AI governance is the set of decision rights, accountabilities, policies and oversight arrangements that guide how an organisation develops, buys and uses artificial intelligence. It makes clear who can approve a use, what limits apply and how concerns are escalated throughout a system’s life.

Good governance begins before a system is chosen. Leaders should ask whether AI is appropriate for the task, what people or services could be affected, and whether the organisation can manage the resulting risks. The answers shape the permitted purpose, acceptable level of risk, review requirements and conditions for stopping use. Governance should also account for systems bought from suppliers and tools introduced informally by staff.

Senior executives discussing strategy around a boardroom table. — Effective oversight gives decision-makers clear information and authority to act.

Accountability needs a person

Committees can set policy, review evidence and resolve issues, but accountability cannot live in a committee. Every deployed AI system should have a named human being who owns its use. That owner needs enough authority to question performance, request changes, suspend use and make sure incidents reach the right people. Naming someone without giving them access to evidence or decision-making power creates only paper accountability.

At organisational level, the board remains ultimately responsible for oversight of risk. Executives translate that responsibility into a workable governance structure: who approves high-impact use, who monitors compliance with internal policy, who checks technical performance and who can intervene. A system owner is not expected to do every task personally, but should know which teams perform them and remain answerable for the system’s operation.

Governance and compliance

Compliance asks whether an organisation meets a particular legal or policy requirement. Governance is broader. It sets the organisation’s objectives, risk appetite, decision rights and oversight for AI, including areas where rules are incomplete or where more than one law applies. Compliance evidence can inform governance, but a completed checklist does not prove that a system is suitable, fair or under effective control.

Useful governance is proportionate and connected to real decisions. It records why a system is used, what risks were accepted, who can change its purpose and what signals trigger review. It also gives workers a route to report unexpected behaviour without needing to prove harm first. When responsibilities, thresholds and escalation paths are explicit, an organisation is better placed to learn and respond as a system or its context changes.