Same Models, Different Outcomes: The Hidden Variables of AI Effectiveness

Most engineering organizations now have access to the same frontier models and coding agents, yet their outcomes diverge dramatically. Why do some teams ship low-cost, low-rework AI-generated code with minimal human intervention, while others grind through clarification loops, review churn, and runaway token spend?

Drawing on new research analyzing real-world agent trajectories across hundreds of engineering teams, plus market survey data on AI adoption, spend, and proficiency, this talk examines what distinguishes high-performing teams. The evidence points away from model selection and toward measurable, trainable factors.

Attendees will leave with a practical framework for instrumenting their own organization's AI effectiveness, including leading indicators they can measure before costs land, concrete practices that improve outcomes, and a clear-eyed view of where the market stands.