AI Call Scoring: Can Software Tell You Which Calls Were Handled Well?

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What is AI call scoring?

AI call scoring evaluates a call against criteria that your business has chosen, such as whether the team member identified the caller’s need, explained the next step clearly or followed a required process. The software then produces scores against those criteria and an overall result.

A modern scoring workflow usually has three parts:

Research into automated call coaching describes a similar approach: using managers’ quality-assurance questions to identify calls that are suitable for coaching, rather than treating the technology as a replacement for a supervisor’s judgement. The AI Coach Assist research paper was published in 2023 and focuses specifically on recommending calls for agent coaching. (arxiv.org)

That makes AI call scoring different from simply measuring call volume, duration or missed calls. Those figures describe activity. A scorecard is intended to assess the quality of a particular interaction against an agreed standard.

How does a call scoring rubric work?

A call scoring rubric turns vague expectations such as “sound professional” into observable behaviours that a reviewer can reasonably identify. Each criterion should explain what good performance looks like and how it will be scored.

For example, “build rapport” is too broad on its own. A more useful criterion might be: “The employee acknowledged the caller’s stated concern before moving to a recommendation.” That gives the manager and employee something specific to discuss.

A practical rubric for a small business might look like this:

This is deliberately short. A 20- or 30-point scorecard may look thorough, but it can become difficult to maintain, calibrate and explain. The better question is not “How many things can we measure?” but “Which behaviours would make the next call better?”

A useful rubric should also allow not applicable. For example, a caller asking for a delivery update should not be marked down for failing to discuss a new product. Call type matters, and the scorecard should not penalise an employee for not performing an action that was irrelevant to the conversation.

What should you include in an AI call scoring rubric?

Include behaviours that are important, observable and connected to the outcome you want to improve. Avoid criteria that depend mainly on personality, accent, speaking style or whether the employee sounds like the manager.

For most SMBs, start with five to eight criteria:

The sales call scorecard guidance from Amotions, published in 2026, makes the useful distinction between generic impressions and observable behaviours such as discovery depth, question quality, listening and next-step discipline. It also recommends keeping the criteria limited enough for managers to use consistently. (amotionsinc.com)

For coaching sales calls, you might use criteria such as:

For service calls, the rubric may focus more on ownership, accuracy, explanation and resolution. The same scorecard should not be used unchanged for sales, reception, accounts and technical support.

Can AI tell whether a call was handled well?

AI can identify whether a call appears to match defined criteria, but it cannot decide perfectly whether the interaction was “good” in every human or commercial sense. The quality of the result depends on the rubric, the available evidence, the type of call and the manager’s review.

This is where many businesses overestimate the score. A high number does not necessarily mean the customer was satisfied, and a low number does not automatically mean the employee performed poorly. A caller may have been frustrated by a policy the employee could not change, or a technically complex issue may have required a longer conversation.

Treat the score as a signal, not a final decision. A sensible process is:

This approach is consistent with Fair Work’s guidance on managing performance, which recommends clear expectations, specific feedback, support, coaching and follow-up rather than relying on unexplained conclusions. (fairwork.gov.au)

Is AI call scoring better than manual call quality monitoring?

AI call scoring can make review more consistent and help a manager identify patterns, while manual review remains important for context, fairness and calibration. For a small business, the practical answer is usually a combination rather than an either-or choice.

A 2010 multilevel study of call-centre employees found that the amount of coaching received predicted objective performance improvements over time. The finding does not prove that every AI scoring system will improve results, but it supports the broader principle that measurement is most useful when it leads to regular, constructive coaching. Read the supervisor coaching study in Personnel Psychology. (onlinelibrary.wiley.com)

For a small team, you might use AI scoring to surface two or three calls per person each month, then have a manager review the evidence and hold a short coaching discussion. That is more practical than treating every score as a formal performance event.

How should a manager use an AI call score?

A manager should use an AI call score to choose the next coaching conversation, not to make an automatic employment decision. The best coaching discussion is specific, balanced and focused on a behaviour the employee can change.

A simple five-minute structure works well:

1. Start with the purpose

Explain that the review is about improving a customer interaction or sales behaviour. If employees believe the system exists mainly to catch them out, they are less likely to treat it as useful feedback.

