AI quality assurance for call centres: Score every call automatically

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A quality manager at a 15-agent contact centre has blocked out Friday morning for call reviews. She has time to listen to six calls. Her team made 300 calls this week. She will review 2% of what happened. The six calls she reviews will form the basis of four coaching conversations next week. The other 294 calls, the ones where agents struggled with a new product question, missed a compliance disclosure, or failed to de-escalate a frustrated customer, are invisible. Not because she chose not to review them. Because there are not enough hours.

Aircall coaches sales reps in real time with AI guidance during live calls, and extends that coaching infrastructure to QA with automated call scoring across 100% of interactions. AI quality assurance does not change the QA process. It changes the coverage. The scorecard is the same. The coaching conversation is the same. What changes is that the manager now has scoring data for all 300 calls, not 6.

Key takeaways

• Manual QA covers fewer than 5% of calls: AI QA scores every call automatically against a defined scorecard

• 41% of contact centres monitor fewer than 4 calls per agent monthly: AI QA eliminates that coverage ceiling

• AI QA accuracy depends entirely on scorecard configuration: validate against manual scores before deploying to the full team

• AI QA handles coverage; the manager handles coaching: neither replaces the other

What is AI quality assurance and how does it differ from manual QA?

AI quality assurance for call centres is the process of automatically scoring every call against a defined QA scorecard using AI, without a human reviewer listening to each call. The difference from manual QA is coverage: manual QA samples a fraction of calls because human review time is finite; AI QA scores every call because the scoring is automated. The process, scorecard, evaluation, coaching conversation, is the same. What changes is the number of calls it applies to.

AI quality assurance (AI QA) is the automated application of a defined QA scorecard to every call in a contact centre, using AI to evaluate each scorecard question against the call transcript and assign a score without a human reviewer, replacing the manual sampling step that limits most teams to fewer than 5% QA coverage.

Contact centre benchmarks compiled by Plivo in 2025, drawing on Call Centre Helper's industry research, confirm that the QA scoring standard is 4 calls per agent per month, and that a significant share of contact centres fall below even that level. A 15-agent team making 300 calls per day reviewed at that rate covers roughly 60 calls per month out of 6,000 made, 1% coverage. AI QA scores all 6,000.

Call Centre Helper's expert guidance on QA sample size confirms that the optimal manual QA sample is 5-6 calls per agent, but that setting a universal volume target without a strategic approach wastes resources. The value of AI QA is not simply that more calls are reviewed. It is that every call is scored consistently, and human review is focused on the calls AI flags as most relevant for coaching.

QA coverage rate is the percentage of all calls that are reviewed and scored against a QA rubric in a given period. For a team making 6,000 calls per month that reviews 60 manually, the QA coverage rate is 1%. AI QA raises that rate to 100% without increasing analyst headcount, which changes the entire basis of performance visibility and coaching prioritisation.

How does AI call scoring actually work?

AI call scoring works in four steps. First, the call is transcribed from audio to text. Second, the AI evaluates each question on the QA scorecard against the transcript: for binary questions, it determines whether a required phrase or action occurred; for rating questions, it evaluates the interaction against the defined criteria. Third, a score is calculated and written to the call record. Fourth, calls meeting a manager-defined threshold are flagged for human review and added to the coaching queue.

The accuracy of both question types depends on how precisely the scorecard defines what the AI should evaluate. A rubric that asks "was the agent professional?" gives the AI nothing specific to assess. A rubric that asks "did the agent avoid interrupting the customer during the first 30 seconds of the call?" gives the AI a specific, evaluable criterion it can identify in the transcript. Scorecard design is the most important step in AI QA implementation, more important than the tool itself.

Automated call scoring is the process of using AI to evaluate every call against a predefined QA rubric automatically when the call ends, producing a numeric score without a human reviewer. It makes QA coverage scalable by removing the per-call time cost of manual review while applying the same criteria consistently across every interaction.

