Building AI Fluency: Leading Teams Through the Learning Curve
summary
Argues that AI fluency is judgment not tools and introduces the PATH framework: Psychological safety, Adopt gradually, Teach through champions, Harmonize practices. Recommends phased rollout—observer / assistant / integrated phases—with 3–6 months for the J‑curve, examples from Zapier and SpotAI, and concrete safeguards: review all AI‑generated code, require AI attribution in PRs, run AI SQL against test DBs, and add verification tests for refactors. Notes common failures: hallucinated APIs, AI-only reviews, and runaway technical debt.
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chapters (23)
- 0:00AI fluency in teams
- 2:13Why AI fluency matters
- 5:05Review AI generated code
- 7:32AI code dangers
- 9:16Build AI judgment
- 10:58Human fears in AI
- 13:47PATH framework for AI
- 14:52Psychological safety first
- 15:54Gradual AI adoption
- 17:18Unexpected champions teach AI
- 18:20Freedom versus control in AI
- 19:02Safety with AI fails
- 21:34AI adoption phases
- 25:05Champions beyond engineering
- 28:37Balancing freedom and standards
- 32:03Measuring AI fluency success
- 34:32Key takeaways for leaders
- 36:58Closing and call to action
- 37:20Q&A on AI adoption
- 39:06Embed AI literacy everywhere
- 40:09Train in responsible AI
- 41:54Delivery speed with AI
- 43:18Collaborate across AI levels
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