A new generative AI tool can make it easy to produce drafts, summaries, or variations, but a fluent response is not proof that the result is correct or appropriate to use. A pilot helps a business learn about quality, data handling, cost, and workflow fit before the tool reaches more people. The pilot should answer one practical question and have a named owner, defined boundaries, and a way to stop if the risks outweigh the benefit.
Select a low-risk task with a clear baseline
Choose a repetitive task where a person can review the output and where an error would not immediately harm a customer, employee, or business record. Define what happens today, who performs it, and how long or difficult it usually is. Avoid starting with a high-stakes decision or a task that depends on confidential information. A narrow baseline makes it possible to tell whether the tool improves the work rather than simply producing something that looks impressive.
Set data and tool boundaries before use
Decide what information people may enter, which account or approved service they should use, and what data must stay out of prompts. Review the provider’s terms, retention controls, and access model with the person responsible for security and privacy. Do not use a public tool with customer, employee, financial, or confidential business material unless the organization has reviewed and approved that use. Give staff a short example of a safe prompt and a prompt they must not submit.
Keep a human responsible for the result
Name the person who checks facts, context, tone, citations, calculations, and sensitive details before the output is shared or used. Ask reviewers to verify a sample against authoritative source material instead of relying on the AI tool’s own explanation. For generated code or process steps, use the organization’s normal review and change controls. Make it clear to customers or colleagues when the business requires disclosure of AI assistance.
Measure quality and operational cost
Track whether the task is completed more quickly, how much editing is needed, which errors appear, and whether the output meets the original quality standard. Include the time spent writing prompts, reviewing results, correcting mistakes, and managing the service. Record any customer or employee feedback and note where the tool performs poorly. NIST’s Generative AI Profile offers a risk-management reference for identifying and addressing risks across AI use; adapt the level of formality to the pilot’s scale.
Decide whether to expand, change, or stop
At the end of the pilot, compare the results with the baseline and document unresolved risks. Expand only when the benefit is clear, the data controls are understood, and staff know who remains accountable. If the tool is useful only for part of the task, redesign the workflow around that limited role. Set a review date because product behavior, terms, and team needs can change. A small experiment is successful when it leads to a better-informed decision, including a decision not to adopt.