A useful first AI project starts with a piece of work the team understands. It gives everyone something concrete to discuss: the request, the information needed and the result that would help.
Choose a task you can describe.
Preparing a supplier comparison. Finding the procedure that applies to a machine. Gathering the context for a customer reply. Each has a recognizable beginning and an output someone needs.
It helps if the task repeats. The team can bring several examples, explain the exceptions and say where the effort goes today.
Know what a useful result looks like.
Agree what the person receiving the output needs to see. A comparison might need matching requirements, delivery terms and unanswered questions. A document search might need the relevant passage and a link to its source.
This gives you a way to evaluate the work. A fluent answer alone does not tell you whether the task was completed well.
Look at the information behind it.
Identify where the records live, who can access them and how they are kept current. Some workflows can start with a small set of documents. Others depend on a connection to a live business system.
Missing context is part of the task, too. Decide when the workflow should ask a question or pass the work back to a person.
Compare the complete effort.
Try representative requests and include the time spent checking and correcting the result. Look at quality, useful time saved and the effort of operating the system.
A first deployment should help you decide what to keep, what to improve and whether to take it further. That decision becomes easier when the starting task is clear.