AI score vs. team lead: how to calibrate trust in automated QA
Automated scoring only works if your leads trust it. A practical calibration loop that takes two weeks, not two quarters.
The fastest way to kill an AI QA rollout is to ask team leads to trust a number they cannot argue with. The fastest way to make it stick is to let them argue with it on record.
Verify a sample, deliberately
In week one, leads verify a fixed sample of calls per agent. For every parameter they can match the AI score in one click or enter their own. Where the gap is large, Cadence requires a short remark explaining why.
Read the discrepancies, not the averages
Average agreement hides the real problem. Look at which parameters attract the most remarks. Usually one or two definitions are ambiguous, such as "empathy" or "objection handled". Tightening the parameter definition fixes most of the gap.
When to step back
Once AI and human scores land within a few points on most parameters, move leads from sampling to exception review: fatal flags, low scores and disputed calls. That is where QA cost falls and coaching time rises.