How do you build a QA scorecard that AI can score reliably?

A QA scorecard that AI scores reliably has three design principles: most questions are binary or rule-based rather than subjective, each question defines precisely what the AI evaluates rather than describing an outcome, and subjective questions requiring human judgment are flagged for analyst review rather than AI scoring. The scorecard is the single biggest variable in AI QA accuracy, a well-designed scorecard produces scoring that managers trust; a poorly designed one produces scores that feel arbitrary and causes teams to abandon the tool within the first month.

QA scorecard is the structured evaluation framework used to assess call quality, consisting of specific questions each scored on a defined rating scale (yes/no, 1-5), applied consistently across every reviewed call; in an AI QA context, questions must be designed around criteria the AI can evaluate from a transcript rather than those requiring human judgment about tone or emotional nuance.

1. Binary compliance questions (high AI accuracy): "Did the agent confirm the customer's account number before discussing account details?" (yes/no), the AI searches the transcript for this step and scores accordingly

2. Specific behavioral questions (high AI accuracy): "Did the agent avoid interrupting the customer in the first 60 seconds?", evaluable against transcript timing and turn structure

3. Script adherence questions (high AI accuracy): "Did the agent use the required product disclosure language within the first 3 minutes?", evaluable against a defined phrase or phrase equivalents

4. Outcome questions (moderate AI accuracy): "Did the agent confirm the next step before ending the call?", evaluable but depends on how precisely the required outcome is defined in the rubric

5. Subjective quality questions (low AI accuracy, flag for human review): "Was the agent empathetic throughout the call?", reserve these for the flagged calls that a human reviewer follows up on

6. Validation step (required before full deployment): compare AI scores to analyst scores on 20-30 calls; the gap on each question reveals which criteria need refining before the AI scores are trusted

Calibration is the process of comparing AI QA scores to manual scores from an experienced analyst on the same set of calls, before the AI scoring is trusted for coaching decisions. Calibration reveals which scorecard questions are ambiguous or poorly defined, the questions where AI and analyst scores diverge most are the ones that need refinement before deployment.

What is the implementation sequence for AI QA in a call centre?

AI QA implementation has five steps in a defined sequence. Skipping or reordering any step produces unreliable output. The most common implementation failure is enabling the tool before completing steps 1 and 2, which produces automated scores without a validated rubric and creates the "the scores feel arbitrary" problem that causes teams to abandon AI QA before it has been properly configured.

• Step 1: Configure the scorecard. Define each QA question with a binary or specific rubric, assign a rating scale (yes/no or 1-5), and set a preliminary flag threshold. Do not enable AI scoring until the scorecard is complete. A scorecard with 8-12 questions, majority binary, takes approximately one day to build.

• Step 2: Validate on 20-30 calls. Have an experienced QA analyst score the same calls the AI will score, then compare. The gap on each question reveals which criteria are ambiguous and need refinement before the AI scores are trusted.

• Step 3: Calibrate the threshold. Set the coaching flag threshold based on validation data, not the default setting. The threshold should trigger a review on calls that the validation showed were genuinely below standard, not an arbitrary percentage of all calls.

• Step 4: Deploy and observe for 30 days. Run AI scoring across the full team without using the data for formal coaching yet. In a platform like Aircall, AI-powered call scoring and QA is configured in the same dashboard as the phone system and recordings, the scorecard, the transcript, and the AI score are all in one interface, which removes the integration complexity that slows deployment when QA is a separate tool. The first 30 days identifies systematic scoring anomalies and gives managers time to calibrate their interpretation of the scores before using them in performance conversations.

• Step 5: Use flagged calls as coaching starting points. The score identifies which call to review. The conversation with the agent is where coaching actually happens. How Aircall's AI call scoring helps managers coach teams faster and more consistently covers how to structure those conversations around scorecard data rather than subjective impressions.

What results should you expect from AI quality assurance?

