Many organizations are excited about AI, but few have the operational architecture required to support it in real production environments.

Healthcare revenue cycle operations illustrate this challenge clearly. Critical operational data lives across electronic medical records, clearinghouses, payer systems, and internal work queues that rarely share a common structure. Without a coherent data architecture, AI systems struggle to produce meaningful results.

This session explores how a healthcare revenue cycle organization designed an AI-ready operational architecture by modeling the lifecycle of work rather than the departments performing it.
The approach was implemented in a real ambulatory surgery center revenue cycle environment where lean operational teams require precise workflow visibility and automation.

Using Microsoft Dataverse to capture operational lifecycle events and Microsoft Fabric to unify analytics and operational data, we created a canonical model that allows AI copilots to reason about workflow state and surface real-time insights.

Attendees will learn:

• why many AI initiatives fail when they start with models instead of architecture
• how to model operational workflows as structured lifecycle data
• how Microsoft Fabric and Dataverse can support AI-ready operational platforms

Rather than focusing on theoretical AI capabilities, this talk demonstrates how operational architecture, data platforms, and AI systems must work together to solve real-world problems.