Why some teams see 20-point jumps in productivity while others see performance decline

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Software Engineering Research

Engineering leaders can drive significant AI productivity gains by prioritizing psychological safety, implementing structured enablement programs, and measuring impact through a mixed-methods framework. Successful AI adoption requires positioning tools as performance multipliers rather than replacements, investing in education and controlled pilots before scaling, and focusing on automation across the entire SDLC beyond just code generation. Teams with strong leadership strategy see major velocity improvements while others experience stalled adoption or confusion.

The AI Strategy Playbook: What engineering leaders need to know about driving AI impact