AI call center KPIs: rethinking traditional metrics

AI agents are increasingly replacing human operators in customer service. They’re polite, fast, tireless – and capable of handling thousands of conversations at once. But the real question isn’t whether they can speak. It’s whether they can actually deliver.

When a call centre is powered by AI, traditional performance metrics no longer apply in the same way. Legacy KPIs like response time, call volume, and customer ratings still matter – but they need rethinking. And they’re joined by a new set of indicators: intent recognition accuracy, self-resolution rate, escalation frequency, and business impact.

In this article, we explore which metrics truly reflect the effectiveness of an AI agent, how to track and interpret them, and – most importantly – how they translate into real, measurable outcomes. With real-world examples, we show how numbers become actionable insights.

Metrics have changed – because the nature of call center service has changed

In traditional contact centres, performance metrics were people-centric. We cared about how polite the agent was, how long they took to resolve an issue, and whether they could solve it on the first try. These KPIs remained relevant – as long as humans were the ones answering the calls.

With the arrival of AI agents, everything shifted. Automation has raised the standard of service, and with it, the way we measure quality has changed. Traditional metrics still matter, but they now need to be interpreted differently. For instance, response time becomes irrelevant – an AI agent replies instantly, with no hold time. What matters more is tone, contextual relevance, personalisation, and the ability to set the right tone from the very first phrase.

The number of handled enquiries is no longer tied to the size of the team. A single AI agent can manage thousands of conversations at once – but volume alone is no longer a success indicator. What really counts is how many of those interactions were completed without human involvement, how accurately the AI recognised customer intent, and how often it had to “give up” and escalate to a live agent.

This calls for a new logic of quality assessment. While a human agent can improvise, show empathy, or rephrase on the fly, an AI follows a defined scenario. Its effectiveness depends on how well that scenario handles ambiguity – unexpected phrasing, interruptions, emotional tone, and edge cases.

In one project for a telecoms provider, the initial AI deployment struggled. The scripts were based on structured CRM data, but failed to reflect how customers actually voiced their complaints. As a result, the AI agent couldn’t understand even basic queries. Only after the team listened to dozens of real calls and reworked the flow to match natural speech did intent recognition improve dramatically – and automation efficiency shifted from abstract figures to real-world impact, measured in fewer escalations and freed-up phone lines.

In other words, with AI we’re not only measuring outcomes – we’re also assessing how resilient the system is to uncertainty. And that’s why we need a different perspective on metrics: not as tools for controlling staff, but as a way to answer a more fundamental question – is the automation genuinely working better, faster, and more effectively than a human?

Classic metrics in a new context: how they work when the agent is AI

Many of the KPIs we associate with human-operated call centres still apply in the AI call centre – but how we interpret them changes significantly. An AI agent doesn’t pick up the phone, doesn’t pause, and doesn’t get tired – and this requires a different lens when analysing familiar metrics.

First Response Time (FRT) for an AI agent is practically zero. It responds instantly. But that fact alone says little about the quality of the interaction. What matters is how the conversation begins: does the greeting sound friendly, is there any personalisation, and does the customer feel engaged from the first second? In this context, it’s not about speed – it’s about appropriateness and tone.

Average Handle Time (AHT) also needs a rethink. In a manual setting, a short call is often seen as efficient. But in an AI call centre, if a conversation lasts 25 seconds and ends with an escalation, that’s not a success – it points to a weak or incomplete scenario. A longer exchange that ends in a successful resolution is a far better sign of the AI agent’s resilience and flexibility. In other words, a short call doesn’t always mean a good outcome.

First Contact Resolution (FCR) is perhaps one of the most reliable indicators of how mature an AI-powered service truly is. If the agent resolves the issue on the first attempt, it shows that the system not only understands the query, but also has access to the right data, logic, and structured flow to deliver a complete result. Here, completeness is as important as accuracy – the customer shouldn’t need to call back or ask to speak to someone.

CSAT (Customer Satisfaction Score) and NPS (Net Promoter Score) remain relevant, but the way they are collected changes. AI agents allow for lightweight surveys to be embedded directly into the conversation: after an issue is resolved, the customer can be prompted for feedback, and responses are gathered automatically. What matters is not just the average score, but the context – how ratings vary by scenario, customer segment, time of day, or query type.

In short, traditional metrics still serve a purpose – but they now describe the behaviour of a system, not a person. And what we should be looking for in these numbers isn’t simply “faster” or “shorter” – it’s stable, accurate and effective.

