AI Support Quality Review Playbook
Configure AI support QA metrics, sampling rules, review loops, knowledge feedback, risk review, and weekly improvement cadence for support supervisors.
AI support QA should not only review final replies. It should inspect intent, knowledge, verification, review, evidence, and feedback loops.
1. Review Scope
Sample these every week:
- frequent automated replies;
- replies edited by humans;
- replies approved and rejected in review;
- high-risk cases such as refunds, compensation, complaints, and negative reviews;
- unresolved or repeated conversations;
- conversations after new knowledge or workflow releases.
Sampling should cover channels, stores, languages, and support teams.
2. Score Dimensions
For each sample, check:
| Dimension | Check |
|---|---|
| Intent | customer need, emotion, and urgency were understood |
| Knowledge | correct knowledge was used and applied to the right market and channel |
| Verification | order, logistics, or refund status was checked when needed |
| Risk | draft, review, or human takeover triggered correctly |
| Reply | tone, accuracy, completeness, and actionable next step |
| Evidence | original message, basis, review, and result were retained |
| Feedback | errors became knowledge or rule updates |
3. QA Metrics
Core metrics include:
- intent detection accuracy;
- knowledge hit rate;
- human edit rate;
- review pass rate;
- high-risk interception rate;
- evidence completeness rate;
- repeat complaint rate;
- knowledge feedback closure rate.
Do not measure only automation rate. High automation with missing evidence and rising complaints means risk is accumulating.
4. Rejection Reason Taxonomy
When humans reject AI replies, classify the reason:
- missing knowledge;
- outdated knowledge;
- wrong channel applicability;
- missing back-office verification;
- incorrect risk level;
- off-brand tone;
- inaccurate multilingual wording;
- human relationship management required.
The clearer the taxonomy, the faster the improvement loop.
5. Weekly Review Process
Use a fixed weekly cadence:
- Summarize QA samples;
- Rank frequent and high-risk issues;
- Identify root cause in knowledge, workflow, review, or verification;
- Update knowledge and rules;
- Retest with historical samples;
- Release to limited scope;
- Record metrics to watch next week.
6. Release Acceptance
A QA improvement is ready to release when:
- historical problem samples pass;
- high-risk cases trigger review;
- replies trace back to knowledge;
- back-office evidence is complete;
- human edit rate decreases or issue attribution becomes clearer.
7. Supervisor Dashboard
Supervisors should watch:
- new high-risk issues this week;
- rejection reason distribution;
- knowledge gap closure;
- quality gaps across channels;
- review queue wait time;
- cases missing evidence.
The goal is not to make supervisors read more chat logs. It is to help them find system-level problems faster.
