A rep says the AI scored their call wrong. What should happen next?
A defined path, every time, or the program loses legitimacy fast. The rep flags the score with a reason, a human listens to the cited evidence, the outcome and rationale get recorded, and the score is corrected or upheld by that person. Then analyze disputes in aggregate — clustering on one criterion almost always means the criterion is worded ambiguously, not that reps are complaining.
Why the appeal path is load-bearing
Any evaluation system will produce some wrong results. What determines whether a team accepts the system is not the error rate — it is whether there is a visible, respected way to challenge a result and have a person actually look.
Without one, every disagreement becomes a grievance about the software, and reps learn that the correct response to a bad score is resentment rather than a conversation. With one, the same disagreement becomes a five-minute review that usually improves the rubric.
The four steps
- Flag with a reason. Not just "this is wrong" — which criterion, and what the rep believes happened instead. This forces engagement with the standard.
- A human reviews the evidence. Open the cited transcript span and listen. This is why evidence-linked scoring matters; without a citation, review turns into re-litigating the whole call.
- Record the outcome and the reasoning. Upheld or corrected, in one or two sentences. The reasoning is what makes the next similar case consistent.
- Close the loop with the rep. Same week. A dispute that disappears into a queue is worse than no process.
The aggregate is the valuable part
Individual disputes correct individual scores. The pattern across disputes corrects the system. If a quarter of all challenges land on one criterion, that criterion is ambiguous — two people reading the same words are reaching different conclusions, and the model is only one of them.
If disputes cluster around one rep, that is a coaching conversation about the standard, not about the software. If they cluster around one call type — outbound follow-ups, warranty calls, transfers — the rubric was written for a different kind of conversation than the one being scored. Each pattern has a distinct and obvious fix, which is why the analysis is worth doing monthly.
Who holds the decision
The human review is the record. The AI output is an input to a manager's judgment, and when the two conflict, the manager's documented decision is what stands and what any later conversation refers back to.
That ordering has to be stated out loud to the team, not just implied by the workflow. Rubric evaluation applies your standards consistently across every call instead of the handful a supervisor had time to hear. It supports the coaching decision. It does not make it, and coaching programs that pretend otherwise fail on the human side long before they fail on the technical one.
Topics: disputes · coaching · rubric · trust
Have a version of this question about your own business?
The useful answer usually depends on which systems you run and how they're connected. That's a conversation, not a blog post.