New metrics for a new reality: how to measure AI agent effectiveness

When customer service is handled by an AI agent, much of the traditional evaluation framework needs to be reconsidered. We still care about response time, resolution, and customer satisfaction – but these alone are no longer enough. Automation requires different criteria – ones that reflect the system’s ability to understand, solve, adapt, and deliver tangible business results.

The first metric that becomes critical is intent recognition accuracy. An AI agent may respond quickly and politely, but if it fails to understand why the customer has reached out, its efficiency collapses. This metric – the ability to correctly identify intent – becomes the foundation for everything that follows.

Next is the agent’s ability to resolve enquiries independently. If the AI simply asks for clarification but ends up escalating every second call to a human, then meaningful automation hasn’t yet been achieved. The containment rate measures how often the AI resolves issues without human input – but it must always be viewed in context. If the AI “closes” the conversation but the customer calls back or leaves frustrated, the job isn’t truly done.

Another vital metric is the escalation rate – the frequency with which the AI passes a conversation to a human agent. This might happen due to complex queries, emotional responses, or poorly designed scenarios. A rise in this figure signals the need to refine flows or improve speech understanding.

Also important is the system’s ability to handle multi-intent interactions. Traditional voice scripts often break down when a customer tries to do more than one thing at once – like checking order status and updating their address. The AI’s ability to resolve multiple intents in a single exchange reflects the flexibility and robustness of the system.

Finally, there’s a new layer: emotional resilience. Even if the issue is resolved, if the customer ends the call feeling frustrated, the overall experience suffers. That’s why tracking sentiment drift – how the emotional tone of the conversation shifts from start to finish – becomes key.

Together, these metrics allow teams not just to monitor AI performance, but to actively manage the quality, adaptability, and impact of the digital channel.

Let’s imagine an AI agent deployed in a national postal service, handling inbound calls related to typical customer enquiries:

• “Where is my parcel?”
• “Can I change the delivery date or address?”
• “When will the courier arrive?”

Over the course of a month, the AI agent processed 10,000 calls, of which:

• 9,000 intents were correctly identified (IRA = 90%)
• 6,200 enquiries were resolved without human involvement (Containment Rate = 62%)
• 5,700 were fully resolved at first contact (FCR‑AI = 57%)
• 3,800 required escalation to a human operator (Escalation Rate = 38%)
• The average call duration was 55 seconds

Interpreting the results

High intent recognition (IRA = 90%) The AI agent demonstrates a strong ability to understand the nature of customer enquiries – a critical factor in logistics, where callers may express themselves emotionally (“Where’s my courier?”, “I’m sick of waiting!”). Recognising the intent behind such input – whether it’s a delivery request, complaint, or rescheduling – is essential to routing the call correctly.

62% containment rate Nearly two-thirds of enquiries are resolved automatically. This means the AI agent is able to handle high-volume questions like “When will it arrive?” or “Where’s my order?” by pulling data from a CRM or parcel tracking system. In logistics, where such queries are repetitive, this level of automation delivers major efficiency gains.

57% first contact resolution (FCR‑AI) The 5% gap between containment and FCR‑AI suggests that some calls marked as “resolved” didn’t actually solve the customer’s problem. The agent may have provided information, but the customer still needed to call back or ask for a human. This points to optimisation opportunities – especially for scenarios involving address changes, rescheduling, or cancellation.

38% escalation rate One in three calls still needs to be transferred to a human. This is understandable in more complex cases – lost parcels, courier complaints, same-day route changes. While not a bad figure, this rate could be improved with more advanced flows, especially given the strong intent recognition already in place.

Average call duration: 95 seconds This is a healthy figure. The AI agent isn’t wasting time, but also isn’t cutting the customer off too quickly. In logistics, where queries tend to be specific and outcome-driven, this balance of brevity and clarity is ideal.

When do metrics really matter – and are they always necessary?

When a company launches an AI agent in its customer service operation, one of the first questions is usually: what metrics should we track? It’s a natural instinct – especially in a digital environment where everything can be measured. But before deciding what to measure, it’s essential to clarify why the AI is being implemented in the first place. Because metrics only make sense when they’re aligned with a goal.

