AI scoring can match human reviewers on clearly written criteria and applies the same standard to every call, but accuracy depends on your rubric, so test it on your own calls against your own reviewers. Itero (iteroapp.ai) shows the transcript evidence behind each score, so reviewers can check any result.
Last updated: November 3, 2026. Itero is not affiliated with iTero, the intraoral dental scanner made by Align Technology.
This article covers where AI scoring is strong, where people are still better, and how to test it on your own calls before you rely on it.
Where is AI scoring strong, and where are people better?
AI scoring is strong on coverage and consistency, and people are better at judgment. A sound QA program uses both.
| AI scoring | Human reviewers | |
|---|---|---|
| Coverage | Every call it receives | A sample |
| Consistency | Applies the same criteria every time | Can vary by reviewer and by day |
| Clear criteria | Strong | Strong |
| Unusual situations | Can miss what the rubric doesn't describe | Can apply judgment |
| Context outside the call | Sees only what it is given | Can draw on account history and policy |
Transcripts matter too. AI scores what the transcript shows, so poor audio can affect a result, which is another reason to keep the evidence visible.
Why do human reviewers disagree with each other?
Reviewers disagree because many criteria leave room for interpretation, which is why QA teams hold calibration sessions. Two experienced reviewers can read "showed empathy" differently and both defend their score.
That matters for any accuracy test. AI scoring can't be held to a perfect answer key if your reviewers don't agree with each other. Measure how often your reviewers match first, and use that as the reference point.
How do you test AI scoring accuracy on your own calls?
Test it with a blind comparison between the AI and your reviewers, on the scorecard you plan to use.
- Pick a mixed sample. Include strong calls, weak calls and known failures.
- Score blind. Have two reviewers score each call without seeing the AI result.
- Compare by criterion. An overall score can hide a criterion that is badly off.
- Review each disagreement. Read the evidence and decide whether the AI, the reviewer or the wording was wrong.
- Fix the wording and re-test. A disagreement often points to a criterion that needs a clearer definition.
- Keep sampling. After launch, keep reviewing a sample so accuracy doesn't drift when calls or rules change.
When the AI and a reviewer disagree, check the wording of the criterion first. The cited evidence shows whether the cause was the wording, a transcript error or a situation the rubric doesn't describe.
What makes a criterion score accurately?
A criterion scores accurately when two people reading the same transcript would give it the same answer. AI scoring is at its strongest on clear, observable criteria such as required disclosures, so the writing of the rubric does much of the work.
- Make it observable: "Did the rep state the APR?" works better than "Was the rep transparent?"
- Keep one idea per criterion: split anything joined by "and".
- Say when it applies: a check that only applies to some calls needs that condition.
- Define a pass: give an example of what counts and what doesn't.
For regulated teams, see the compliance scorecard checklist.
How accurate is Itero's AI scoring?
In one customer's comparison against their own QA rubric, Itero's scores matched 95% of the time. That is one customer's result on one rubric, not a general benchmark, so run the comparison on yours.
Itero is built to make that checking practical. Scores use your own scorecards, and each evaluation surfaces every supporting citation from the transcript, so a reviewer can see the basis for a result. You can re-run a single evaluation type on a conversation after changing a criterion, without re-processing everything else.
What should stay with human QA reviewers?
Keep the work that needs judgment with people, and let AI handle coverage.
- Calibration: reviewers own the rubric and check a sample of AI scores.
- Disputes: a rep who challenges a score gets a person.
- Edge cases: unusual calls and flagged compliance misses get a human read.
- Rule changes: reviewers update criteria when products, scripts or rules change.
This usually moves analysts from listening to calls to improving the standard (Automated QA). For a closer look at that shift, read manual call review vs automated scoring and the automated call QA software roundup.
Start with a small blind test
Pick a mixed sample of calls, have two reviewers score them blind, and compare the results with AI scoring by criterion. Request a demo to talk through that comparison.
