How to keep one QA system across AI and human 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.
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
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.