The next phase of contact center artificial intelligence will be measured less by how many interactions machines handle and more by whether customers actually get their problems resolved.
That was one of the central conclusions from Bob Laliberte (pictured, left), principal analyst for networking and observability at theCUBE Research, and Zeus Kerravala (right), principal analyst and founder of ZK Research. During “The AI ROI in Contact Center Summit,” an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio, the analysts concluded the event reflecting on insights from Cisco Systems Inc., Talkdesk Inc., Zoom Communications Inc. and Five9 Inc. Despite differences in platforms and deployment strategies, the conversations pointed toward a common destination: end-to-end resolution backed by connected data, governance and measurable business outcomes. (* Disclosure below.)
“Resolution and resolution quality is the new unit of value,” Kerravala said. “Agentic systems should be judged on whether the customer’s needs were completed — and completed actually across the full journey.”
Rather than relying primarily on traditional measures such as containment and deflection, organizations will need broader scorecards that account for customer satisfaction, effort, employee productivity, cost and growth, according to the analysts.
Connected data and governance underpin AI results
That shift places greater importance on context and integrated data. Fragmented systems and stale knowledge can undermine AI accuracy, create repetitive interactions and accelerate flawed processes rather than fix them.
“If you’ve got a broken process, you’re going to get to that bad destination faster,” Kerravala said.
The analysts recommended that organizations begin with a clearly bounded, high-value problem rather than attempting to redesign the entire customer journey at once. Companies can establish baseline metrics, deploy AI against that specific workflow and measure the resulting improvement before expanding.
Those initial deployments still need architectures capable of connecting systems and reusing governance controls as deployments grow. Governance itself must also move beyond a preproduction checkpoint and become an ongoing operating practice involving continuous evaluation, observability, policy enforcement and testing.
“If you have the proper governance in place, you can actually move faster with your AI initiative,” Kerravala said. “It should be something that enables adoption, not holds it back.
AI changes the contact center workforce
AI adoption will also reshape how contact centers divide work between people and digital agents. Human employees are expected to spend more time handling exceptions, emotionally sensitive situations and interactions requiring judgment, while AI takes on more standardized processes.
Supervisors will need to manage that blended workforce differently as well, including understanding when AI is working, when it is failing and how work should move between automated systems and people.
Laliberte and Kerravala ultimately recommended a controlled approach: Select one customer journey, document the workflow and data requirements, establish baseline metrics, and test both routine interactions and edge cases before expanding.
“AI ROI in CX won’t be determined by the number of bots deployed,” Laliberte said. “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.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of “The AI ROI in Contact Center Summit”:
(* Disclosure: TheCUBE is a paid media partner for “The AI ROI in Contact Center Summit.” Sponsors of theCUBE’s event coverage do not have editorial control over content on theCUBE or SiliconANGLE.)