2. Ask for their view first

Before presenting the score, ask how they thought the call went. This may reveal missing context, a system problem, an unusual customer request or an issue with the rubric.

3. Discuss one strength and one opportunity

Avoid presenting a long list of faults. For example: “You clarified the delivery issue well. Next time, confirm the action and timing before ending the call.”

4. Practise the replacement behaviour

If the issue was a weak close, practise two alternative closing questions. If the issue was an incomplete discovery conversation, role-play the first three questions the employee could ask.

5. Set a follow-up point

Review a later call to see whether the behaviour changed. Fair Work’s performance guidance recommends clear expectations, documented discussions, employee input and follow-up when managing performance. (fairwork.gov.au)

The manager should also calibrate the scorecard regularly. If two reasonable reviewers would score the same call very differently, the problem may be the wording of the rubric rather than the employee’s performance.

Could AI call scoring become workplace surveillance?

Yes, depending on how it is introduced and used. The risk increases when a business collects information without clear notice, scores employees against hidden criteria or uses automated results as the sole basis for warnings, targets or dismissal.

Australian workplace surveillance requirements are not identical in every state and territory. The Office of the Australian Information Commissioner explains that employers must consider relevant Australian, state and territory laws, including laws that apply to monitoring or recording telephone conversations. (oaic.gov.au)

In NSW, the Workplace Surveillance Act 2005 includes written notice requirements for employee surveillance, including details such as the type of surveillance, how it operates and whether it is continuous or intermittent. (legislation.nsw.gov.au) The ACT has its own Workplace Privacy Act 2011, while Victoria has recently examined whether its workplace surveillance framework keeps pace with current technology. (legislation.act.gov.au) (vic.gov.au)

That means a workplace surveillance notice may be relevant, but the exact obligation depends on your location, system and proposed use. Whether a particular Act applies to audio or AI-based scoring should be checked with an Australian workplace relations or privacy adviser before implementation.

This article is general information, not legal advice. Questions about call recording and capture belong in is call recording legal in Australia?, rather than being repeated here.

A practical, coaching-first policy should explain:

How can a small business introduce AI call scoring safely?

Start with one call type and a small rubric, then test the results before using them across the business. The first objective should be learning whether the scorecard produces fair, useful coaching conversations.

A sensible rollout could look like this:

Do not begin by setting a minimum score for every employee. First establish whether the rubric is reliable enough for your business, and whether different call types need different criteria.

Also avoid making the score the only measure of performance. Customer outcomes, accuracy, teamwork, workload, product knowledge and the difficulty of the call all matter. AI call scoring should narrow the manager’s attention to useful examples, not pretend to replace management.

If your operation is expanding beyond a small office phone system into queues, service levels, reporting and structured quality assurance, read when does a standard phone system become a contact centre phone system in Australia?. For broader phone-system context, see the AI business phone system guide.

What should Australian SMBs look for in call scoring software?

Look for transparent scoring criteria, evidence behind each result and controls that let a manager review or correct an unsuitable score. A polished dashboard matters less than whether the system helps your team have better conversations.

Before choosing a platform, ask:

Think of the product as an assistant for call quality monitoring, not an automated manager. The right system should reduce repetitive review work while leaving responsibility for interpretation and people decisions with a human manager.

NexGen can show you what call intelligence looks like in the context of an AI phone system. See what call intelligence looks like on the AI phone system.

Frequently asked questions about AI call scoring

Is AI call scoring the same as recording every call?

No. AI call scoring is the assessment layer that applies a rubric to a call, while recording and capture are separate issues governed by the system setup and applicable law. For Australian requirements, review the call recording legal guide and obtain professional advice for your situation.

What is a good call scoring rubric for a small business?

Start with five to eight observable behaviours linked to your customer or sales process. Include clear scoring anchors, allow “not applicable” where appropriate and require a manager to review unusual or disputed results.

Should AI call scores be used for employee warnings?

They should not be the sole basis for a warning or other serious employment decision. Use the score as one piece of evidence, check the call and context, give the employee an opportunity to respond, and follow a fair performance-management process.

Can AI call scoring help with coaching sales calls?

Yes, if the rubric focuses on behaviours such as discovery questions, listening, objection handling and agreeing on the next step. The score should lead to a specific practice activity and follow-up review, not simply a pass-or-fail label.