Contact centers emerge as proving ground for agentic AI, but customer expectations lag behind deployment

The role of artificial intelligence in customer service is undergoing a fundamental shift. Rather than optimizing for call duration and call deflection rates, organizations are increasingly prioritizing genuine issue resolution. This transformation is reshaping how companies deploy AI—moving beyond isolated task automation toward orchestrating complex interactions that involve customers, AI agents and human representatives working in concert.

The AI ROI in Contact Center Summit brought together industry analysts and technology vendors to explore how human and agentic AI capabilities can be combined to enhance customer service delivery. During the event's opening remarks, theCUBE Research's Bob Laliberte outlined why contact centers represent an ideal testing environment for agentic AI systems. He noted that "Contact centers have large volumes of interactions. There [are] significant labor costs and direct moments of truth with customers," and emphasized that "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 shared the stage with Zeus Kerravala, principal analyst and founder of ZK Research, A Division of Kerravala Consulting, alongside representatives from companies including TalkDesk Inc., Cisco Systems Inc. and Five9 Inc.

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Resolution matters more than speed

A disconnect often exists between what call center operators and customers consider successful service interactions. The industry has traditionally emphasized speed of resolution as a key performance indicator, but this approach may be misaligned with actual customer needs. Kerravala observed that "Historically, we've measured success in the context of things like average handle time, first call resolution. 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?"

Agentic AI systems have the potential to enable more substantive customer interactions, but success requires a focus on relationship management rather than transactional efficiency. Vinod Muthukrishnan, vice president and general manager of Webex customer experience at Cisco, described this distinction through the lens of Cisco's AI Concierge, designed to maintain conversational continuity across different customer entry points. "An AI agent that can fill a slot or fulfill a transaction is not agentic. Agentic is much more of a framework of tools and products and processes," he explained. "The big thing around agentic is going away from just voice agents that can answer a question, fulfill a transaction, to orchestrating a journey, to now evolving all the way to be able to orchestrate a relationship."

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Context preservation bridges AI and human handoffs

Customer success teams should be evaluated based on their ability to move from conversation to actual completion of the customer's request. AI agents can strengthen this capability by retaining contextual information when transferring interactions between automated systems and human representatives. Ram Rajagolpalan, head of product, AI for Zoom CX at Zoom Communications Inc., emphasized the importance of this continuity: "You want to transfer the full context, the reason why they're calling, the collected variables that you need to pass on to a human being so that human being can get on with the job and get it done without having to repeat themselves or look at 10 different places of record just to answer a simple question."

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Organizations should begin deploying AI agents where they can deliver the most immediate value. Joe Rittenhouse, co-chief executive officer of Converged Technology Professionals Inc., a Zoom implementation partner, suggested identifying these opportunities systematically: "Usually where we try to start is, where's the low-hanging fruit? Just a simple question of what do you do after hours? We're usually staffed, we follow the sun from the East Coast to the West Coast, but then after hours we just have a general mailbox. The answer is typically, I don't know."

Volume and complexity require different handling approaches

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An effective contact center operating model may involve dividing responsibilities between AI and human agents based on interaction characteristics. AI systems can efficiently manage high-volume, low-risk interactions such as password resets, while human agents focus on sensitive matters and high-value customer relationships. This division of labor could help align how contact centers measure performance with what customers actually expect. Amit Mathradas, CEO and board member of Five9, highlighted a striking disconnect in perception: "From the research we have done, we found one stat really amazing. Ninety-nine percent of business practitioners who are deploying AI think their contact centers and their serviceability has gotten better. Only 66% of actual users think the contact center has gotten better. More than 50% of them are saying the reason is, 'I want to access a human and I don't get that.'"

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The industry requires a more sophisticated operating framework to enable agentic orchestration effectively. Such a framework must address governance, system integration and connected workflows to ensure organizations are prepared for enterprise-scale deployment. Pedro Andrade, vice president of AI and generative AI business specialist at Talkdesk, described this approach as Customer Experience Automation: "Customer Experience Automation, CXA, is what we defined as an operating model. It's not a category of technology. It's the system that coordinates a hybrid workforce of AI and human employees. Then connecting the systems, connecting knowledge and also connecting the workflows."

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