What We Learned Deploying AI within Bloomberg’s Engineering Organization – Lei Zhang, Bloomberg

When it comes to using AI for software engineering, much of the spotlight falls on how large language models (LLMs) can write code—sometimes entirely from scratch. Countless studies highlight productivity gains from turning requirements directly into runnable code. But the reality of applying AI at scale inside a mature engineering organization is far more complex and nuanced. Over the past year, we’ve been on that journey at Bloomberg—integrating AI into the workflows of 9,000+ software engineers—and we’ve learned a few important lessons worth sharing: Where the real ROI lies once you move beyond toy examples What it takes to actually enable AI across a large, established engineering org The best practices, cultural shifts, and guardrails that are required to make it work in practice If you’re wondering what happens after the first demo magic fades and the real work begins, this talk is for you. Speaker: Lei Zhang | Head of Technology Infrastructure Engineering, Bloomberg Timestamps 00:00 Introduction to Bloomberg's Scale & Infrastructure 03:32 AI for Coding: Initial Adoption & The "Greenfield" Drop-off 06:14 Uplift Agents: Automating Refactoring & Maintenance 08:40 Incident Response Agents: Unbiased Troubleshooting & Speed 09:37 The "Paved Path": Standardizing AI Tool Building (MCP) 11:51 Platform Components: Gateway, Discovery Hub, and Deployment 13:34 Leveraging Training & Communities for Adoption 16:15 The Leadership Gap & The Changing Cost Function of Engineering