Stop throwing AI at developers and hoping for magic

Estimated reading time: 7 minutes

Key takeaways:

• AI adoption is fragmented and widening performance gaps: some teams report dramatic productivity gains, others struggle with basic prompting. 
• Tools alone don’t drive productivity – methods and training do: simply rolling out AI tools doesn’t guarantee results.
• Treat AI adoption like a scientific experiment: leading teams don’t just measure AI usage, they measure outcomes.

Behind the headlines, AI adoption in large organizations is messy, uneven, and often ineffective. Engineering leaders typically struggle to bridge the gap between early adopters seeing massive productivity gains and teams still figuring out basic prompts.

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