Why governance matters now
AI governance used to be a concern for large enterprises and regulated industries. It is now a concern for every organization that uses AI — which is, increasingly, every organization. The question is not whether you need governance, but how much, and how to start without paralyzing your team.
The minimum viable framework
A practical AI governance framework for a small organization has five components:
- An inventory — a list of every AI system in use, what data it touches, and who is responsible for it. You cannot govern what you have not cataloged.
- A use policy — what is AI allowed to be used for, and what is off-limits? Keep it short and clear.
- A review process — before a new AI system is deployed, someone reviews it for risk. This does not require a committee; it requires a checklist and a designated reviewer.
- An incident plan — when something goes wrong — a hallucination causes harm, a bias is discovered, a data leak occurs — what do you do? Write it down before you need it.
- A documentation habit — every system should have a short record of what it does, what data it uses, and when it was last reviewed.
What to avoid
- Over-engineering — a 50-page governance document that no one reads is worse than no document. Start with one page.
- Governance theater — policies that exist on paper but are not followed in practice. If you cannot follow it, simplify it.
- Permanent rigidity — governance should evolve as your use of AI evolves. Review the framework itself annually.
The principle
The goal of governance is not to prevent AI from being used. It is to ensure that when it is used, the organization knows what it is doing, can explain it, and can respond when something goes wrong. That is achievable for any organization, at any size.
Knowing better means knowing what you are doing — and being able to prove it.