Morgan Stanley is one of the world’s leading financial services firms, providing a broad range of investment banking, securities, wealth management, and investment management services. Enter AI, large language models, and agentic tooling have fundamentally transformed how we tackle complex engineering challenges. We have leveraged these tools across the entire lifecycle—from rearchitecting legacy systems and rapidly prototyping proofs of concept to executing large-scale migrations with confidence. The resulting productivity gains have been substantial, exceeding our expectations. Most importantly, we began this scaling journey ahead of peak client demand, positioning ourselves ahead of the curve rather than scrambling to catch up. This proactive approach directly enabled us to secure a significant business opportunity that might otherwise have gone elsewhere. In this talk, I will share the practical lessons, the wins, and a few of the stumbles from our modernization journey.

Gergely Nagy joined Morgan Stanley as an intern 15 years ago. He is now the Head of Engineering for Client & Investor Analytics, where his primary focus is helping the team shape the future of their software platform. He has worked with a wide range of technologies, including .NET with C#, C++, and Scala, and currently focuses primarily on Java- and Python-based projects. He completed his PhD at Eötvös Loránd University in 2021.