Definition
AI governance is the framework of policies, roles, processes and controls that a company uses to decide how artificial intelligence is built, bought, deployed and monitored. It answers practical questions: who approves a new AI use case, what data may be used, how risks are assessed, how performance and incidents are tracked, and who is accountable when something goes wrong. AI governance is to AI what financial controls are to money.
In a company, AI governance usually starts small: an inventory of every AI system in use, including the ones employees adopted on their own, a simple risk classification, an acceptable use policy, and a review step for anything touching customers or personal data. Larger organisations add a cross-functional committee, model documentation, vendor due diligence, evals before launch and monitoring after. The goal is speed with accountability, not a bureaucracy that pushes teams to hide their AI use.
Regulation has made AI governance a legal requirement rather than a best practice, with the EU AI Act imposing obligations on providers and users of high-risk and general-purpose AI, and standards such as ISO/IEC 42001 giving a certifiable management framework. The misconception is that governance slows innovation. In practice, clear rules let teams ship faster because they know what is allowed, and they protect the company when a vendor or a model fails.
In practice
A retailer with 100+ employees discovered 14 AI tools in use across teams, none reviewed. It set up a one-page intake form, a monthly review and a list of approved tools. Adoption went up, not down, because people finally knew what they were allowed to use.
Why it matters
AI governance is how you stay in control of decisions your software is now making. Regulators, customers and insurers will increasingly ask for it, and it is far cheaper to build early than after an incident.
Frequently asked questions
- What does AI governance include?
- An inventory of AI systems, a risk classification, policies on acceptable use and data, a review and approval process for new use cases, documentation of models and vendors, evaluation before launch, monitoring in production, incident handling and clear accountability for each system.
- Do small companies need AI governance?
- Yes, in proportion to their risk. A small company needs a short acceptable use policy, a list of approved tools, a rule about personal and confidential data, and a named owner for AI decisions. That is enough to avoid the most common mistakes and to satisfy customers who ask.