Governance terms leaders must know
Know human-in-the-loop (mandatory review of consequential output), shadow AI (ungoverned employee use), MLOps (reliable production operation), and oversight (defined rules and accountability). Only about 28% of organizations have the last one.
The vocabulary of safe deployment
Governance terms describe the controls that make AI safe: a human reviewing consequential decisions, sanctioned tools that prevent shadow AI, and the operational discipline (MLOps) to run models reliably. Defining them is what the 28% do differently.
The gap between the 78–88% of organizations using AI and the ~28% with defined oversight is a governance-vocabulary gap. Human-in-the-loop means a person must review consequential AI output before it acts, the baseline control against hallucination-driven harm. Shadow AI names the risk of employees using ungoverned public tools, solved by offering sanctioned alternatives plus policy. MLOps is the discipline that keeps deployed models reliable over time. Leaders who can name and require these controls are the ones whose AI deployments stay safe and measurable.
Frequently asked questions.
Where should governance responsibility sit?
With a named, accountable owner, not a diffuse committee. Effective AI governance assigns clear responsibility for policy, review, and monitoring, backed by an executive sponsor. The absence of a single owner is a common reason the 72% without defined oversight never close the gap.