers who've hit the wall where an AI proof-of-concept works on clean data but can't connect to the legacy systems that hold actual production data. Disclosure: I work at Ailoitte , which builds AI integration layers connecting legacy infrastructure to production AI. Sharing what the engineering actually looks like. Why does AI work in the demo but break on production data? AI models expect structured, consistently formatted data. Legacy systems — ERPs, mainframes, proprietary CRMs, on-premise databases — store data in formats built for the system's internal logic, not for external consumption by modern APIs. The demo works because test data is clean and pre-formatted. Production data is messy, inconsistently structured, and often accessible only through interfaces that predate REST. This is an integration problem, not an AI problem. The three layers of a legacy AI integration Layer 1: Data extraction Getting data out of the legacy system.…