Your teams have AI coding tools. Adoption rates are high. Leadership celebrates the win. But velocity hasn’t improved. Tech debt is exploding. You’re generating tons of code, but building the wrong things faster than ever. The problem isn’t the tools. It’s treating adoption as the finish line instead of the starting line. Over 12 months coaching development teams through AI coding adoption, I’ve seen the same pattern: teams that invest in learning together (2-4 hours/week) compound their improvements. Teams that skip the learning investment burn out their senior developers cleaning up AI-generated code (10+ hours/week).

This talk covers:

• The three adoption patterns I observe repeatedly (and why only one works)
• What high-performing teams do in their learning sessions
• One practice engineering leaders can introduce immediately to shift trajectory

No tool evangelism. No generic “AI will change everything” hype. Just patterns from the field: what works, what fails, and why the learning investment is your competitive advantage. Attendees will leave with a framework to diagnose where their team is struggling and concrete practices to shift from cleanup mode to capability building.

Steven Diamante is a technical coach who helps software teams get real value from AI coding agents. Most teams use AI to generate code fast, but then spend hours debugging. Steven teaches teams to use