AI usage patterns
in software teams

Between January and June 2026 the share of users active on AI features more than doubled in every function. Product climbed fastest — 12% to 34% — and even go-to-market, the function furthest from the codebase, went from 5% to 18%.

Roles are classified by normalizing job titles, which carries some error at the edges — but the pattern is too broad to be a labeling artifact.

Executives are personally active on AI at rates that match or beat their teams. CEOs at companies of 201 or more people went from 9% to 36% in six months — the largest jump of any cut in this report.

The most senior leaders appear to be learning the technology by using it rather than reading about it. Company size comes from third-party enrichment, so this cut covers fewer workspaces than the rest of the report.

AI adoption roughly tripled everywhere, from startups to enterprises. Company size — usually a good predictor of how fast an organization moves on new technology — barely registers here.

Between June 2025 and June 2026, time spent creating, triaging and commenting rose in nearly every function — engineering up roughly 17% on create and triage alone. Founders show much larger swings: +17 minutes on creation, +26 on commenting. They’re a smaller cohort, and noisier for it.

More work seems to need more coordination — and that coordination increasingly sets the context agents act on.

The same pattern shows up in what gets created. Two years ago, fewer than one issue in a thousand was created by AI. Teams now use AI to write just under half of everything created in Linear — and at the current pace it will soon author more than people and integrations combined.

Time spent on customer requests, docs and projects held steady in a year when nearly everything else in this report moved up. Planning practice varies widely from team to team — and plenty of it happens in conversation before it lands anywhere — so the average blends heavy planners with light ones.

What the steadiness suggests: AI has so far changed how teams execute far more than how they decide what to build.

Chatting with AI and delegating issues to agents are categories of work that didn’t exist a year ago. They now show up in every function’s week — product leaning in hardest, at five minutes a month on AI chat alone.

Nothing else shrank to make room. AI has landed on top of existing work rather than replacing any of it — at least so far.

The share of product managers attaching pull requests rose from 3% to 10% in two years; designers went from 1% to 8%. Only pull requests in repositories connected to Linear are counted — anyone shipping outside that loop is invisible here, which makes these numbers floors rather than ceilings.

The people who used to describe a change increasingly ship it themselves.

Pull requests opened per workspace are up 111% on a June 2024 baseline. Output held roughly level for the first year, then bent upward through 2026 as model quality and adoption climbed together.

The report counts PRs opened rather than merged — an opened PR says nothing about the value of the change — but the inflection is hard to miss.

Teams that connected a coding agent roughly tripled their weekly pull requests over two years — 21 to 65 — while teams without one went from 8 to 10.

Agent teams were already higher-output before coding agents existed, so the levels aren’t directly comparable. But each cohort against its own baseline tells a clean story — and nearly all the growth sits on the agent side.