An impressive answer in a demonstration does not tell you how an AI feature will behave during ordinary work. A useful trial needs a specific job, representative inputs and people who can judge the result. Consider an illustrative support workflow: after a conversation, an agent writes a handover summary for the next person. This task offers a defined input and reader, while leaving the team with practical questions about accuracy, missing context and review effort.
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Describe the current task before choosing AI
Observe how agents prepare a handover today. Which details must they find, and how does the next agent use the summary? Define an output that supports that next action: the customer’s issue, steps already attempted, unresolved questions and any agreed follow-up. Record the difficulty the project should address. If the underlying issue is inconsistent source information, improving the form or workflow may be part of the answer. Compare an AI draft with the existing method and any simpler alternative, using the same type of task.
- Name who prepares and who uses the output.
- Record the information required to complete it.
- Identify the problem the trial should investigate.
Give the AI a bounded role
For this example, let the feature draft an internal summary for an agent to inspect and edit. Sending a customer message or closing the case would be separate capabilities requiring separate decisions. Specify which conversation and account information the feature can access, and how that access follows the user’s permissions. Decide what happens when the source is incomplete or the feature is unavailable. A visible route back to manual work keeps the workflow usable and gives the team a way to handle cases the trial does not cover.
Evaluate examples against written criteria
Create a review set containing routine conversations, unclear requests, changes of mind and missing details. Keep some examples separate from those used to adjust the feature. In one illustrative conversation, a customer asks for a refund, but the agent has only promised to investigate. A useful summary must preserve that distinction; “refund approved” is an invented commitment. Ask reviewers to record what is correct, omitted or unsupported, rather than giving only an overall impression.
- Required facts: does the summary preserve the issue and the actual next step?
- Unsupported claims: does it invent an action, promise or customer detail?
- Whole-task effort: how much checking and rewriting does the agent need?
- Incomplete input: does it identify the gap instead of filling it with a guess?
Set release conditions and a feedback loop
Agree which errors block release, what remaining limitations users need to understand and who reviews the evidence. For the support example, the team might require manual confirmation of every follow-up commitment before a summary is saved. This is a proposed workflow rule, not proof that all errors have been removed. Make the draft easy to compare with its source, edit or reject. After release, review corrections and rerun the agreed examples when the feature changes. Evaluate the complete workflow, including the extra work introduced by review.