Building dependable AI support
How do you measure AI customer support quality?
Measure AI support quality by reviewing whether the answer was correct, whether the customer could take a useful next step, and whether the issue stayed resolved. Track handovers and repeat contacts alongside customer feedback. An assistant sending a message is an activity measure, not evidence that it solved the problem.
Which outcomes should you review?
Sample conversations across common questions, exceptions, and failed lookups. Record whether the response used the right policy, preserved its conditions, and avoided unsupported account claims. Review handovers for usefulness too: stopping at the right moment can be a successful outcome when a person is required.
How do you avoid misleading comparisons?
Keep question mix, date range, and definitions consistent. A trial using only simple setup questions cannot establish performance on billing disputes. Report the size of a reviewed sample and distinguish an agent’s judgment from a customer-confirmed resolution. Avoid assigning meaning to a percentage without its denominator.
What should you change after a review?
Group failures by cause: missing knowledge, contradictory policy, stale context, poor clarification, or inappropriate scope. Fix the underlying source or workflow and test the original questions again. A list of negative ratings becomes useful when someone owns the resulting improvements.
Practical checklist
- Define correctness and resolution before measuring.
- Sample difficult cases as well as easy ones.
- Review handovers and repeat contacts.
- Keep an improvement log with owners and retest questions.
Worked example
An assistant answers 80 of 100 questions, but some customers contact the team again. The 80 replies alone are not an 80% resolution rate. Review whether the answers were correct and whether the issue was actually addressed before using the number to make a staffing decision.
How Fire Away fits
Fire Away provides conversation history, assistant activity and source inspection, and agent feedback on AI replies. Use those records for a staff-led review process with explicit criteria and follow-up.