Use AI conversation review to improve customer service quality

AI is useful when it finds conversations worth examining and brings the relevant evidence to a reviewer. An unexplained overall score does much less to show a team what to improve. The goal should be better answers and service processes, with people able to review context and challenge a finding.

Replace broad criteria such as “answered well” with observable behaviour. For an order enquiry, useful criteria might include identifying the relevant order, reporting a status supported by current information, and explaining the next step and its owner.

Different contact types need different criteria. A question about opening hours does not need the same handoff as a missing delivery. A customer who stops replying should not automatically become evidence that an agent abandoned the issue. Allow “not assessable from available information” and “not applicable.”

Some existing tools already combine configurable criteria with conversation evidence. For example, Amazon Connect's evaluation documentation describes evaluating contacts alongside recordings and transcripts. That is a useful capability to look for when comparing tools. Your team's languages and supported channels still require separate checks.

Completeness: Identify the customer's distinct questions and check whether each was addressed. A correct product answer should not hide an unanswered document request in the same conversation.

Accuracy and commitments: Establish what supports the answer, whether the relevant policy applied at the time, and whether a refund or delivery commitment exceeded the agent's authority. A detected promise is a reason to inspect the case, not automatic proof of a mistake.

Handoffs: When the issue cannot be completed in the conversation, check whether follow-up work exists and has an owner. A good response followed by a failed task-creation integration is a different problem from an incomplete answer.

Clarity for the customer: Can the customer tell what information to provide, how an update will arrive, and what to do if nothing happens? Agents do not need to use identical scripted wording to satisfy this criterion.

A customer says: “Part of the order is missing. Could I also get a tax invoice?” The response is: “Sorry about that. I will ask the team to contact you.” The conversation is then closed.

The system might flag that the missing item was not clarified, the document request has no visible answer, and the follow-up channel is unclear. Its conclusions must remain within the evidence. If the tool sees only the chat, it cannot claim that no follow-up task exists unless it also checked the work system. “Not visible here” is different from “not done.”

A reviewer may discover that a task was created but its reference was not shown in the chat. The improvement could be better data linking or a clearer next-step message, rather than a conclusion that the agent ignored the issue. The unanswered invoice request could become a coaching example for checking multi-part questions before closing a conversation.

An actionable finding includes the criterion, original exchange, explanation and human review status. Keep corrections to the AI's findings. They are useful evaluation examples and prevent a disproved claim from remaining in a report without qualification.

Ask two knowledgeable reviewers to assess a varied sample independently, then discuss disagreements. Inconsistent results may reflect ambiguous criteria, conflicting policy or missing information. Adjusting a model alone will not resolve those problems.

After clarifying the criteria, separate examples used to improve the system from those used to evaluate it. Include correct answers phrased differently from the handbook, customers who remain unhappy despite appropriate handling, and cases with incomplete evidence. Do not use only short, easy conversations.

Inspect errors by criterion, rather than relying on an overall average. A system may identify greetings reliably while missing an unsupported commitment. Those mistakes have different consequences. Count false alerts and missed important issues, alongside the time required for people to investigate them.

Explain to the team what is reviewed, who can access findings and how the results will be used. Limit customer information in reports to what is needed for review. Coaching examples should not unnecessarily carry personal details into a wider audience.

Repeated inaccurate answers may point to a handbook that is hard to find, outdated or internally inconsistent. Repeated handoff problems may require clearer ownership or a repaired notification flow. Asking everyone to write longer responses would miss those causes. Skills such as clarifying a multi-part request can instead be coached using examples with identifying details removed.

Give each improvement an owner and a way to assess the result. After updating a policy page, review comparable conversations about that topic again. A lower raw error count could simply reflect fewer contacts, so relate findings to the number and type of conversations examined.

Keep the first reporting workflow subject to human review before circulating findings internally. Expand the screening volume once evidence references and criteria behave consistently. The value comes from identifying what should change and checking whether the change worked, rather than producing more scores without anybody owning the response.

Where the underlying problem is the knowledge source, see preparing company documents for AI for source and version management. If the team also wants automated answers, review chatbot and AI agent responsibilities separately from permission to inspect conversations.