Notes on whole-team AI adoption
For founders and eng leaders whose teams bought the tools and only a few people got faster.
When AI coding usage is concentrated in a few fast engineers, review load spikes instead of dropping. Here is why, and how a shared workflow fixes the review bottleneck AI often creates.
Vague guidance like 'use good judgment with AI' does not survive contact with a real sprint. Here is what a usable, whole-team AI coding standard actually needs to cover.
Not everyone using AI the same amount. A concrete picture of what it looks like when a whole eng team, not just the early adopters, is actually running on a shared AI workflow.
The first AI coding workflow that actually sticks with a whole team is narrow, owned, and reviewable. Start with one owner, one trigger, one output, and one review step.
Want a faster answer than another month of reading?
See if a guided whole-team path makes sense for your team. If I'm not the right person, I'll say so.
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