Choose a repeatable task
A useful first AI project has a clear input, a recognisable output and someone who can judge the result. Drafting a summary of a support conversation is easier to evaluate than asking AI to improve customer experience across the company. Start by observing a task that already happens regularly. Record how long it takes, where errors occur and what a good result looks like. This creates a baseline for a meaningful comparison.
Separate assistance from authority
Generating a draft and sending it to a customer are different responsibilities. Let the first version assist a person who remains accountable for the decision. Show the original information beside the suggestion so checking is practical. Define which claims require confirmation and which actions are outside scope. An AI pilot should make the work easier to review, rather than quietly moving judgement into a system nobody understands.
Test with examples from the actual workflow
Build a small evaluation set that includes ordinary cases, incomplete inputs and awkward exceptions. Use synthetic or appropriately approved data during development. Ask the people who do the job to mark outputs as useful, needs editing or unusable, with a short reason. Compare time saved after review, not just generation speed. A draft produced instantly is not a saving if it takes longer to correct than writing it yourself.
Decide what would justify expansion
Agree a review date and an owner before the pilot starts. Track quality, effort and failure patterns. Keep a simple fallback to the original process. If the system helps, expand one boundary at a time: more document types, more users or a limited integration. If it does not, the experiment still teaches you whether the problem is poor inputs, unclear instructions or a task that needs a different approach.
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