Plan requirement
| Subscription | Any plan |
| Also required | Quality Assurance or Workforce Engagement Management |
| Access | Admin |
Where AI genuinely helps in a quality programme, and the one line that is worth not crossing.
Where it genuinely helps
- Scoring mechanical categories on every conversation rather than a sample.
- Finding conversations worth reviewing, such as ones where sentiment fell sharply.
- Summarising long threads so a reviewer can orient quickly.
- Spotting patterns across hundreds of conversations that nobody would find by reading.
The second one is underrated
Reviewer time is the scarce resource. Using AI to surface the conversations most worth a human look is worth more than using it to score them.
The line
Feedback to an agent should come from a person. An automatic score with a generated comment is a machine telling somebody their work was poor, and nobody learns from that.
Use it to inform the reviewer, not to replace them.
It cannot judge correctness
Whether the answer was right needs product knowledge and context that only a colleague has. That is also the category that matters most, which limits how much of this work can be automated.
Check it before trusting it
Read conversations it scored and compare with your own reading. Where it disagrees with you consistently, the category is wrong for your context.
Tell agents what is automatic
People accept machine scoring of spelling. They resent discovering that a score they thought came from a colleague did not.
Watch for language differences
Automatic scoring performs unevenly across languages, so a multilingual team can show differences that reflect the tooling rather than the work.
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