How Agentic AI Turns Calls Into Continuous Learning

Описание: Customers don’t call to chat with a bot—they want outcomes. In my chat with Jim Palmer Dialpad's Chief AI Officer we dive into how Dialpad’s AI-first platform turns real-time voice, video, and digital interactions into agentic workflows that act on behalf of users, learn from every result, and hand off seamlessly to humans when it counts. No black boxes. No bolt-ons. Just a unified stack where speech recognition accuracy, in-domain adaptation, and transparent evaluations compound into trust you can measure.

We talk through the nuts and bolts of building AI where it belongs: inside the media layer, not tacked on after the fact. That foundation unlocks higher-quality transcripts, reliable summaries, and precise suggestions that downstream models can use without drowning in errors. From there, in-domain training becomes the engine for better answers: when support questions share patterns across industries, the system adapts faster and resolves issues with fewer escalations. And when a case does escalate, the agent sees the distilled context—intent, key points, attempted steps, and recommended next actions—so the conversation keeps moving.

The flywheel is the real breakthrough. As new issues surface in a rolling window of conversations, the platform quantifies impact and proposes automations. Humans review, approve, and refine. The system executes, measures completion and containment, and folds learnings back into models. Layer on governance—clear data policies, observability, and rigorous evaluation—and you get AI that leaders can trust and teams love to use. We also share how internal hackathons and applied research push practical features over the line, keeping innovation and execution in lockstep.

If you care about customer experience, agent performance, and measurable ROI from AI, this conversation brings a clear blueprint. Subscribe, share with a colleague who owns CX or support, and leave a review with your biggest automation challenge—we might feature it next.