An employee pastes a confidential proposal into a public AI tool because it saves an hour. Elsewhere, a formal AI committee debates a policy for six months. One side moves without control. The other controls without moving.
Good governance must solve both problems.
Govern the use case, not the word AI
A spelling assistant and a system recommending customer credit do not carry the same risk. Practical AI governance for UAE companies classifies use cases by data sensitivity, impact and reversibility.
Low-risk assistance can move within clear rules. High-impact decisions need stronger evaluation, human review, logging and escalation. Prohibited uses should be stated plainly.
Give teams a usable path
A policy nobody can apply will be bypassed. Create a short intake that asks:
- What decision or task does the system influence?
- What data enters it, and where does that data go?
- Who could be harmed by a wrong output?
- Can a person review or reverse the result?
- Who owns performance after launch?
The answers determine the review level. They also force the project team to define what it is actually building.
Evaluation is part of governance
Do not approve a model based on a vendor demonstration. Test it with representative business cases, including difficult examples, multilingual inputs and missing information. Record where it fails and what the workflow should do when confidence is low.
Human oversight is not a person clicking approve. It is a person with context, authority and enough time to intervene.
Keep a register of active AI uses, owners, data sources, evaluation dates and incidents. Review systems when models, prompts, data or business rules change.
Governance should make safe work faster by removing ambiguity. If every idea needs the same committee, teams will either stop experimenting or experiment invisibly. Neither is control.