QA brief: one system for AI and human support work
Quality breaks down fastest when AI and human work are graded by different standards.
The better approach is simpler: use one QA system, log the exceptions, and let the workflow improve from what the review finds.
This page is the short QA brief. Use the related guide when you need the more direct checklist version for training or manager review.
One bar keeps quality visible
AI and human work should be graded against the same standard so the team can compare what is working and what is slipping.
- accuracy
- continuity
- resolution quality
Logging matters as much as grading
A useful QA loop records what happened, where it happened, who handled it, and whether the same issue appears again.
- exception logging
- repeat detection
- owner visibility
QA should change the next round of work
Quality review is only useful when it feeds routing logic, approval rules, handoff design, and exception handling steps.
- routing updates
- approval updates
- handoff updates
What the review sample should include
A useful QA sample should show enough evidence for a manager to see whether support followed the approved process, not just whether the final answer sounded polite.
- customer intent and channel
- required fields captured
- blocked actions and escalation reason
- correction or coaching note
FAQ
What should one QA system measure?
It should measure accuracy, continuity, exception handling, and resolution quality.
Is QA only a support-team issue?
No. The same visibility problem appears anywhere AI and humans share a workflow.
Next step
Request a workflow review.
