Case study 11 of 15

Data Governance Was the Foundation

AI initiatives kept exposing the same data classification, ownership, and access gaps.

James Staud · Anonymized field pattern — details generalized to protect specifics

Business challenge

Teams wanted to move fast, but production AI needed trustworthy data boundaries that didn't exist yet.

Retrieval patterns, data classification, identity integration, and source-based grounding were strengthened before more use cases were added.

How the work moved from request to production

↻ Feeds back into intake to improve the pattern or the governance around it

Approved tool categories and data-handling rules were enforced through identity and platform controls, not just policy documents.

The organization gained a real way to evaluate new AI use cases based on data readiness instead of enthusiasm alone.

Lesson learned

Bad data governance becomes bad AI governance — the model just makes the gap visible faster.