Contact Center AI Success Now Hinges on Problem Resolution, Not Call Volume

The emerging standard for measuring contact center artificial intelligence effectiveness has shifted away from volume metrics toward tangible customer outcomes. Bob Laliberte, principal analyst for networking and observability at theCUBE Research, and Zeus Kerravala, principal analyst and founder of ZK Research, reached this conclusion while reflecting on insights from Cisco Systems Inc., Talkdesk Inc., Zoom Communications Inc. and Five9 Inc. during "The AI ROI in Contact Center Summit," an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. The discussions across these platforms and deployment models converged on a shared vision: end-to-end problem resolution underpinned by connected data infrastructure, governance frameworks and demonstrable business results.

Resolution and resolution quality is the new unit of value. Agentic systems should be judged on whether the customer's needs were completed — and completed actually across the full journey.

Zeus Kerravala

Moving beyond conventional performance indicators like containment rates and call deflection, organizations must adopt comprehensive measurement systems that encompass customer satisfaction, effort reduction, employee productivity, operational cost and revenue impact, the analysts explained.

Connected data and governance underpin AI results

This reorientation elevates the importance of data integration and contextual awareness. When systems operate in isolation and information grows outdated, AI systems lose accuracy, generate redundant customer interactions and can amplify existing process failures rather than resolve them.

If you've got a broken process, you're going to get to that bad destination faster.

Zeus Kerravala

The analysts advised organizations to begin with a narrowly defined, high-impact use case rather than attempting a wholesale overhaul of the entire customer experience workflow. By establishing initial performance baselines, applying AI to that specific process and quantifying improvements, companies can validate their approach before scaling to additional areas.

Even early-stage deployments require infrastructure designed to link disparate systems and apply consistent governance rules as the initiative expands. Governance itself must evolve from a gate-keeping function applied before deployment into a continuous operational discipline encompassing real-time evaluation, system visibility, rule enforcement and validation testing.

If you have the proper governance in place, you can actually move faster with your AI initiative. It should be something that enables adoption, not holds it back.

Zeus Kerravala

AI changes the contact center workforce

The integration of AI will fundamentally alter how contact centers allocate responsibilities between human agents and automated systems. Human staff will increasingly focus on exception handling, situations with emotional dimensions and decisions requiring human judgment, while AI systems absorb more routine, standardized work.

Supervisory roles will require new competencies for managing this hybrid workforce, including visibility into AI system performance, failure detection and the ability to route work appropriately between automated and human channels.

Laliberte and Kerravala advocated for a methodical implementation path: identify a specific customer journey, map the associated workflow and data dependencies, set baseline performance metrics and validate the approach with both standard scenarios and edge cases before expanding to additional areas.

https://www.youtube.com/embed/hkZTx-tHFpw?feature=oembed

AI ROI in CX won't be determined by the number of bots deployed. It's going to come from getting to better resolutions, being able to have more capable employees and more efficient operations, and responsible execution at scale.

Bob Laliberte