Modernizing Enterprise Codebases for AI: Lessons from Large-Scale Java Library Migrations

About the Session

Before enterprises can unlock the power of AI, they must first tackle the foundation—modernizing legacy codebases riddled with outdated libraries and brittle dependencies. This lightning talk highlights lessons learned from leading Java migration efforts in SAP-integrated enterprise applications, including complex upgrades across major libraries such as Spring, Jersey, OpenSAML, and Tomcat. We’ll explore how technical debt, unsupported dependencies, and version incompatibilities silently block AI adoption and slow innovation. I’ll share practical strategies for navigating large-scale codebase modernization, ensuring backward compatibility, and reducing risk—all essential for building stable, AI-ready systems. This talk will resonate with engineering leaders and developers facing modernization blockers as they prepare their systems for the next wave of AI. Key Takeaways: Why modernizing libraries is critical before scaling AI Strategies to safely upgrade legacy dependencies without regressions How to balance performance, security, and stability in AI-prep initiatives

Speaker

Sravan Reddy Kathi

SAP-Developer