Point AI at the actual bottleneck — not just the coding, because coding was rarely the slow part.
The team friction Teams adopt AI “for coding” and celebrate faster keystrokes — while the work still piles up at testing, review, deployment, and the wait between them. If coding wasn’t your constraint, speeding it up just grows the queue in front of the real bottleneck. The team feels busy and ships no faster.
The pattern Before scaling AI anywhere, the team finds the bottleneck together and aims AI there first. Flaky tests, manual QA, slow releases, ambiguous requirements — wherever value actually stalls. Treat the whole value stream as fair game for agents: test generation, deployment scripts, observability, spec-writing — not coding alone.
Why it works with AI Agents are general-purpose; they’re as good at writing a deployment pipeline or a test harness as a feature. The leverage isn’t in the tool, it’s in where you point it. Aiming at the constraint is the difference between local speedup and faster delivery.
Watch-outs The bottleneck moves. Once you relieve one constraint, the next one surfaces — so this is a recurring team conversation, not a one-time audit. Resist declaring victory at “we use AI for coding now.”
Related Trap: measuring AI adoption by tokens spent · Product observability · Reshape roles — drop the silos