AI governance should help a team make better operating decisions. It should answer what is allowed, who owns the outcome, what evidence is required, how risk is controlled, and when a workflow must be changed or stopped. A small team can establish those answers without copying an enterprise policy library.
Create one intake path for AI use cases
Teams lose visibility when every department experiments through a different tool, budget, or approval path. Use a lightweight intake that captures the business problem, user, decision, data, system, owner, expected value, consequence of error, and proposed human review.
The intake should route low-impact experiments quickly while identifying work that needs privacy, security, legal, compliance, finance, or executive review.
Set a minimum control baseline
Accountability
Assign a business owner who remains responsible for the workflow outcome after implementation.
Data boundaries
Define approved tools, access, retention, sensitive-data restrictions, and the evidence required for traceability.
Evaluation
Test representative normal cases, edge cases, missing inputs, and known failure patterns against written acceptance criteria.
Human review
Match the reviewer, evidence, authority, and escalation path to the consequence of error.
Change and monitoring
Review material changes and track quality, exceptions, overrides, incidents, usage, and user feedback.
Keep governance connected to delivery
A policy that sits outside the implementation workflow will be bypassed. Build approval evidence into the project plan, test results, access setup, release decision, and operating review. This makes governance part of how work moves rather than a separate document assembled at the end.
Use conditions when a use case can proceed with specific gaps closed. Name the owner and due date for every condition, and confirm completion before expanding access or scope.
Review, improve, and retire
Governance continues after launch. Reassess the workflow when the model, vendor, prompt, data source, integration, policy, decision threshold, or user population changes. Retire workflows that no longer deliver value, cannot meet acceptance criteria, or create unmanaged risk.