Many organizations are eager to adopt AI, but few have the operational data architecture required to support it in production systems.
In real environments, critical workflow data is scattered across transactional systems, operational tools, and analytics platforms that rarely share a common model. Without a coherent operational architecture, AI tools struggle to reason about workflow state or produce meaningful insights.
This session explores how operational lifecycle modeling can create the foundation for AI-enabled systems.

Using a healthcare revenue cycle environment as a case study, we’ll examine how operational events can be captured in Microsoft Dataverse and unified with analytics in Microsoft Fabric to create a canonical operational model. This architecture allows AI copilots and automation tools to reason about the state of real-world workflows rather than disconnected data points.

Attendees will learn:
Why many AI initiatives fail when they start with models instead of architecture
How lifecycle-based data modeling improves observability of operational systems
How platforms like Dataverse and Microsoft Fabric can support AI-ready architectures

Rather than focusing on AI theory or tools alone, this session focuses on the operational systems design required to make AI useful in complex real-world environments.