Artificial intelligence is moving out of the pilot phase in customer service, and knowledge management is emerging as the constraint that decides whether the investment pays off. Contact centers combine high interaction volumes, heavy labor costs and visible moments of truth with customers, which makes them the clearest test of where the technology delivers measurable returns.
That pressure is reshaping how suppliers position their platforms, from Cisco Systems Inc.’s bet on AI agents across collaboration and customer workflows to a wave of voice agent launches in the cloud contact center market — Gartner projects conversational AI will cut contact center labor costs by $80 billion this year. These environments are demanding and highly measurable, which makes them worth studying, according to Bob Laliberte (pictured, left), principal analyst for networking and observability at theCUBE Research.
“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.”
Knowledge management and new metrics for AI value
The measurement frameworks the industry has relied on for decades are the first thing to break. Legacy targets reward speed and containment, even when a customer’s problem goes unsolved, and that gap is pushing brands toward outcome-based scoring, Kerravala noted.
“Historically, we’ve measured success in the context of things like average handle time, first call resolution,” Kerravala said. “And those metrics don’t, I don’t think, 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, right?”
Moving from assistants to autonomous agents raises the bar on testing, governance and data foundations. Companies including Five9 Inc. have leaned on implementation playbooks and voice AI agents to shorten time to value, but the groundwork still sits with the customer.
“Organizations should be listening for some practical answers on the importance of high data quality, the integrations that need to be done,” Laliberte said. “Being open to redesigning their process, right? There [are], I think, some issues around knowledge management that need to be addressed as well.”
Success ultimately rests on knowledge management, workflow redesign and the handoff between virtual and human agents.
“It’s important to understand that the brands that lead will not be the ones that simply automate the most, right?” 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.”
