Everyone's talking about how AI is transforming software development. Teams are shipping more code, deploying more frequently, and getting features to market faster than they could a year ago. The productivity gains are real.
But we kept hearing a different story from engineering leaders. Yes, velocity is up. But incidents are climbing, resolution times are getting longer, and code review processes are struggling to keep up. The gains from AI-generated code are being offset by quality problems that many organizations didn't see coming.
We wanted to understand what's actually happening. So we surveyed over 50 engineering leaders and analyzed development metrics across multiple organizations. The result is the Engineering in the Age of AI: 2026 Benchmark Report, and what we found reveals both the promise and the hidden costs of AI adoption.
The productivity paradox
The data makes it abundantly clear that AI acts as an indiscriminate amplifier. It takes your existing engineering practices, both the good and the bad, and magnifies their impact.
Our research surfaced four key findings:
Teams are moving faster. PRs per author are up 20% year-over-year, and deployment frequency is up across the board.
But quality is taking a hit. Incidents per pull request increased by 23.5%, and change failure rates are up approximately 30%.
Governance is inconsistent. Only 45% of organizations have formal AI usage policies, leaving most to operate without centralized oversight.
The differentiator is engineering foundations. Organizations with strong testing practices, clear service ownership, and robust documentation see better outcomes.
These trends paint a clear picture of an industry grappling with the hidden costs of AI-driven speed. The challenge for leaders is to harness the velocity without undermining the stability of their systems.
How engineering teams can scale AI adoption safely
AI adoption isn't slowing down. Nearly 90% of engineering leaders report their teams are actively using AI tools, with adoption ranging from individual experimentation to mandatory rollouts. The question is no longer whether to adopt AI, but how to do it without creating more problems than you solve.
The teams that thrive in 2026 won't be the ones that ship the fastest. They'll be the ones that invest in the engineering foundations that make sustainable speed possible: comprehensive testing, clear ownership, robust incident response, and quality governance that scales with velocity.
Get the full report
The rapid adoption of AI has prompted engineering leaders to think more deeply about how to leverage its speed without sacrificing the quality and stability that customers depend on. Our research shows that the answer isn't to slow down, but to invest in the engineering foundations that make sustainable speed possible.
The full Engineering in the Age of AI: 2026 Benchmark Report provides the detailed data, peer insights, and actionable frameworks to help you navigate this shift.
Ready to dive in? Download the report to learn more.
