Contact Center AI Hits a Knowledge Wall as Old Metrics Lose Relevance

Customer service operations are becoming the proving ground for enterprise AI, with the sector's combination of high call volumes, substantial workforce expenses, and direct customer interactions making it an ideal test of real-world technology value. The shift away from experimental deployments toward production systems is forcing suppliers and enterprises alike to rethink how they measure success and structure their operations around AI capabilities.

The business case appears compelling on the surface. Gartner projects that conversational AI will reduce contact center labor costs by $80 billion this year, while major platform vendors including Cisco Systems Inc. are positioning AI agents as central to their collaboration and customer service strategies. A wave of cloud-based voice agents is entering the market, reflecting growing confidence in the technology's maturity.

Yet beneath these optimistic projections lies a more complex reality. Bob Laliberte, principal analyst for networking and observability at theCUBE Research, emphasized that contact centers deserve close examination precisely because their constraints are so visible. "Contact centers have large volumes of interactions. There [are] significant labor costs and direct moments of truth with customers," Laliberte said. "When something fails, the consequences are also pretty highly visible, and a poor AI interaction can increase customer effort. Damage trust and ultimately hurt the brand."

Laliberte and Zeus Kerravala, principal analyst and founder of ZK Research, a division of Kerravala Consulting, discussed these challenges during theCUBE's coverage of "The AI ROI in Contact Center Summit," exploring how AI is reshaping the economics of customer experience.

Measurement frameworks breaking down

The metrics that have guided contact center operations for decades are proving inadequate for evaluating AI performance. Traditional benchmarks emphasize call speed and first-contact resolution, but these measures can mask underlying problems when customers' issues remain unresolved despite shorter interactions.

Kerravala pointed out the fundamental mismatch: "Historically, we've measured success in the context of things like average handle time, first call resolution," he said. "And those metrics don't … matter as much anymore. We've had such a focus on average handle time in this industry, but is that shorter call actually a good thing if the issue remains largely unsolved?"

This recognition is pushing organizations toward outcome-based scoring frameworks that prioritize whether customer problems are actually resolved rather than how quickly agents process calls.

Knowledge management emerges as the critical constraint

Deploying autonomous agents rather than AI-assisted human representatives demands substantially more rigorous testing, governance structures, and data infrastructure. Companies such as Five9 Inc. have developed implementation playbooks and voice AI agents designed to accelerate deployment timelines, but the foundational work remains the responsibility of each organization.

Laliberte identified the core requirements for success: "Organizations should be listening for some practical answers on the importance of high data quality, the integrations that need to be done … being open to redesigning their process," Laliberte said. "There [are] some issues around knowledge management that need to be addressed as well."

The ability to manage organizational knowledge, redesign workflows, and smoothly transition interactions between AI systems and human agents ultimately determines whether AI investments generate returns.

https://www.youtube.com/embed/95HmL7YVi4I?feature=oembed

Kerravala offered a final perspective on competitive advantage: "It's important to understand that the brands that lead will not be the ones that simply automate the most," Kerravala said. "They will be those that turn AI into a better, more consistent set of outcomes, but will also be able to earn employee and customer trust."