AI customer support: approved answers, handoffs and quality checks

Choose a narrow set of questions with approved answers#

AI customer support works best when the assistant has a clear job, reliable source material and a way to stop when a person is needed. Start with a small set of repeat questions that can be answered from published policies: how to find a feature, where to read the current plans or which information a customer needs before contacting the team. Avoid beginning with refunds, identity checks or changes to a customer account. Those requests need authority and verification that a helpful-sounding answer cannot provide. The initial goal is a correct, useful response with a dependable route to a human when that response is insufficient.

Review recent conversations and group them by the customer’s intended outcome. For each group, record an approved answer, its source, the date it was checked and the owner who can update it. Keep important conditions in the answer: an offer that applies to one plan cannot become a general promise. If sources disagree, have the owner resolve the conflict before letting the assistant answer. Remove personal details from training examples and test cases. Do not paste an entire customer history when a short, sanitized description is enough to reproduce the question.

• Explore the AI customer support use case

Define what the assistant may say and when it must escalate#

Write the handover rules before launching the assistant. A payment dispute, request to delete an account, suspected account compromise, unsupported policy question or explicit request for a person should have a named destination. Distinguish the message shown to the customer from the action recorded by the system: saying that a case was assigned is only accurate after the assignment succeeded. If your current tools only draft replies, keep a person responsible for sending and routing them. Describe tone in concrete terms: acknowledge the problem, use the relevant approved answer and ask one necessary question at a time. Never request passwords, one-time login codes or full payment card details. Do not promise a response time unless the team has approved and can meet it.

• Use the escalation matrix and test cases before launch

Test the boundaries, then review resolution quality#

Build a small set of realistic test conversations before inviting customers to use the workflow. Include an ordinary product question, a missing policy, conflicting sources, an upset customer, a repeated failure and a request for a human. Add a case where routing is unavailable so you can check the fallback message. For every case, write down the expected answer or owner and the action the assistant must not take. Have someone besides the prompt author review the result. A fluent answer can still be wrong, and a correct explanation can still fail if the customer has nowhere to go next. After launch, review a sample of completed conversations and every serious failure. Track whether the customer’s problem was resolved, whether the same issue reopened and whether the human received a usable summary. Speed and the percentage of automated replies are supporting measures, not proof of resolution. When a question repeatedly reaches a person, find out whether the policy is missing, the wording is unclear or the case needs human judgment. Update the source or routing rule, then rerun the affected tests. Keep a dated record of policy changes and the person who approved them so a future reviewer can understand why a reply changed.

• Each supported answer has a current source and an owner.
• Sensitive and unsupported requests have a defined human destination.
• The fallback works when routing or the knowledge source is unavailable.
• A reviewer checks resolution and reopened cases, not response speed alone.

• Responsible AI: check inputs, assumptions and output
• Intercom documentation: escalation guidance and rules (provider example)