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An AI demo can look impressive in a meeting and still make a team’s work harder. A summary may omit the one detail a customer needed. A draft may take longer to check than it would have taken to write. A pilot gives you a way to find those problems while the stakes are manageable.
The aim is to make a decision: should this workflow continue, change, or stop? Here’s a practical way to get there.
Pick a task with a clear finish line
Start with a recurring task that people can describe and check. For example, a support team might draft replies to common product questions using an approved knowledge base. A person still checks and sends each reply during the pilot.
Choose one queue, team, or document type. Ask the people doing the work where the friction is and what a good result looks like. Avoid starting with a high-consequence decision, such as whether someone gets hired or receives a benefit. The cost of a plausible mistake can be much higher there.
Write down the boundaries: which inputs are allowed, which tool and account are approved, who reviews the output, and what the tool must never do. Check the provider’s terms and your organization’s policies before using customer information. If you cannot use it safely, use approved synthetic or redacted examples and recognize that the results may not reflect every condition in production.
Measure the work before adding AI
Record a baseline for a sample of ordinary cases. For the support example, that might mean how long a reply takes, including finding the answer and checking it, and how often a reviewer finds an error. Include difficult cases, such as an outdated article or a question the knowledge base does not answer.
Then use comparable cases to measure the AI-assisted workflow. Count the whole process: preparing the input, generating a draft, checking sources, correcting it, and sending it. A draft produced in ten seconds is not a time saving if it takes five minutes to verify and repair.
Agree on a small scorecard before the pilot begins:
- Quality: How often are the draft and the final answer accurate and complete according to a reviewer? Record those separately so corrections do not hide recurring draft errors.
- Time: How long does the full task take, including review?
- Failure: What kinds of mistakes appear, and how serious are they?
- Experience: Can staff use the workflow without workarounds or confusion?
Set a threshold that fits the task. A team might decide that any unsupported product promise is unacceptable, even if average handling time falls. Record the sample size and who reviewed it. A small pilot can reveal problems, but it cannot establish a reliable error rate for every future situation.
Teach the review, not just the prompt
Give participants a short practice session using examples they recognize. Show them where the approved information comes from, how to ask for a draft, and how to check the result against the source. Include an example where the system produces a confident answer with an unsupported detail. Ask the group to find it.
That exercise matters because fluent text can be wrong. The NIST Generative AI Profile describes this problem as confabulation: a system can produce false or inconsistent content, including inaccurate citations. A link or citation in an answer is a starting point for checking, not proof that the source supports the claim.
Make it easy to say, “I don’t know” or route a case to a colleague. Reviewers need enough time and authority to reject a draft, not just a checkbox to mark it reviewed.
Decide what happens after the pilot
At the end, look at the scorecard with the people who did the work. If quality is acceptable but checking takes too long, try narrowing the task or improving the source material. If errors are frequent or hard to catch, stop and reconsider the use case. If the results justify continuing, assign an owner and keep reviewing samples as the work, source documents, or tool changes.
Pre-launch testing cannot capture every condition a system will encounter. NIST’s research on monitoring deployed AI systems discusses why teams also need to watch performance in use.
A useful pilot ends with a record of the decision, the evidence behind it, and the remaining questions. It may also end with a decision not to proceed. That is still a useful result.
If your team is choosing a use case or wants help building the skills to evaluate one, explore our AI strategy and AI training services.