Regal Brings Generative AI Agents to Customer Calls

Regal is deploying generative AI voice agents that handle full conversations, pull live customer data, and route only complex calls to human agents, collapsing the tradeoff between expensive human support and frustrating self-service. CEO Alex Levin says the key to ROI is putting voice AI on meaningful call types like billing and retention, where resolution rates and revenue impact can be measured immediately.

Synopsis: The contact center has spent the better part of two decades trying to make voice automation feel less like a maze. IVRs and IVAs improved containment numbers but stopped well short of actually resolving anything, which left enterprises stuck choosing between expensive human agents and frustrating self-service. Generative AI voice agents are starting to collapse that tradeoff — handling real conversations, pulling live customer data and routing only the calls that need a person, rather than punishing every caller with the same scripted menu. Alex Levin, CEO and co-founder of Regal, sat down with Mike Vizard to dig into how that shift actually plays out in contact center operations. Levin draws a sharp line between legacy IVR/IVA automation and modern generative agents — the difference is not just better natural language, it is the agent’s ability to hold a full conversation, reach into systems of record, resolve an issue end to end and hand off cleanly when escalation makes sense. That changes the unit economics of high-volume phone support in a way the previous wave of automation never managed to. Levin and Vizard work through what enterprises have to get right to make voice AI work at production scale. Latency is non-negotiable — even small lags break the illusion of a real conversation and tank containment. Human agent roles shift toward the harder calls, which raises the bar on training and tooling. Synthetic testing becomes essential because you cannot wait for live calls to find the edge cases. And the cost model has to be transparent enough that finance can map AI spend to actual call outcomes rather than vague productivity claims. The framing Levin pushes is that enterprises should not start with low-impact pilots. The biggest gains, and the cleanest ROI stories, come from putting voice AI on meaningful call types — billing questions, retention saves, scheduling, support escalation — where resolution rate, revenue retention and customer satisfaction can all be measured in the same week. That posture turns voice AI from a curiosity into a revenue-driven layer of the contact center, and it is where the next round of enterprise adoption looks like it is heading.