As AI code assistants become standard across enterprise development, the real differentiator is no longer adoption. It is the ability to measure, optimize, and operationalize productivity gains at scale.
Engineering leaders are now being asked tougher questions. How do we prove time savings? How do we benchmark improvement across thousands of developers? How do we connect developer experience metrics to outcomes like faster delivery, higher quality, and cost efficiency?
Join LTM, GitHub, and Opsera for a session on how enterprises are moving beyond usage metrics to build a structured framework for AI-driven engineering success grounded in data, benchmarks, and actionable insights.
What We’ll Cover:
- From Adoption to Measurable Outcomes
Learn how leading organizations move beyond license counts to quantify real impact including cycle time improvements, PR velocity gains, and engineering hours saved. - GitHub’s Engineering System Success Metrics That Scale
Explore GitHub’s ESS framework and how enterprises connect inner-loop developer productivity with outer-loop delivery performance. - LTM’s Enterprise-Scale Productivity Journey
Hear how LTM scaled from 2,000 to 22,000+ Copilot users while establishing baselines and benchmarks that demonstrate clear ROI. - Turning Insights Into Action with Opsera
See how Opsera Unified Insights enables engineering leaders to make day-to-day decisions using reasoning agents, automation, and auto-remediation – not just dashboards.
Who Is This For
- CTOs and VPs of Engineering
- Directors of DevOps and Platform Engineering
- Engineering Managers focused on operational excellence
- Technical Leaders looking to align delivery with business strategy
