Where does your team stand on AI adoption? The 2026 report
AI coding tools went from experiment to expectation in under two years. But adoption is uneven: some teams ship twice as fast, others are stuck cleaning up after their agents. We asked 1,200 engineering leaders what separates the two.
Key findings
- 71% of teams use AI agents weekly, but only 28% let non-engineers contribute code.
- Teams that share context across roles report 2.3× higher satisfaction with AI output.
- The top blocker isn’t model quality — it’s review bottlenecks.
The four stages of AI adoption
We found teams move through four stages: experimenting (individuals try tools), accelerating (engineers ship faster), collaborating (every role contributes to the codebase) and compounding (agents handle the long tail automatically).
The biggest gains came when designers, PMs and marketers could safely make changes — not when engineers typed faster.
What leaders should do next
- Index your design system so AI output stays on-brand.
- Route every AI change through pull requests.
- Give non-engineers a safe, visual way to contribute.
Want the full dataset? Talk to our team for a walkthrough of the benchmarks for your industry.
