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A happy team isn't the same as a well-led one. This episode covers why psychological safety exists so people can raise problems and learn, a simple test for whether your team is safe or just comfortable, and one move to try after your next hard 1:1 or retro.
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A postmortem is only valuable when it leads to real change. Discover why engineering leaders should focus less on explaining what went wrong and more on implementing meaningful fixes that prevent the same incidents from happening again.
Learn how to turn incident reviews into lasting improvements through automation, clear ownership, and actionable follow-ups. Because real progress happens when lessons become changes in the system, not just notes in a document.
If your VP asked for your AI plan tomorrow, what would you hand them?
In this episode, Erwin walks through a 13-week plan for moving your engineering team from personal AI experiments to a written team policy: four stages, four small artifacts, and one 5-minute action to start today.
Key takeaways
Treat AI adoption like a release, not a hobby. Pick one team, one workflow, and block 3 hours a week for 13 weeks.
Stage 1, Sandbox (weeks 1-3): use AI on real work, try to break it, and keep a failure log.
Stage 2, Staging (weeks 4-6): have AI draft your recurring leadership documents, and review the output with a checklist the way you review code.
Stage 3, Production (weeks 7-10): build a decision ledger, a "where AI fits" grid and one agent runbook card, then pilot it for two weeks.
Stage 4, Operations (weeks 11-13): measure before and after, write a value case with kill criteria, sort uses into risk tiers, and brief your VP in 10 minutes.
Capstone: put it all on one page. That page is version 1 of your team's AI policy.
Your 5-minute action: open a doc called "Team AI runbook card v0" and fill in two lines: what one AI use on your team is for, and what it must never touch.
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