Enterprise data rarely arrives with consistent names, ownership, and context. AI can propose mappings, but an unchecked suggestion can spread semantic errors faster than a manual process.
What made it hard
The same label can mean different things across organizations, time periods, and analytical uses. A reliable solution therefore has to preserve source context and distinguish recommendation from approval.
How I approached it
I combined AI-assisted suggestions with governed reference data, provenance, confidence signals, human review, validation rules, and re-checks before downstream use. The design treated uncertainty as operational evidence rather than hiding it behind a single answer.
Selected impact
The approach created a reusable control model for AI-assisted standardization while keeping business-sensitive operational metrics out of the public record.
Lesson I still use
AI reliability begins with the evidence and controls surrounding the model—not with the model alone.