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The policies, roles, processes and controls an organization uses to direct, monitor and stay accountable for how it builds, buys and uses AI.

AI governance lets a company adopt AI quickly without losing control of risk, data, customer commitments or legal duties, and gives buyers, auditors and regulators evidence that AI use is deliberate, owned and monitored.

A 120-person software company names an executive owner for AI, builds an AI system inventory, approves an AI policy, adds a risk and impact check to its release process, and reviews AI incidents and metrics in a standing leadership meeting.

Policies do not make AI safe on their own: every control needs an owner, evidence and budget. Programs copied from a framework without adapting them to how the company actually uses AI tend to slow teams down without reducing real risk.

AI governance sets direction and accountability for AI across its life cycle: who approves a use case, which risks and impacts must be assessed, which controls apply, and how performance is monitored and reported to leadership. Common reference frameworks include ISO/IEC 42001 for a certifiable AI Management System, ISO/IEC 38507 for oversight by governing bodies, the NIST AI Risk Management Framework (AI RMF) and the OECD AI Principles. In the EU it also covers duties under the EU AI Act, such as role classification, AI Literacy measures and high-risk obligations. Day-to-day elements usually include an AI policy, an AI System Inventory, risk and impact assessments, supplier controls, incident handling and periodic review.