Ai-Projects
Why most AI projects fail in regulated industries
The demo trap
Every AI project starts the same way. Someone shows a demo on clean data. The room gets excited. Budget gets approved. Then the project hits real data and everything stops.
Demos work because they cheat. The data is curated, the edge cases are removed, and the integration layer is a mock. In healthcare, HL7 messages arrive malformed half the time. FHIR resources are missing required fields. EHR exports use undocumented formats that vary between installations of the same vendor. In finance, schemas change between API versions without notice, legacy core banking systems don’t expose APIs at all, and regulatory reporting formats differ by jurisdiction, sometimes by state.