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AI usage patterns in software teams
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- DeepHorus
A new report from Linear, the popular issue-tracking and product development platform, reveals that AI adoption among software teams has more than doubled across every function in the first half of 2026. The most surprising finding: CEOs are adopting AI faster than their own teams, and AI now authors nearly half of all issues created in Linear. These numbers provide the first comprehensive look at how AI is reshaping the entire product development workflow, not just code generation.
Linear has accumulated six years of workflow data from tens of thousands of software teams, spanning the period before and after widespread AI adoption. While model companies have published data on token usage and code volume, Linear is uniquely positioned to capture the entire process, from issue creation to pull request closure. The report acknowledges it cannot see AI usage outside Linear, so the data reflects adoption within its own customer base rather than the broader market.
Between January and June 2026, the share of users active on AI features more than doubled in every functional role. Product teams saw the steepest climb, from 12% to 34%, while go-to-market teams, furthest from the codebase, rose from 5% to 18%. Executives were also hands-on: CEOs at companies with 201 or more employees jumped from 9% to 36% in six months, the largest increase of any segment measured. Adoption roughly tripled across all company sizes, from startups to enterprises.
The report tracks users engaging with Linear's native AI features, not external tools, and normalizes job titles to classify roles. It also measured how time allocation shifted: between June 2025 and June 2026, time spent creating, triaging, and commenting rose in nearly every function, with engineering up about 17% on creation and triage. Founders showed even larger swings, adding 17 minutes per month on creation and 26 on commenting, though this smaller cohort is noisier. Remarkably, AI-written issues have surged from fewer than one in a thousand two years ago to nearly half of all issues created today.
These changes mean that AI is not just accelerating coding but also increasing the coordination burden around it. More work requires more context-setting and management, which is why time on planning, customer requests, and docs held steady even as everything else moved. The report suggests that planning remains a deeply human activity, at least for now. For software teams, the practical takeaway is that AI has become an integral part of the workflow, not an experimental add-on.
Industry analysts see the data as evidence that AI has crossed the chasm from pilot projects to standard practice. The consistency of adoption across company sizes is particularly notable, as larger organizations typically move slower on new technology. While the report does not include direct quotes, the implication is that senior leaders are learning by doing rather than delegating.
At the current pace, Linear predicts that AI will soon author more issues than people and integrations combined. The company plans to release future editions of the report, using the 2026 data as a fixed baseline to measure progress. As AI agents become more sophisticated, the boundary between human and machine work will continue to shift.
This report adds a critical layer to the ongoing conversation about AI in software development, complementing data from code-focused tools like GitHub Copilot. While much attention has been paid to code volume, Linear shows that AI's impact on the entire lifecycle β from idea to issue to execution β is just as profound. For companies building software in 2026, the question is no longer whether to adopt AI, but how to manage the new balance it creates between human judgment and machine output.