If the objective is to cut costs, increase throughput, or ease the workload on human agents, then metrics are essential. You need to understand how often the AI agent resolves issues without escalation, how many interactions are successfully completed at first contact, how many are handed over to humans, and what financial impact that delivers. In these cases, metrics act as a compass – they allow you to compare “before and after,” optimise flows, and demonstrate project viability. This becomes especially important at scale, where weak logic or a poorly designed flow can lead to resource waste and reduced service quality.

But there are other scenarios where metrics play a more limited role. If the project has a narrow, task-specific goal – improving debt recovery, sending mass notifications, or confirming account status – then you only need to measure what’s directly tied to that goal. In such cases, one or two metrics are enough: number of successful connections, confirmed intent to pay, or conversion to action. Deep analysis of satisfaction scores, handling time, or sentiment is unnecessary – all that matters is whether it works.

Metrics can also be simplified – or even deferred – in pilot projects or MVP stages. At that point, it’s more useful to uncover friction points, understand how customers phrase their requests, and test whether the scenario logic holds up in practice. Listening to calls and gathering qualitative feedback often matters more than detailed numerical reporting. The same applies to experience-led projects. If the goal is to make customer interactions more human, softer, and more personal, then standard KPIs like containment rate or AHT may not reflect success. Instead, you may need to track customer perception, emotional tone, conversational flow, and likelihood to recommend.

The key thing to remember: there’s no such thing as a universal set of metrics. One project might need just a single number – for example, uplift in successful payments. Another may require a whole system of parameters to assess the adaptability and consistency of the AI agent. Metrics only come to life when they’re rooted in context. If the business knows what it’s trying to achieve, a metric becomes a tool for action. Without that clarity, even perfect data won’t lead to insight.

A metric is meaningful only when it helps answer the essential question: is what we’re doing actually working – and how can we make it better?

Why it all comes down to ROI – because you know how to count

The official reasons for implementing AI agents in customer service may vary – to enhance the customer experience, accelerate service, or ensure availability during peak hours. But in practice, nearly every mature automation project in the call centre space comes down to one thing: efficiency. Above all, financial efficiency.

A modern call centre is not just a room full of agents – it's a complex infrastructure that can be costly to maintain, especially when built on outdated approaches.

Here are just a few of the recurring costs businesses face:

Workstations and infrastructure. Every new agent means additional space, furniture, hardware, utilities, cleaning and security.

Software and licences. CRM systems, telephony, monitoring tools and cloud services all require paid licences and ongoing maintenance.

Training and onboarding. High staff turnover is common in the industry, making recruitment, onboarding, mentoring and compliance training a constant burden on resources.

Management and oversight. The larger the team, the greater the need for shift planning, quality assurance, internal support, and conflict resolution.

Reduced performance during peak hours. When demand spikes, service capacity often drops, leading to missed calls, customer frustration, and follow-up calls – all of which inflate the cost per contact.

Automation doesn’t eliminate the team – it makes the overall model more scalable, stable and manageable. AI agents don’t need physical desks or software licences, and they perform consistently even during traffic surges. What’s more, their performance leaves a clear digital trail for analysis.

Apifonica delivers projects that help companies operating traditional service models – such as call centres with around 20 agents – restructure their operations through AI automation. By automating repetitive conversations, integrating with CRMs and offloading routine queries, businesses can ease the pressure on live agents and realise savings of up to €540,000 within the first year. These projects show that automation can generate ROI from the start – especially when dealing with high-volume, predictable scenarios that can be formalised into clear, reusable flows.

Conclusion: how do you measure what didn’t exist before?

AI call centres have introduced not only a new technology, but a new way of looking at customer service. We're no longer limited to metrics designed for evaluating human performance. Instead, the focus has shifted to conversational flows, system behaviour, intent recognition accuracy, scenario flexibility, and the overall impact on business processes.

Effectiveness today is made up of many components: speed, consistency, automation coverage, customer engagement, and the ability to complete tasks without human intervention. Most importantly, it comes down to whether all of this supports a specific business goal – whether it’s reducing costs, increasing revenue, or improving customer retention.

Metrics in the AI channel act as a compass. They don’t simply replicate traditional KPIs – they define a new coordinate system that enables businesses to make precise, evidence-based decisions.

To deliver results, an AI agent needs more than just deployment – its performance must be measured. And not just through numbers, but in context: why it exists, what problem it solves, and how well it solves it. That’s when metrics move beyond reporting – and become a driver of growth.

Let’s talk about your AI CALL CENTRE project? Book a 30-minute call with one of our experts.