AI Use Cases, Deployment, and Measuring Real-World ROI - with Ylan Kazi of Blue Cross Blue Shield of North Dakota

2026-03-10 · Source: The AI in Business Podcast · Field: Health & Wellbeing — Healthcare Systems & Policy, Medical Devices & Health Technology · Depth: Intermediate, long

Summary

Ylan Kazi, Chief Data and AI Officer at Blue Cross Blue Shield of North Dakota, discusses responsible AI adoption in regulated healthcare. Kazi emphasizes the importance of aligning executive leadership on an organizational AI posture (first mover, fast follower, or cautious) to guide subsequent decisions on governance, team structure, and build-versus-buy strategies. The conversation highlights the need for internal AI capabilities to ensure sustainability and cost-effectiveness, while acknowledging that complex models like foundational LLMs are best acquired externally. Key considerations include embedding explainability and auditability into AI workflows, especially for direct patient care, and prioritizing use cases that deliver measurable customer value, reduced friction, and improved experiences, alongside traditional ROI metrics. The discussion also touches on navigating evolving AI regulations and the shift towards an "AI-first" mindset in organizational design.

Key takeaway

For healthcare executives guiding AI strategy, aligning your leadership on a clear organizational AI posture is critical before making build-versus-buy decisions. You should prioritize developing internal capabilities for sustainable AI adoption and ensure all deployments focus on measurable customer value and operational ease, integrating explainability and auditability from the outset to manage regulatory risks and build trust.

Key insights

Successful AI adoption in regulated sectors requires clear organizational posture, robust governance, and a focus on measurable customer value.

Principles

• Align executive leadership on AI posture early.
• Build internal AI capabilities for sustainability.
• Prioritize use cases with clear customer value.

Method

Organizations should define their AI adoption posture (first mover, fast follower, or cautious), then build internal capabilities for core functions while leveraging external vendors for cutting-edge or resource-intensive AI solutions.

In practice

• Embed explainability and auditability into AI systems.
• Focus AI on reducing patient friction and improving experience.
• Consider AI agents in future leadership management.

Topics

• AI Adoption Strategy
• Healthcare AI
• AI Governance
• Explainable AI
• AI Regulation

Best for: AI Product Manager, Director of AI/ML, Executive, IT Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by The AI in Business Podcast.