Beyond AHT and FCR: The new metrics for AI-powered contact centers

AI isn’t just changing how contact centers operate internally; it’s rewriting the playbook for customer experience (CX) as a whole. 

Take self-service, for example. A year ago, 75% of service organizations had invested in AI and were using it to power smarter self-service experiences. Today, that percentage is likely even higher. Paired with AI-powered agent assistance, these innovative strategies have become a vital way for organizations to handle growing demand without sacrificing quality or increasing cost.  

The upside for AI in the contact center is huge. Across our own client engagements, we’ve seen returns such as $3 million in cost savings due to faster problem resolution or 388% return on investment generated by AI-enabled customer deflection.  

So why are so many organizations still struggling to show the value of AI — or even measure it accurately?  

74% of organizations struggle to generate tangible value from their AI investment. -Boston Consulting Group, 2024.

The problem starts with contact center metrics. Most contact center KPIs were built for linear human conversations, not dynamic, multi-intent AI interactions. Relying on outdated measurement frameworks can hide experience friction, misdiagnose performance, and even put your brand at risk.

To get it right, we need a new approach: blending traditional metrics with AI-era insights to more accurately measure outcomes in the new customer experience paradigm. Let’s dive into two of the most common contact center KPIs — AHT and FCR — to explore how contact centers can adapt to better assess and amplify AI’s impact in this new reality.

Rethinking Average Handle Time (AHT)

Why it falls short:

For decades, the rule was simple: keep AHT low. In the AI era, that logic no longer holds. A low AHT isn’t always a sign of success. AI-powered interactions often resolve multiple issues in a single conversation — a big efficiency win that naturally increases handle time.

At the same time, AI tends to deflect simple queries, leaving human agents with more complex, time-consuming problems. According to a recent Salesforce report, 77% of agents have noticed they face more complex workloads than just one year ago. In other words, a rising AHT can actually indicate a successful AI deployment as long as case resolution rates remain consistent.

How to evolve this KPI:

Instead of focusing on AHT alone, build a dashboard of related metrics.

• Intents per contact (IPC): How many issues were resolved in one interaction?

• AHT per intent: How much time did each issue take?

• AI resolution rate: What percentage of intents were fully handled by AI?

• AI prompt usage rate: How often do agents use or override AI suggestions?

This multi-dimensional view reveals true efficiency gains and highlights where AI or agent-assist tools need refinement. For example:

• High AHT and high IPC? Likely a good sign — your AI is handling complex, multi-intent conversations.

• Rising AHT and flat IPC? Check AI resolution rates. If they’re low, the issue could be conversation or experience design, in addition to the technical capabilities of the bot itself.

Without these contextual measures, AHT becomes a guessing game. Is low AHT good or bad? The answer is, “it depends.” To prove your AI implementation’s value — and justify additional AI innovation budget — you need more concrete results than that.  

Moving beyond First Contact Resolution (FCR)

Why it falls short:

FCR has long been a go-to metric, measuring whether an issue was resolved in a single contact. But this binary “yes or no” view ignores what matters most: the quality of the customer’s experience.

An interaction can achieve FCR and still feel frustrating. With 73% of consumers willing to leave a brand after just one bad experience, overlooking journey quality is a major risk. FCR also fails to account for some AI successes, like when a customer calls in more frequently because they’re happy with the voice bot experience. These experiences add value (and drive AI containment), but FCR still counts it as a failure.

How to evolve this KPI:

Support this binary assessment with new metrics that capture the resolution path and experience quality.

• Sentiment shifts: Did the customer’s mood improve or decline during the interaction?

• Intent recognition accuracy: How quickly and accurately did AI understand the customer’s need?

• Resolution path quality: A composite score tracking friction points like transfers, channel switches, and repeated questions.

These metrics expose friction that FCR hides. For example:

• High FCR and negative sentiment? You’re solving problems but creating frustration.

• Low FCR and high intent accuracy? AI understands the issue, but lacks the capacity to resolve it — an opportunity for optimization.

• Flat FCR and complex resolution paths? Time to refine bot workflows or improve the information agents use to solve the problem.

Together, these additional measures give FCR the context it needs to reflect AI’s true impact on customer experience.

KPIs: The engine of AI agility

Generative AI is not a "set it and forget it" solution. Its long-term value depends on your ability to measure, learn, and iterate quickly. Relying on outdated KPIs risks misreading AI’s impact, frustrating customers, and missing your ROI targets.

It comes down to this: You can't prove the value of AI if you aren't measuring what matters.

The journey to an agile, AI-powered contact center begins by rethinking measurement. By auditing your current KPIs and introducing more nuanced metrics like AI resolution rate and sentiment shift, you create a dynamic feedback loop. This is the engine of true agility — using data not just to report on the past, but to continuously optimize for the future.  

Leaders who embrace this new mindset will be the ones who build a customer experience that truly sets them apart.

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