Making AI Adoption Work: A Practical Structure for Modern Software Development
summary
Practical structure shows that AI collapses coding time and shifts bottlenecks to testing, review, codify (versioned context), and planning; agents run automated tests and reviews but need precise prompts and guardrails to avoid automation complacency. Four guiding policies are explicit: everyone knows what success looks like, all teams show good tech health (DORA metrics), cross-role collaboration, and AI proficiency. Measurement combines tool usage, cycle time, review patterns and developer reflections to answer “Did I make something better?” and “Did I break something?”
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chapters (14)
- 0:00AI adoption challenges
- 4:29Cynefin framework for AI
- 7:42AI as complex challenge
- 9:54AI productivity principles
- 12:45AI impact on workflows
- 19:47Codifying knowledge in AI
- 23:21Outcome policy for success
- 26:51Measuring AI productivity
- 29:20Output policy for teams
- 31:29AI team collaboration
- 35:01Leadership for AI scaling
- 39:16AI prompting risks
- 41:45AI development future
- 42:49Q&A
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