From Claims to Copilots: Architecting AI-Ready Operational Systems

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.

Stop Automating Broken Work: Designing AI-Ready Systems That Actually Scale

As organizations accelerate adoption of AI and automation, many initiatives fail to deliver meaningful results, not because of limitations in models or tools, but because the underlying workflows lack structure, consistency, and clear decision boundaries.

In complex operational environments, work spans multiple systems, inconsistent data sources, and human-driven processes. When automation is introduced into these conditions, it doesn’t resolve inefficiencies, it amplifies them.

A key challenge is the gap between how systems are built and how work actually happens. Engineering and data teams often design solutions based on observable data and system behavior, without a shared model of the underlying workflow. The result is technically sound systems that are operationally incomplete.

This session explores how complex, multi-system workflows break under automation, and how to redesign them into deterministic, AI-ready systems. Through real-world patterns, we’ll examine how inconsistent data, undefined states, and hidden decision logic create fragile automation, and how to rebuild them into scalable, reliable architectures.

The session will cover practical approaches to normalizing data across systems, defining actionable workflow states, implementing data contracts, combining rule-based and AI-assisted decision layers, and designing effective human-in-the-loop feedback mechanisms.

Rather than focusing on tools alone, this talk provides a systems-level approach to moving from automation experiments to reliable, production-ready workflows, applicable across industries where data, decisions, and execution intersect.

Session Objectives / Takeaways

By the end of this session, attendees will be able to:

Identify whether a workflow is truly ready for automation or AI integration
Design deterministic workflow states that enable reliable system behavior
Implement data contracts to stabilize inputs across multiple systems
Distinguish between rule-based logic and AI-assisted decision-making

From Claims to Copilots: Architecting AI-Ready Healthcare Systems with Microsoft Fabric

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.

AI & Automation – Redefining Revenue Cycle Management for the Digital Era

Healthcare revenue cycle management faces rising denials, staffing shortages, and increasing complexity. This session explores how organizations are practically using AI and automation today within existing RCM environments, without full system replacement. Attendees will learn where automation delivers real value, why process readiness matters for getting results, and gain a framework for evaluating AI and automation opportunities based on real operational needs and outcomes.

AI and the Impact on Coding

Artificial intelligence (AI) is transforming healthcare operations, and coding is no exception. In this session, we’ll explore how AI is reshaping the coding landscape, enhancing accuracy, streamlining workflows and strengthening compliance, while also raising new questions about risk, documentation quality and the evolving role of coders. From a revenue cycle and analytics perspective, we’ll examine how AI is being applied in ASCs today, including computer-assisted coding, audit automation and enhanced documentation support. Most importantly, we’ll highlight why coders remain essential to success, not replaced by AI but elevated as strategic experts who ensure accuracy and protect revenue. Participants will leave with insights into how AI can be thoughtfully applied to coding today, and what it means for the future

2026 ASCA Coding & Reimbursement for ASCs 1-13-2026