Healthcare · 2026
Replacing 2% manual call sampling with 100% AI auditing
A QA team listening to a fraction of a percent of calls now reviews all of them — with humans spending their time on the calls that actually warrant it.
- Call coverage
- 2% → 100%
- Every call scored
- Time to coaching
- 3 wks → 1 day
- Exception review cycle
- QA hours on routine scoring
- −80%
- Redirected to exceptions
- Compliance phrase adherence
- +22pts
- Within one quarter
The situation
Four QA staff were manually scoring roughly two percent of calls, chosen close to at random.
In a regulated vertical, the calls that mattered most were the ones least likely to be sampled.
Coaching lagged the behaviour it addressed by weeks, so it rarely changed anything.
What I did
- 01
Deployed transcription across all calls and built a scoring rubric matching the existing manual scorecard, so results stayed comparable.
- 02
Configured AI auditing to score every call and surface only exceptions for human review.
- 03
Added sentiment and keyword triggers for the compliance phrases the vertical requires.
- 04
Rebuilt the coaching loop around next-day exception review instead of monthly sampling.