Choose a First AI Assistant Use Case That Can Be Tested

Start with a repeat question, not an ambitious bot

A useful first AI assistant use case is usually a question your team answers repeatedly from material that already exists. Think of a customer asking what a plan includes, which documents are needed before an appointment, or how to find a published return policy. The best pilot is narrow enough that a person can judge each answer against a source, yet common enough to produce a meaningful sample.

Begin by collecting recent examples from real support work. Remove names, account details, and other information that is not needed to understand the question. Group near-duplicates together, but keep materially different situations separate. “How long does delivery take?” may sound like one topic, while delivery to a remote region or an order with multiple items may require a different source or a human decision.

Choose a question family with a clear answer boundary. A public policy or a maintained product guide is stronger pilot material than a complicated exception handled through memory. Confirm that the source is current, approved for customer use, and specific enough to resolve the question. If two documents disagree, resolve that conflict with the policy owner before asking an assistant to use either one.

Define what counts as a good outcome

Before configuration, write down the expected result for each test question. A practical rubric can distinguish a correct resolution, a useful answer that still needs a person, an unnecessary escalation, and a response that is unsupported or wrong. Keep the categories small and observable. “Sounds helpful” is not a reliable scoring rule; “states the return window correctly and does not promise an exception” is.

Select a fixed sample of questions and preserve it unchanged during the pilot. Include routine wording, a paraphrase, an incomplete question, and a case whose answer is absent from the approved source. Add an example that should go to a person if policy or context requires judgment. For each one, record the expected answer or the expected stop point before testing. This prevents the team from grading a response more generously simply because it sounds fluent.

Set a staffed fallback for the hours and channel where the pilot will be available. Decide who reviews escalations, what context that person needs, and what the assistant should say when a person cannot take over immediately. That is an operating procedure for the business, not a guarantee that a particular response time exists in the product. Keep any commitment within the support policy the team can actually meet.

Configure and review a bounded pilot

In Resonoon, an existing assistant can be configured in the authenticated bot editor. Its role and purpose, tone, and knowledge are separate concerns: describe the intended support responsibility, provide approved source material, and guide how replies should be phrased. The editor supports PDF and Markdown knowledge files. Uploading a document does not make an outdated policy authoritative, so curate the material before adding it.

Use the assistant test page to try the prepared sample. Keep a record outside the product of the exact question, the answer, the expected result, and the reviewer’s score. If an answer fails, identify whether the missing ingredient is a source fact, a boundary in the role, or clearer behavioral guidance. Change one cause at a time and rerun the same question set. Otherwise, a better score cannot tell you which edit helped.

For example, a retailer might pilot questions about the standard return window. The source may answer the ordinary case clearly but say nothing about damaged goods. The test should reward a precise answer for the ordinary case and a careful handoff for damage, rather than treating a confident guess as success. The scope can remain small until policy owners decide how exceptions should be handled.

Decide whether to expand

Review the whole fixed sample, not only the strongest exchanges. Count correct resolutions and appropriate escalations separately. Look at unsupported answers as a serious failure even if the customer could probably find the right answer elsewhere. Also note whether the fallback is understandable and whether staff can act on the context provided.

Expansion should follow evidence and operational readiness. If a source conflict caused errors, correct the source and repeat the tests. If the instructions invited the assistant to infer an exception, make the stop condition explicit and test it again. If the human fallback is not available when customers need it, adjust the pilot’s availability or staffing before adding more question types.

A pilot is useful even when it says “not yet.” It gives the business a concrete list of unresolved policy questions and a repeatable way to judge a later revision. Resonoon’s AI customer support use case describes the broader application; the specific pilot scope, scoring rubric, and staffing arrangement remain decisions for the team.