AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
Model governance is the set of policies and controls for approving, monitoring, and documenting the AI models an organization uses. As AI spreads across a company, governance is what keeps it auditable and accountable. Its absence is what turns a promising pilot into an unmanaged risk.
Model governance is the change management process applied to AI. You would not roll out new enterprise software with no documentation, no approvals, and no rollback plan. AI models deserve the same rigor.
A bank runs 14 AI models across credit, fraud, and marketing. Model governance ensures every model has a named owner, a documented version, a performance benchmark, and a review schedule. When one drifts, the process knows who to call.
As soon as more than one AI model is in production. The cost of governance at that scale is low. The cost of ungoverned AI at scale, when a model makes a systematic mistake, is high.
Model governance should not slow down AI experimentation in the PoC phase. Apply it to production systems, not every internal demo or prototype still being evaluated.
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