Every few weeks someone asks me how AI is changing team productivity. The honest answer: less than most people think, and in different ways than expected.
Individual engineers using Copilot or ChatGPT to write code faster is fine. It’s also not the point. One person moving 20% faster doesn’t help if the team is still bottlenecked on the same things it was bottlenecked on six months ago: stale docs, unclear decisions, and onboarding that requires cornering a senior engineer for two hours.
The teams I see getting real gains are the ones that treat AI as shared infrastructure. Not a personal productivity hack. Infrastructure.
What that looks like in practice
A shared assistant for team documentation and search. Not a chatbot that guesses – something that points to actual internal sources and tells you who owns what. Automated meeting summaries that feed into the same system where the team already tracks decisions. Onboarding workflows where a new hire can get a credible first answer and a pointer to the right human, instead of posting in Slack and hoping someone responds.
None of these need perfect accuracy. They need consistent routing and clear expectations about when AI is advisory versus authoritative.
The measurement trap
Here’s where most teams go wrong. They measure AI tool adoption. Number of prompts. Lines of code generated. That’s like measuring how many emails your team sends and calling it productivity.
The only question that matters: is the team less stuck?
Fewer repeated questions about the same topic. A shorter gap between a decision being made and that decision being documented. Less rework because someone missed context from a meeting they weren’t in.
If AI usage goes up but those numbers stay flat, you have added a toy, not infrastructure.
Docs, specifically
Documentation is where AI has the most underrated impact. Not generating docs from scratch – that’s garbage. But proposing small updates when code changes, flagging content that no longer matches reality, and making the update feel like a five-second approval instead of a batch project.
At a startup I ran, we struggled with doc decay like everyone else. The trick was making updates feel like routine housekeeping, not a chore you schedule for “next sprint” and never do.
Start small, stay boring
Pick one shared workflow. Make it reliable. Expand based on evidence, not enthusiasm. A small, visible win – like meeting notes that are actually useful the next day – changes team behavior more than any broad AI rollout plan.
The teams getting durable gains are the ones keeping AI practical, scoped, and accountable. Boring wins. As usual.