AI scales whatever data you feed it— including inconsistencies, bias, and stale records. Data governance is the difference between confident automation and expensive rework.
The minimum viable governance stack
- Lineage: Know which reports and models depend on each table and field.
- Access tiers: Separate public, internal, confidential, and regulated data with technical enforcement.
- Quality SLAs: Define freshness and completeness thresholds for features used in production models.
Stewardship that sticks
Assign domain stewards in finance, supply chain, and customer operations—not only central IT. Stewards approve definitions, resolve conflicts, and own remediation backlogs.
Questions to ask before any model ships
- What is the authoritative source for this label or metric?
- How do we detect drift when upstream systems change?
- Who approves training data that includes personal or payment information?
Governance accelerates AI when teams trust the catalog. Slowdowns usually mean unclear ownership, not too many rules.
Business outcome
Organizations with mature governance launch AI features 2–3× faster in regulated workflows because legal and security reviews become repeatable—not bespoke every sprint.