People ask me some version of the same question every week. "If we switch to AI call scoring, what happens to my QA team?"
The honest answer is that a lot changes, and a lot doesn't. The form stays. The standards stay. The people stay. What changes is how much of the work is listening and how much is judgment.
This post is a straight comparison. What manual QA does well. What automated call scoring does well. Where AI still needs people. And what you should actually expect in week one versus month three.
I'm not going to run a cost-per-call math exercise here. That's its own post. This one is about the work itself.
What manual QA is good at
Let me start by defending manual QA, because it deserves it.
Context. A good analyst knows your floor. They know the agent who just came back from leave. They know the billing system went down at 2 PM and every rep had to stall. They hear a call and understand what was going on around it.
Gray areas. Some questions on a QA form are hard. "Did the agent take ownership?" "Was the resolution appropriate?" An experienced analyst can make a call on those, explain why, and defend it in a calibration session.
Relationships. A QA analyst who has been around a while can walk up to a supervisor and say, "Watch this one, I think something's off with her." That's not a score. It's a heads-up, and it matters.
Catching what the form misses. Analysts notice things nobody wrote a question for. A new customer complaint. A process that confuses people. A product issue showing up in three calls in one afternoon.
So manual QA is not broken. The problem is volume.
Where manual QA runs out of road
A person can only listen to so many calls in a day. Each call takes as long as the call takes, plus the time to score it and write notes.
That means most teams score a small sample. A few calls per agent per month. Sometimes fewer when the team is short.
A small sample creates real problems:
- Agents don't trust it. If you score four calls and one of them was a bad day, that bad day is a quarter of their score. Reps know that, and they push back.
- It's slow. By the time a call gets scored and coached, it can be weeks old. The agent barely remembers it.
- Analysts drift. Two people reading the same question score it differently. Even one person scores differently on a Monday morning than a Friday afternoon. That's not a knock on anyone. It's just being human.
- The best people spend their day listening. Your sharpest QA folks spend most of their time on playback instead of on coaching insight.
What AI call scoring is good at
Automated call scoring flips the volume problem.
Coverage. The AI doesn't get tired. It can score far more of your calls than a team of people could, so each agent's score rests on a lot more than a handful of calls.
Consistency. Once it's calibrated to your form, it reads the same question the same way on Tuesday as it did on Monday. That's a big deal for fairness.
Speed. Scores come back fast, so a supervisor can coach on yesterday's call instead of last month's.
Your form, not a generic one. This part matters to me. Good AI call scoring should score against the QA form you already use, with your questions and your weights. If a tool forces you onto its own generic scorecard, you're not measuring what your team cares about anymore.
Patterns. With more calls scored, you can see which questions fail most, which agents struggle with which part of the call, and whether a coaching change actually moved anything.
Where AI still needs people
This is the part vendors tend to skip. AI scoring does not run itself. Here's where your people stay in the loop.
Calibration
The AI has to learn how your team reads your form. "Showed empathy" means one thing at a healthcare client and another at a collections shop.
The way we do it at OttoQA: you give us 10 to 20 calls your team already scored. The AI scores the same calls. We compare line by line. Where the scores don't match, we tighten the wording on the question or add an example of a pass and a fail. Then we score again.
Calibration isn't a one-time thing either. Forms change. Scripts change. Somebody on your team should spot-check AI scores in your regular calibration sessions, the same way you'd check a human analyst. I wrote more about that in how to calibrate AI scoring.
Judgment calls
Some situations need a person. A customer threatening legal action. A call where the agent broke a rule to do the right thing. An escalation where the "correct" script answer would have made it worse.
AI will score those calls against the form. But a human should look at the hard ones and decide what it means for that agent. Flag them, route them to a person, and let the person own the decision.
Disputes
Agents will dispute scores. They should be able to. A score nobody can question isn't a fair score.
When an agent disputes an AI score, a person reviews the call. If the AI got it wrong, you fix the score, and you look at whether the question wording needs work. Every dispute tells you something about your form. Use that.
Coaching
AI finds the issue. It doesn't coach. The supervisor still pulls the agent aside, plays the call, and works on one thing. That's not changing, and I wouldn't want it to.
Week one vs month three
Here's what to realistically expect.
Week one
- Setup is underway. Setup takes about 2 weeks. You're not scoring production calls on day one.
- Calibration is the main job. Your team is comparing AI scores to their own scores on those 10 to 20 calls. Expect some disagreement. That's the point of the exercise.
- The form gets sharper. Almost every team finds two or three questions that were vague all along. Humans were scoring them differently too. They just never noticed.
- People are skeptical. Your QA analysts are wondering what this means for them. Your agents are wondering if a robot is grading them. Talk to both groups early.
Month one to two
- Scores start flowing. Supervisors have much more data per agent than they used to.
- The first surprises show up. You'll see questions that fail way more than you expected. Sometimes it's an agent problem. Often it's a form problem or a process problem.
- Spot-checks continue. Your team keeps checking AI scores against their own on a sample of calls.
Month three
- QA analysts are doing different work. Less playback. More reviewing flagged calls, handling disputes, running calibration, and working with supervisors on coaching plans.
- Coaching gets more specific. Instead of "your score was low," a supervisor can say, "You're missing the verification step on transfer calls. Here are three examples."
- Agents trust the scores more. When a score rests on a lot of calls instead of four, one bad day doesn't sink anybody. In my experience, that changes the tone of coaching conversations.
- You're tuning, not building. Form tweaks get smaller. The work becomes keeping the AI calibrated as your business changes.
What doesn't change
Your standards. Your form. Your supervisors' job. The need for someone who knows your operation to look at the hard calls.
What changes is who does the listening. The AI does most of it. Your people do the thinking.
Where Otto Fits
OttoQA scores your calls against your own QA form, in any language, at 96% accuracy against your team's scoring. We calibrate using 10 to 20 calls your team already scored, and your people stay in charge of judgment calls, disputes and coaching.
No IT needed. It works with any CCaaS or cloud CRM via API, including Zoom Contact Center. Setup takes about 2 weeks. Hosting is US-only, and your customers' calls are never used for training. We're PCI compliant and sign HIPAA BAAs.
The one-time $1,000 covers setup and a 200 to 500 call proof of concept. After that, it's contract or month-to-month. See pricing for details.
If you want to see your own form scored on your own calls, Book a demo.
Frequently asked questions
What is AI call scoring?
AI call scoring uses software to listen to recorded calls and score them against a QA form. The best tools score against the form you already use, so the results match your standards.
Is automated call scoring as accurate as manual QA?
It depends on calibration. OttoQA reaches 96% accuracy against the customer team's own scoring after calibrating on 10 to 20 calls the team already scored. People should keep spot-checking scores over time.
Does AI call scoring replace QA analysts?
No. It changes their work. Analysts spend less time listening and more time on calibration, flagged calls, disputes and helping supervisors coach.
How long does it take to set up AI call scoring?
With OttoQA, setup takes about 2 weeks. Most of that time goes into calibrating the AI to your form.
What happens when an agent disputes an AI score?
A person reviews the call. If the score was wrong, fix it and check whether the question wording needs to be clearer. Disputes help you improve the form.
About the author
Tom Laird is the founder of OttoQA and CEO of Expivia, with about 30 years in contact centers. He hosts the Advice from a Call Center Geek podcast. Connect with Tom on LinkedIn.