AI governance is the set of decisions, boundaries and records that determine how an organisation is allowed to use AI. In practice it answers four questions: what AI may be used for, what it must never be used for, which data may cross into it, and who is accountable when it is wrong. Good governance speeds adoption up rather than slowing it down, because people stop guessing where the line is.
Why it matters for a UK business
Without it, every AI decision in the business is being made by whoever happens to be using the tool that day. Governance is what turns a hundred private judgement calls into one organisational position that a regulator, a client or an insurer can be shown.
What it looks like in practice
For a business under 200 people it is three things written down: which data may go near a model and which may not, which decisions a person must sign before they take effect, and who owns the answer when something goes wrong. It fits on a few pages.
What to do about it
Find out what is already in use, decide the data boundary, write the policy that reflects the decision rather than an aspiration, then pick a framework to align to if a customer or investor is going to ask. In that order, it takes weeks.
Related terms
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How we deliver AI governance → · All 22 terms in the glossary · The resource library
If this is the term that has come up in your business and you want it worked through against your own situation rather than in the abstract, that is a conversation, not a page.
Speak to ChrisWritten by Chris Duffy. Last reviewed .