Engineering in the Age of AI: 2026 Benchmark Report

Engineering organizations everywhere are adopting AI tools, with coding assistants leading the way

The Engineering in the Age of AI: 2026 Benchmark Report reveals how teams are using AI across the SDLC, what’s working (and what’s risky), and what defines true AI excellence.

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Adoption & Usage

AI adoption is uneven, and often bottom-up.

50%

of orgs have already standardized AI usage across teams

While others are still in pilot or exploration mode. Most engineering orgs report that adoption starts with individual contributors before formal policies catch up.

Impact

Anecdotal confidence is high,  but data is lagging.

91%

of leaders say AI has improved developer velocity and quality

But only 25%

actually have data to back it up. Measurement is still immature.

Most teams track productivity anecdotally, while a handful are starting to correlate AI adoption with metrics like deployment frequency, bug rate, and cycle time.

Governance & Risk

AI is creating a lot of questions

Few organizations have formal AI governance in place.  Only 32% have formal policies with enforcement, 41% rely on informal guidelines, and 27% have no formal governance at all.

Most rely on peer review and existing code quality checks but plan to introduce AI usage policies, tool whitelisting, or human review standards in the next year.

Foundations & Readiness

AI excellence starts with strong foundations.

Leaders consistently point to security, testing discipline, and ownership clarity as prerequisites for scaling AI safely.

Secure coding practices
Testing & automation coverage
Service ownership
Incident response practices

Top foundations identified include

Future Outlook

From hype to operational change

Over the next 12 months, most leaders expect a shift from manual coding to AI oversight and governance.

Majority of respondents predict stronger investment in testing, reliability, and security, while new skills like prompt engineering and AI-agent management emerge.