Tracking software development workflows over the past six years has created an empirical baseline for evaluating how engineering organizations actually build software. While foundation model vendors and specialized code editors relentlessly publish vanity metrics centered on raw token throughput and generated lines of code, those figures capture only an isolated, noisy sliver of the software development lifecycle. Issue-tracking platform Linear Orbit Inc. analyzed internal activity across tens of thousands of organizations to establish how AI tooling reshapes workflows from initial task ideation down to the pull requests that resolve them.
Cross-Functional Expansion
Between January and June 2026, active utilization of native AI features surged across every organizational function rather than remaining isolated within IDEs. According to Linear's dataset tracking 127,000 paid users across that six-month window, active engagement with platform AI capabilities more than doubled across all departmental roles. Product management logged the steepest rise, leaping from 12% adoption in January to 34% by June 2026. Engineering roles climbed from 12% to 30%, design expanded from 6% to 22%, and even go-to-market teams climbed from 5% to 18%.
"Model companies and coding tools have published plenty on token usage and code volume, but that captures only one layer of the work. We’re unusually well placed to see the entire workflow behind building a product, from the first issue to the pull request that closes it."
As Tim Qi noted in Edition 01 of the Linear report, tracking the entire delivery pipeline exposes the operational friction that purely code-centric telemetry systematically overlooks. The emerging bottleneck is no longer initial draft creation or boilerplate authoring—it is verification throughput. While upstream planning and code generation accelerate, engineering managers and senior reviewers face expanded PR volumes, shifting the core engineering burden from raw authoring toward exhaustive review and architectural triage.
Executive Adoption Patterns
For engineering leadership, measuring real ROI requires moving past generated token volume to track cycle time from issue triage to merge verification. CTOs must audit internal issue tracking and PR review latency this week to verify whether AI adoption is genuinely accelerating delivery or simply shifting bottlenecks upstream into unreviewed backlog accumulation.