Leading AI Transformation in Health Insurance: A Senior Director’s Playbook

Duration

2 Days

Skills Gained

By the end of this course, participants will be able to:

• Develop an intuitive understanding of AI systems and how they create business value

• Differentiate high-value AI use cases from low-value applications in a portfolio

• Propose KPIs that avoid pilot purgatory for AI initiatives

• Assemble an AI adoption roadmap for the participant’s own organization

• Assess organizational readiness across tech, talent, and culture

• Apply Responsible AI principles to evaluate ethical, regulatory, and HIPAA-driven risks

Who Can Benefit

This course is designed for:

• Senior Directors & VPs at Health Insurance Organizations

• Health Plan Compliance, Clinical, and Claims Operations Leaders

Prerequisites

Participants should enter this course with:

• Familiarity using Generative AI Chatbots (e.g., Copilot, ChatGPT, Gemini, Claude)

Software

• All attendees must have a modern web browser and an Internet connection.

Organizational Objectives

This course assists organizations to:

• Reduce pilot-purgatory risk through KPI selection grounded in measurable outcomes such as claims cycle time and medical loss ratio

• Build senior-director-level fluency in AI strategy and risk across the health plan in two days

• Establish a shared vocabulary and prioritization framework across claims, compliance, clinical, and IT leadership

• Lower governance risk through trained recognition of NAIC- and HIPAA-driven AI compliance issues

Module 1 - A Leader’s Guide to AI

By the end of this module, you will be able to differentiate generative AI from traditional AI and from agentic AI, describe how foundation models work at the level a leader needs, and use the vocabulary (prompt engineering, RAG, agents) accurately in business conversations.

• Defining AI, generative AI, and agentic AI

• Differentiating generative AI from traditional AI

• Why AI matters: efficiency, innovation, growth

• How generative AI and agentic AI work, with foundation models

• Demo: inner workings of an LLM

• Key terminology: prompt engineering, RAG, agents, and more

• Hands-on Lab: Apply prompt engineering flash-learning to one realistic health insurance business prompt (e.g., summarizing a coverage policy) and compare results across two foundation models.

Module 2 - Strategic Opportunities & Business Impact

By the end of this module, you will be able to identify high-value AI use cases, define KPIs that avoid pilot purgatory, decide between human, automation, and AI for a given workflow, and recognize the cultural conditions that let AI initiatives succeed.

• Identifying high-value use cases across claims, prior authorization, fraud/SIU, member engagement, and provider network management

• Demo: building an agent live

• Avoiding pilot purgatory and quantifying ROI through health insurance KPIs (claims cycle time, medical loss ratio, prior-auth turnaround)

• Selecting among humans, automation, and AI

• Demo: end-to-end agentic coding teams

• Fostering an AI-driven culture

• Hands-on Lab: Apply KPI-selection best practices to one candidate health insurance use case (e.g., prior authorization automation) and defend the selection against pilot-purgatory failure modes.

Module 3 - The AI Risk & Responsibility Landscape

By the end of this module, you will be able to characterize the ethical and security risks unique to AI systems in claims, underwriting, and utilization management, distinguish them from traditional software risks, apply responsible AI principles, and reason about the NAIC Model Bulletin and state-specific regulatory environment that governs your health plan’s deployment.

• Ethical and security risks of AI

• Demo: external prompt injection attack

• AI vs. traditional software risks

• Responsible AI principles and frameworks

• The NAIC Model Bulletin, state AI laws, and the health insurance regulatory environment

• Is your AI vendor a HIPAA Business Associate — and what does your BAA actually cover?

• State clinician-review requirements for AI-influenced coverage denials, and rising algorithmic claims-denial litigation exposure

• Hands-on Lab: Author one Responsible AI principle for your health plan grounded in a risk identified through workshop exercises (e.g., clinician review of AI-influenced coverage denials).

Module 4 - Case Studies of Generative AI in Action

By the end of this module, you will be able to read AI investment cases through a “better, faster, cheaper, safer, possible” lens, distinguish table-stakes implementations from strategic bets, and draw lessons from early adopters across health insurance and adjacent industries.

• AI value framework: better, faster, cheaper, safer, possible

• From table stakes to strategic bets

• Health insurance case studies: claims automation, prior authorization, and fraud detection

• Member engagement and care navigation case studies

• Lessons from early adopters

• Demo: agentic analytics for business insights

• Hands-on Lab: Run an AI investment game over a portfolio of candidate health insurance use cases and defend allocation through the value framework.

Module 5 - Enterprise AI Readiness

By the end of this module, you will be able to author an AI adoption roadmap, assess organizational readiness across tech, talent, and culture, structure a Center of Excellence and grassroots adoption, and design a talent strategy for an AI-driven workforce.

• An AI adoption roadmap

• Assessing organizational readiness across tech, talent, and culture

• Building an AI Center of Excellence spanning actuarial, clinical, claims, compliance, and IT

• The importance of grassroots adoption

• Activity: knighting your AI champions

• Talent strategies across actuarial, clinical, claims, and compliance teams

• Hands-on Lab: Plan to lead by example — author a personal AI adoption roadmap and identify champions inside your team.

Module 6 - Recap and Next Steps

By the end of this module, you will be able to articulate the course’s key takeaways, identify your next-step actions, and locate resources for continued AI leadership development.

• Key takeaways and action items

• Resources for continued learning

• Question & response session

• Hands-on Lab: Capture three personal next-step commitments grounded in the course content and a follow-up resource for each.