The Policy vs. Governance Gap
Most organizations have documented their AI governance posture. They have an AI use policy, a vendor assessment checklist, and a designated AI governance owner. What most organizations do not have is the operating cadence that makes governance real rather than aspirational.
We spoke with thirty legal operations leaders about their AI governance programs over six months. Ten described programs that were genuinely operating. Twenty described programs that existed on paper.
What the 10% Actually Do
Five practices consistently separated the organizations with operating governance from those with paper governance. First, a standing AI review cadence — the effective programs meet monthly to review active AI deployments against their approved use cases. Second, a deployment registry — every AI tool, every use case, every integration point documented and maintained.
Third, incident tracking — every time an AI tool produces an output that required material human correction, it is logged. Fourth, vendor monitoring — a mechanism for detecting when a vendor has changed its model or data processing terms. Fifth, a genuine no — the 10% have declined AI deployments.
The Accountability Structure
Effective AI governance requires someone with three things: designated time, budget authority, and the organizational standing to say no to a proposed deployment from a senior stakeholder. Most AI governance programs fail because they lack at least one of these three.
The budget component is underrated. Effective AI governance requires investment: in vendor assessments, in tooling to monitor deployed systems, in training for the lawyers who use AI tools. Organizations that treat AI governance as a zero-cost overhead activity get zero-cost governance results.