Enterprise IT separation projects driven by mergers, acquisitions, or restructuring represent some of the highest-risk transformation initiatives in large organizations. When deeply integrated systems must be disentangled, traditional testing strategies often fail to account for hidden dependencies, integration debt, and data integrity risks. This session introduces a modern, Azure-powered framework for executing resilient separation programs with AI-enhanced validation and cloud-native testing strategies.

Participants will explore how Azure DevOps, Azure Test Plans, and AI-assisted regression testing models can be combined to automate complex validation cycles across ERP, HR, and financial systems. The session introduces a structured approach to analyzing architectural entropy measuring dependency disorder across business, data, and application layers during system disentanglement.

The proposed framework is built on four pillars: data integrity validation using Azure Data Factory and automated reconciliation pipelines; integration testing across APIs and event streams; performance and stress testing in elastic Azure environments; and business process continuity validation using AI-driven scenario simulation.

The talk also demonstrates how machine learning models can proactively identify high-risk integration points, predict failure probability during cutover windows, and optimize test case prioritization based on impact analysis.

Attendees will gain practical insights into building scalable test environments in Azure, embedding MLOps into transformation programs, and maintaining operational stability during high-risk transitions. Emphasis will be placed on governance, compliance, and automated monitoring to ensure minimal business disruption.

This session is designed for enterprise architects, DevOps leaders, cloud engineers, and transformation program managers seeking to combine Azure cloud capabilities with AI-driven intelligence to de-risk complex system separations.