Brian Peterson, Cofounder and CTO of Dialpad, joined Doug Green, Publisher of Technology Reseller News, for a podcast conversation ahead of the upcoming Enterprise Connect 2026 and HIMSS 2026 events to discuss how enterprises are moving beyond AI experimentation and beginning to deploy agentic AI at scale.

Peterson explained that many organizations have spent the past year exploring AI pilots but are now facing the harder challenge of operationalizing those experiments in production environments. “The real question now isn’t whether AI works—it’s how you deploy it safely, measure its impact, and scale it across the organization,” he said. Dialpad’s approach focuses on helping enterprises identify high-value use cases, build AI agents around those workflows, and validate outcomes before deployment.

A key advantage for Dialpad is its access to large volumes of real conversational data from voice, messaging, and contact center interactions. By analyzing these interactions, the platform can identify friction points in customer experience and operational workflows. Those insights then guide the creation of AI agents designed to resolve specific tasks—such as assisting contact center agents, automating routine processes, or surfacing real-time insights during live conversations.

Peterson also emphasized the importance of governance and measurement when deploying AI agents. Organizations must ensure that AI systems are transparent, measurable, and aligned with business objectives. By validating performance before production rollout, enterprises can reduce risk while demonstrating clear ROI—an important step in transitioning from experimental pilots to enterprise-wide adoption.

As the industry prepares for major discussions at Enterprise Connect and HIMSS, Peterson believes the next phase of enterprise AI will be defined by practical deployment rather than experimentation. Organizations that can effectively turn conversational data into actionable AI workflows will be best positioned to unlock the real value of agentic AI.

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