Healthcare organizations need the right foundation before deploying AI agents. In this episode, we share a practical readiness model to help teams assess where they are and what they need next. Learn how to move from strategy to execution using a four-layer platform model, six steps for operationalizing AI responsibly, and a framework for identifying and scaling the right use cases.

This episode ties together the core themes from our first eight episodes—covering governance, care transitions, revenue cycle, value-based care, and more—into one cohesive framework. We explore how healthcare organizations can move from isolated AI use cases to a scalable, governed Agentic AI strategy that drives real transformation across payers and providers.

What’s happening in the Agentic AI market—and what should healthcare organizations do next? This episode covers insights from a new report on the state of Agentic AI in healthcare. We explore key trends, use cases for payers and providers, and how to evaluate vendors. You’ll also hear about prioritization strategies, implementation roadmaps, and what to watch out for as adoption accelerates.

In this episode, we share how a national healthcare payer is using Agentic AI to improve Utilization Management and Care Management. Learn how Productive Edge helped identify high-impact use cases through an AI Action Planning Workshop and launched a pilot focused on automating service plan management. We discuss key lessons, quick wins, and a scalable path to broader AI adoption across UM and CM functions.

Revenue Cycle Management is full of manual work, disconnected systems, and costly delays. This episode explores how Agentic AI can change that. We dive into practical use cases—automated claims submission, denial management, and pre-auth simplification—and explain how specialized AI agents collaborate to streamline workflows and improve financial performance. Based on insights from a white paper by Raheel Retiwalla, we also outline a roadmap for adoption.

Value-based care demands smarter coordination, better outcomes, and lower costs. In this episode, we discuss how Agentic AI supports that shift by automating complex workflows, personalizing patient engagement, and enabling real-time collaboration across teams. Based on a guide by Raheel Retiwalla, we highlight practical use cases—from chronic disease management to contract oversight—and share a roadmap for integrating Agentic AI into today’s healthcare systems.

Care transitions are a major source of cost, risk, and inefficiency. In this episode, we explore how multi-agent systems powered by Agentic AI are improving coordination, reducing readmissions, and streamlining discharge and post-acute care workflows. Learn how AI agents work together across payer and provider systems to automate tasks, improve outcomes, and support financial sustainability—plus what to consider when implementing these solutions.

This episode explains what AI agents really are—and what they’re not. We cover how Agentic AI moves beyond traditional AI by enabling agents that can plan, decide, and act across healthcare workflows. Learn how multi-agent systems are already reducing administrative burdens and improving care in areas like claims, care coordination, and patient engagement. We also clarify the role of LLMs, and why governance remains essential for responsible adoption.

What does it take to successfully adopt AI agents in healthcare? In this episode, we walk through the Agentic AI Playbook for Healthcare Leaders, a practical guide to identifying high-impact use cases, aligning stakeholders, and scaling AI responsibly. If you're leading AI strategy in a payer or provider organization, this episode outlines the steps that actually work.

In this episode, we break down the key ideas from our whitepaper AI Governance, Compliance, and Risk Management: A Responsible AI Framework for Healthcare AI Agents. Learn why governance is critical to move from AI experimentation to real-world impact. We explore the principles, roles, and safeguards that healthcare organizations need to deploy AI agents responsibly—at scale and in compliance.