Contact centers emerge as proving ground for agentic AI, reshaping customer service metrics

Artificial intelligence is reshaping how customer service organizations define success, moving away from traditional metrics like call duration and deflection rates toward a focus on actually solving customer problems. This fundamental shift is prompting companies to reconsider AI's role—not as a tool for automating isolated tasks, but as a mechanism for coordinating interactions among customers, AI agents and human staff members.

At The AI ROI in Contact Center Summit, industry leaders explored strategies for combining human expertise with agentic AI capabilities to improve customer service delivery. During the event's opening remarks, theCUBE Research's Bob Laliberte outlined why contact centers represent an ideal testing ground for agentic AI effectiveness.

Contact centers have large volumes of interactions. There [are] significant labor costs and direct moments of truth with customers. 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.

Bob Laliberte, theCUBE Research

Laliberte shared these observations alongside Zeus Kerravala, principal analyst and founder of ZK Research, A Division of Kerravala Consulting. The theCUBE coverage also featured discussions with specialists from TalkDesk Inc., Cisco Systems Inc., Five9 Inc. and other organizations regarding how AI is reshaping the economics of customer experience.

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Insight #1: Customer service success goes beyond resolution time

Disagreement frequently emerges between customers and contact center staff about what constitutes a successful service interaction. Kerravala attributed this partly to the industry's historical emphasis on speed of resolution.

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?

Zeus Kerravala, ZK Research

Agentic AI has the potential to enable more substantive customer service interactions, though the emphasis must stay on relationship management, according to Vinod Muthukrishnan, vice president and general manager of Webex customer experience at Cisco. This philosophy underpins Cisco's AI Concierge, designed to maintain continuous conversational context regardless of which communication channel a customer uses to initiate contact.

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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. 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.

Vinod Muthukrishnan, Cisco

Insight #2: AI agents can help close the gap between conversation and resolution

Customer success teams should be evaluated based on "conversation to completion," or the degree to which they successfully address the customer's underlying issue. AI agents can contribute by maintaining context throughout transitions between AI and human representatives, according to Ram Rajagolpalan, head of product, AI for Zoom CX at Zoom Communications Inc.

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.

Ram Rajagolpalan, Zoom Communications Inc.

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Organizations looking to implement AI agents should prioritize areas where they can deliver the most immediate value. Joe Rittenhouse, co-chief executive officer of Converged Technology Professionals Inc., a Zoom implementation partner, outlined a practical approach.

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.

Joe Rittenhouse, Converged Technology Professionals Inc.

Insight #3: AI can handle volume while humans handle complexity

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An optimal contact center workflow may involve a combination of human and AI agents, with AI managing high-volume, routine interactions like password resets while humans concentrate on sensitive matters and high-value customer relationships. This division of labor could help reconcile the gap between how contact centers traditionally measure performance and what customers actually expect, according to Amit Mathradas, CEO and board member of Five9.

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.'

Amit Mathradas, Five9

The sector requires a more sophisticated operating model to support agentic orchestration. Such a model would address governance, integration and interconnected workflows to achieve enterprise-level readiness, explained Pedro Andrade, vice president of AI and generative AI business specialist at Talkdesk.

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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.

Pedro Andrade, Talkdesk