• Legacy contact center metrics such as Average Handle Time (AHT) and First Call Resolution (FCR) can actively harm customer experience in an AI-enabled world.
  • Rebecca Wettemann of Valoir argues organizations must shift to outcome and effort-based metrics, plus measure handoff friction and agent experience.
  • AI can reduce repetitive work and improve routing, but measuring the wrong KPIs risks faster, not better, interactions.

Why AHT and FCR No Longer Tell the Full Story

Rebecca Wettemann, principal at Valoir, says many contact centers still optimize for legacy KPIs—most notably Average Handle Time (AHT) and First Call Resolution (FCR). Those measures were useful in a voice-centric, queuing model. But as contact centers adopt conversational AI, omnichannel engagement, and automated back-office work, these single-dimension metrics can mislead managers and damage long-term customer loyalty.

AHT encourages speed over quality: an agent (or bot) that cuts a call short to hit a target might leave issues unresolved, generating repeat contacts and frustrated customers. FCR can look good on paper while masking poor outcomes—if a resolution requires multiple digital touches or delayed follow-up, counting only the first contact hides the true customer journey.

What to Measure Instead

Wettemann recommends shifting measurement toward outcomes and experience rather than raw throughput. Practical alternatives include:

Customer-focused metrics

  • Customer Effort Score (CES): tracks how hard the experience was for the customer, which correlates strongly with loyalty.
  • Time to Resolution/Time to Value: measures how quickly a customer gets the outcome they need, across channels.
  • Recontact and Escalation Rates: follow-up contacts and escalations reveal whether an initial interaction truly solved the problem.

Quality and conversational metrics

  • Resolution Quality or Outcome Accuracy: assesses whether the customer’s problem was actually fixed, not just whether the call ended.
  • Sentiment and Intent Resolution: uses voice or text analytics to see if sentiment improves and intents are resolved across a conversation.

Operational and human metrics

  • Handoff Success and Friction: measures the frequency and outcome of transfers between bots and humans or between teams.
  • Agent workload and cognitive load: tracks how automation affects agent stress and ability to handle complex issues.

How AI Changes the Measurement Landscape

AI can automate routine tasks, surface recommended actions for agents, and provide frictionless routing. But these gains are only visible if organizations redefine KPIs. Wettemann warns that applying AI without changing what you measure lets old incentives persist—agents and systems will still be pushed to minimize handle time rather than maximize resolution quality.

Why This Matters

Shifting metrics has direct business impact: better measurement helps reduce repeat contacts, improve customer satisfaction, and free agents for higher-value work. It also helps leaders spot where AI actually improves outcomes versus where it merely speeds an interaction.

Contact centers moving to AI should audit their KPIs, instrument outcomes across channels, and add measures for handoff friction and agent experience. That combination, Wettemann argues, is what will turn AI from a cost-cutting tool into a genuine customer experience multiplier.

Image Referance: https://www.cxtoday.com/ai-automation-in-cx/modernizing-the-contact-center-with-ai-rebecca-wettemann-on-rethinking-metrics-handoffs-and-agent-experience/