Governed AI is AI that runs inside clear limits, with a named owner, human approval at the points where risk calls for it, and a record of every decision it takes. The aim is AI you can explain to a board, an auditor or a regulator after the fact, not just AI that works in a demo.
George McKenna, Chairman and CTO, ETT Group. Updated 15 October 2026.
In practice, governed AI comes down to four things being true before a system goes live.
- An owner. One named person in the business is accountable for what the system does.
- Limits. It's written down what the system can do on its own, and what has to go to a person. The limits are enforced in the systems it uses, not only in a prompt.
- Human checkpoints. Decisions above a set level of risk, value or uncertainty are routed to a named person with the full history attached.
- A record. Every action leaves evidence: what the system saw, what it decided, which rule allowed it, and who approved it if approval was needed.
Governance is often treated as the thing that slows AI down. In regulated businesses it's the opposite. Projects stall when risk and compliance can't see what the system will do. When they can, they sign off, and the conversation moves to where to use it next.
That's the approach ETT takes, and why I describe the goal as AI that shows its working.
Show Your Working
A fortnightly piece on building and buying AI that pays its way. What I'm building, what it costs, and the questions to ask before you sign anything.