Govern the decisions that repeat
As AI use grows, the same questions recur: what data is allowed, which uses need review, how quality is tested, who accepts residual risk, and how incidents are handled.
A lightweight operating rhythm makes those decisions visible and consistent.
Start with a small governance stack
- An inventory of active and proposed AI use cases.
- A simple risk classification that determines review depth.
- Named owners for business outcomes, technical operation, and oversight.
- Acceptable-use guidance written in language employees can apply.
- Regular review of incidents, exceptions, quality, and policy changes.
The goal is traceable judgment: people should know who decided, what evidence they used, and when the decision will be revisited.
Scale the control with the consequence
Low-consequence drafting may need basic disclosure and human review. Decisions that affect rights, safety, employment, finance, or customers require deeper evidence and stronger oversight.
A transformation leader helps the organization match the control to the actual consequence instead of treating every use as equally risky or equally safe.
Put it into practice
Create a one-page register of current AI uses with an owner, purpose, data type, review rule, and risk level. Visibility comes before sophistication.