What it means
AI governance is the set of structures, roles, policies and processes an organisation uses to decide how AI is selected, developed, used and monitored responsibly. It covers who decides, on what basis, with which safeguards, and how accountability works when things go wrong.
Real-world example
A state ministry sets up an AI board with members from IT, data protection, legal, staff representatives and the business units. Every proposed AI use case goes through a short intake form, is classified by risk, and is either approved, approved with conditions or referred for a full assessment. A public register lists approved systems.
Why it matters for AI adoption
Without governance, AI adoption tends to happen through uncoordinated tools and individual decisions, creating hidden risks and duplicated costs. Good governance is proportionate: light for low-risk uses, thorough for high-risk ones, and designed to enable responsible adoption rather than block it.
Often confused with
AI compliance
Compliance means meeting specific legal and regulatory requirements, such as the EU AI Act and GDPR. Governance is broader: it includes compliance but also covers strategy, ethics, risk appetite, roles, quality and organisational values. An organisation can be compliant on paper yet poorly governed.
Questions to ask
- Who decides whether a new AI use case may go ahead, and on what criteria?
- Who is accountable for each AI system after it goes live?