A company can have a thoughtful AI policy and still leave an employee unsure whether a particular task is appropriate. The gap is often between a general principle and an everyday decision: which information can be used, when an output needs checking, and who can approve a new application.
Governance becomes practical when those choices are designed into the workflow. The aim is to give people a clear path to responsible use and a reliable way to escalate uncertainty.
Inventory the actual uses
Begin with where AI enters the work today. Capture the task, the information involved, the people affected, and what the output can influence. A tool name alone tells a reviewer very little. The same product may be used for low-stakes brainstorming or to support a consequential recommendation.
Assign an owner who understands the process. That person should be able to explain the intended benefit, the limits of the system, and what happens when it produces an unsuitable result.
Put review at the point of consequence
Controls should respond to the nature of the decision. Consider whether an output remains internal, reaches a customer, updates a record, or triggers another action. Decide where a person must inspect the work and what evidence they need to do so meaningfully.
Review should include a route to reject or correct the output. Requiring a click without giving the reviewer enough context can create the appearance of oversight while adding little judgment.
Maintain the operating record
Keep track of material changes to the workflow, the information it uses, and the tools supporting it. Establish a simple process for reporting failures and deciding whether a use should be revised or paused.
Specific legal obligations depend on the situation and require appropriate specialist review. Operationally, however, a company can already make ownership, approved uses, review points, and escalation paths explicit. Those foundations help teams act consistently as both their systems and external requirements evolve.