AI Legacy Modernization: Why AI Alone Won’t Fix Your Legacy Code
Peer insights how enterprise data leaders are using AI to modernize legacy systems, and what it takes to make it work.
Modernizing legacy systems has quickly become a top priority for data and AI leaders. But while AI promises faster migration, most organizations are finding the reality more complex.
Decades-old code isn’t clean or complete. It contains hidden dependencies, undocumented logic, and critical business rules that AI alone can’t interpret.
The result: successful modernization depends less on the model, and more on how the work is structured.
In this field report, we examine how one organization used a multi-agent AI approach to migrate SAS workflows into a modern data platform — unlocking significant efficiency gains while surfacing new governance and design challenges.
Inside, you’ll discover:
- Why legacy code must be broken down and reconstructed before AI can be applied
- How a multi-agent workflow improves visibility, accuracy, and efficiency
- Where teams are seeing meaningful productivity gains, and what’s driving them
- How data lineage and documentation expose hidden risks in legacy systems
- Why governance is emerging as the biggest barrier to scaling AI-driven modernization
Download the field report to learn how your peers are approaching AI-driven legacy transformation.
