Organizations across industries are investing heavily in AI, yet many struggle to move beyond experimentation. The core challenge is rarely the AI model itself—it is the lack of operational data architecture that allows AI systems to understand real-world workflows.

In most enterprises, operational data is fragmented across transactional systems, analytics platforms, and workflow tools that were never designed to share a unified model of how work actually progresses. Without structured lifecycle data, AI systems cannot reason about operational state, limiting their ability to provide meaningful insights or automation.

This session explores how lifecycle-based data modeling can create the foundation for AI-ready systems. Using a healthcare revenue cycle environment as a case study, we will examine how operational events can be captured using platforms such as Microsoft Dataverse and unified with analytics environments like Microsoft Fabric to create a canonical operational model.

Attendees will learn why many AI initiatives fail when they start with models instead of architecture, how lifecycle modeling improves operational observability, and how organizations can design data platforms that allow AI systems to reason about real-world processes.