The first AI project does not need to transform an entire department. It needs to answer a useful question about a specific task. A focused pilot creates room to understand quality, effort, and operational fit before expanding the scope. Start with work that can be described clearly and evaluated by people who understand what a good result looks like.
Choose a task rather than a broad ambition
“Use AI in customer service” is too broad to evaluate. “Prepare a draft answer to a defined group of questions using approved support documents” gives the team something observable. Identify the input, the expected output, the person using it, and the decision that remains with a human. Be explicit about what the pilot will not do, particularly where an output could trigger an external action.
Check whether the inputs are ready
Collect examples of the information the task depends on. Ask whether it is current, complete enough for the purpose, and available to the intended users. A model cannot resolve missing ownership or contradictory documents simply because the interface looks conversational. Assign a content owner and record which material is approved for the pilot. Keep unrelated or unnecessary information out of the initial scope.
Create a small but varied evaluation set
Include ordinary examples, ambiguous requests, incomplete inputs, and situations where the system should decline to give an answer. Have a knowledgeable reviewer describe acceptable outcomes before seeing the generated results. This reduces the temptation to judge quality only from a few impressive demonstrations. Keep some examples separate from development so the evaluation can reveal more than whether the prototype fits familiar cases.
Measure the whole task, including review
Compare the assisted workflow with the existing one. Consider the time needed to prepare inputs, check the result, correct errors, and handle exceptions. Record operating cost and response time alongside usefulness. A draft produced quickly may offer little benefit if a reviewer spends longer checking it than they would have spent doing the work directly.
- One task and a named owner.
- Approved, representative input material.
- Written quality criteria and failure examples.
- A defined human review step.
- An agreed decision at the end of the pilot.
Decide whether to expand, refine, or stop
Agree in advance what evidence would justify the next stage. The outcome may be a narrower scope, better source material, a different approach, or a decision not to proceed. Document what was learned even when the pilot stops. A useful experiment prevents an unsuitable feature from becoming an expensive operational dependency.
Choose an AI pilot that is small enough to evaluate and important enough to teach you something. The deliverable is evidence for a decision, not just a demonstration.
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