Be specific about the work
Suppose employees regularly search internal documents to answer customer questions.
Before choosing a tool, look at the information itself. Are the documents current? Do they contradict each other? Who is allowed to access them?
An assistant will not automatically resolve those problems. Preparing the material may be just as important as building the interface.
The same applies to content work. Asking for “better copy” is vague. Asking for a first draft based on an approved brief, for a named audience and a defined channel gives the task clearer boundaries.
Decide what people should remain responsible for
There is a difference between a tool that suggests a reply and one that sends it.
There is also a difference between helping someone find information and making a decision on their behalf.
AI can produce incorrect or incomplete outputs that sound convincing. Agree where someone needs to check the result, approve an action or step in when the tool cannot handle a request.
Those decisions should shape the solution from the beginning.
Test the difficult situations too
A few impressive examples can make a prototype look ready before it is.
Try incomplete questions, ambiguous requests and cases where the correct response is to ask for help. Check whether the tool uses the right information and stays within its permissions.
Include the people who will use it. They are often best placed to notice when an answer sounds plausible but would not work in practice.
Finally, compare the benefit with the effort needed to review and maintain the output.
You may decide to develop the idea further. You may discover that the information needs work first, or that a simpler solution would do the job.
The purpose of the test is to find that out before you build AI into everyday work.