The work comes before the AI

An AI use case becomes easier to discuss when the conversation starts with a piece of work, rather than a promise about intelligence.

What should the system help with? What information may it use? Who decides when its answer is ready to become an action?

Those questions do not settle every risk. They make the proposed responsibility more concrete.

Define the work before the model

“Help the team work faster” is an aspiration. “Prepare a first draft from these approved documents for a named person to review” is a bounded task.

The second version gives people something they can evaluate. It also makes it easier to say what is outside the scope.

Separate available information from approved information

A system may be able to reach information that it should not use for a particular task. The source boundary needs to be a deliberate choice.

For a useful first use case, identify the approved sources, their owner, and how the team will know when they need updating.

Settle review, escalation, and maintenance

Who checks the output? What happens when the sources disagree? When should the system stop rather than improvise? Who maintains the instructions after the initial enthusiasm has passed?

An approval step is only useful when a person can understand what they are approving and has a realistic opportunity to reject it.

The test is not whether the system sounds like a capable colleague. It is whether the work and its boundaries are clear enough for the team to use it responsibly.