AI Explained

In this episode of AI Explained, we are joined by Stacey Johnston, MD, Chief Information and Digital Execution Officer at Beacon Health System.

Dr. Johnston brings a rare dual perspective to healthcare AI. She trained as a physician and hospitalist before moving into health informatics, giving her firsthand insight into where technology helps clinicians and where it gets in the way. She discusses how Beacon built its AI governance council from scratch, the policies and vendor risk assessments that gate every new tool, and why requiring a defined ROI before approval has become a forcing function for disciplined adoption. She also shares how agentic AI is already delivering results in scheduling, autonomous benefits verification, and colon cancer screening, why ambient listening drove $10,000 in additional per-physician revenue over 12 months, how clinician trust is earned incrementally through seamless workflow fit and real time savings, and what a federated but centrally monitored AI model could look like as health systems scale.

In this episode of AI Explained, we are joined by Jeff Dalton, Head of AI and Chief Scientist at Valence.

Jeff has spent two decades at the intersection of research and industry, from building early conversational search benchmarks at Carnegie Mellon and Microsoft to leading the AI behind Nadia, Valence's purpose-built enterprise coaching assistant. He discusses the fundamentals of agentic system design that still hold from classical AI theory, why evaluation has to come before the prompt, how he approaches memory as a first-class object in coaching systems, and the defense-in-depth approach to guardrails that keeps complex agents safe across diverse enterprise deployments.

In this episode of AI Explained, we are joined by David Kenny, Executive Chairman of the Board of Nielsen and veteran AI leader. 

He discusses how to prevent AI agents from going rogue, sharing insights from Nielsen's "Ask Nielsen" platform and emphasizing the importance of compound AI systems, real-time control planes, "generally accepted trust principles" (GATP) for AI, fit-for-purpose models, production cost management, and change management in moving from pilot to production.

In this episode of AI Explained, we are joined by Gary Stafford, Principal Solutions Architect at AWS Strands Agents.

He delves into how enterprises choose between AI/ML and agentic approaches, patterns for multi-agent systems, and the role of MCP. Gary also shares real-world use cases and practical guidance on safety, scaling, and delivering enterprise-ready agent systems.

In this episode of AI Explained, we are joined by Jayeeta Putatunda, Director of AI Center of Excellence at Fitch Group. 

She discusses essential lessons learned from building and deploying AI agent systems, including challenges in moving from concept to production, key evaluation metrics, and the importance of observability and guardrails in ensuring reliable AI systems.