AI QA produces two phases of results. In the first 30-60 days, managers have QA coverage data across 100% of calls for the first time. Coaching conversations shift from reactive (addressing the call someone happened to review this week) to proactive (addressing the pattern the AI flagged across dozens of calls this month). Aggregate QA scores often appear more consistent than manually-sampled scores suggested, because manual sampling inadvertently selected easier-to-review calls rather than representative ones.

In months 2-6, as coaching conversations become grounded in AI QA data, aggregate scores improve and performance gaps between top and bottom quartile agents narrow. BCG's research on AI agents in customer service confirms that AI-driven improvements in resolution accuracy reduced repeat inquiries by 25%, establishing that the coaching quality improvement enabled by higher QA coverage directly affects the customer-facing metrics managers are accountable for.

A concrete Phase 1 scenario: a manager who previously reviewed 6 calls per week now has AI scores for 300 calls per week. Her coaching conversations are no longer based on the calls she happened to pick. They are based on the calls the AI flagged as below the threshold on compliance or call quality criteria. A concrete Phase 2 scenario: agents receiving consistent, criterion-specific feedback on the same scorecard questions every week, rather than occasional impression-based feedback from the 2% of calls their manager had time to review, show measurable score improvement on those specific criteria within 60 days.

18 best practices for call centre quality assurance covers how to structure the full QA process around AI scoring data, including how to run calibration sessions, how to document coaching conversations, and how to track aggregate score improvement over time.

Frequently asked questions

What is AI quality assurance for call centres?

AI quality assurance for call centres automatically scores every call against a defined QA scorecard using AI, replacing the manual sampling step that limits most contact centres to fewer than 5% coverage. It gives managers coaching data across 100% of calls without additional QA analyst time.

How accurate is AI call scoring?

AI call scoring is highly accurate for binary, rule-based questions, required greeting, disclosure script, callback number captured. It is less reliable for subjective questions involving tone or empathy. Design the scorecard with specific, binary questions to maximise AI scoring accuracy.

How many calls should a call centre review for quality assurance?

The industry standard is 4-6 calls per agent per month for manual QA. Most contact centres fall below this, with 41% monitoring fewer than 4 calls per agent monthly. AI QA eliminates this ceiling by scoring every call automatically, giving managers full performance visibility without increasing QA analyst headcount.

Does AI QA replace manual call monitoring?

No. AI QA replaces the manual sampling step that limits coverage to a small fraction of calls. It does not replace the human judgment required to interpret scores, design coaching conversations, or handle complex performance issues. AI QA handles coverage; the manager handles coaching. Both are required for effective QA.

How long does it take to implement AI quality assurance?

Technical setup takes 1-2 days. Meaningful implementation, scorecard configuration, validating AI scores against manual scores on 20-30 calls, calibrating thresholds, takes 2-4 weeks. Deploy to the full team only after validation. The first 30 days produces calibration insights, not performance verdicts.

What is the best AI sales coaching software?

The best AI sales coaching software coaches reps in real time during live calls with sentiment signals, objection prompts, and script guidance, then extends that coaching to QA with automated call scoring after every call. Aircall coaches reps in real time with AI guidance alongside full QA coverage.

What we are

Where do you start with AI QA, and what coaches the team once it is live?

The teams that get useful output from AI QA in the first 30 days are the ones that built the scorecard before switching the tool on. The teams that disable it after a month are usually the ones that enabled it without a configured scorecard and got scores that felt disconnected from the coaching conversations they already knew how to have.

The starting point is not the tool. It is a scorecard with 8-12 questions, most of them binary, all of them specific enough that the AI knows exactly what to evaluate. That takes a day to build. The validation period takes two weeks. After that, the team has AI QA data across 100% of calls and a coaching process grounded in something more reliable than six randomly sampled calls per manager per week.

How to implement contact centre AI without large IT resources or budget covers the full AI rollout sequence for contact centres starting from minimal automation, including how to introduce QA scoring without disrupting existing workflows.

Published on July 10, 2026.