Artificial Intelligence is reshaping customer service and challenging traditional metrics. We take a look at the new KPIs for AI in contact centers.
For years, KPIs such as Average Handling Time (AHT) or First Contact Resolution (FCR) were sufficient. Today, however, new metrics are needed. Automation and AI are transforming not only processes but also the role of human agents.
In this article, we’ll explore which KPIs for AI in the contact center are truly relevant, how businesses can measure AI performance, and why traditional KPIs need a fresh perspective.
Why classic KPIs are no longer sufficient
Traditional performance metrics have a limitation: they are designed solely to measure human work. With AI in the mix, many of these benchmarks shift:
- Average Handling Time (AHT) often rises, since agents now handle more complex inquiries while AI resolves routine cases. In this context, longer handling times actually indicate higher efficiency – not inefficiency.
- First Contact Resolution (FCR) loses clarity. Many issues are solved directly by AI, while others go to agents. That’s why it’s critical to distinguish between FCR achieved by AI and FCR achieved by human agents.
- Average Response Time (ART) becomes less meaningful when inquiries are answered instantly through automation.
Traditional KPIs still matter, but they need reinterpretation and must be complemented by AI-specific metrics.
New KPIs for AI in the Contact Center
AI Resolution Rate
Measures how many inquiries are fully resolved by AI without human involvement. A high AI Resolution Rate indicates significant relief for agents from repetitive tasks.
Customer Effort AI
Assesses how seamless it is for customers to interact with AI. Factors include escalations, repeated attempts, or transfers to a human agent.
Cost per Resolution
Calculates total costs relative to successfully resolving a request. The more inquiries handled automatically, the lower the overall cost.
Throughput
Shows how many requests or tasks an AI solution can process within a given timeframe.
AI User Satisfaction
Captures customer satisfaction with AI interactions – via feedback tools or quick in-dialog surveys. This makes it possible to directly measure AI’s contribution to the customer experience.
Practical Example: Automation and First Contact Resolution with AI
A contact center with high call volumes introduces voicebots to ease the workload of agents.
When looking at traditional KPIs, the numbers seem disappointing: First Contact Resolution (FCR) appears to be just 30% – significantly lower than before.
The reason: many inquiries handled initially by AI were forwarded to agents. In standard FCR measurement, this gives the impression that fewer cases are resolved at first contact.
But once the AI Resolution Rate is factored in, the picture changes: 40% of standard cases were fully resolved by voicebots. Adding these to the FCR reveals a true first contact resolution rate of 70%.
Automation is redefining how we must interpret KPIs in the call center. Traditional KPIs underestimate the impact of AI – only new metrics uncover the real ROI.
The outcome: better customer experiences, more satisfied employees, and a measurable return on investment.
Measure AI Performance
KPIs for AI in the Contact Center demand a new mindset.
Metrics such as AHT or FCR still matter, but they must be reinterpreted and combined with new AI-focused KPIs.
Companies that actively measure AI performance and update their success metrics unlock the true value of automation in the call center: streamlined processes, higher customer satisfaction, and stronger employee engagement.
New KPIs are just the beginning. The next step is to use AI in ways that simplify processes and ease workloads for people. We’ll support you along the way.