The State of AI-Powered Software Development

The report covers four interconnected themes that define where AI-powered development stands today. You’ll find data on productivity — including the fact that AI coding assistants save developers an average of eight hours per week — alongside an honest look at the bottlenecks offsetting those gains: manual review, security testing, and code rework. It also digs into the governance gap, which is one of the most critical findings: only 30% of teams have full governance in place for AI coding assistant adoption, yet those who do are 55% more likely to see a major improvement in efficiency. Security risk rounds out the picture, with 64% of teams expressing moderate or extreme concern about AI-generated code introducing vulnerabilities.

If you’re responsible for development velocity, application security, or engineering strategy at a mid-to-large organization, this report is built for you. The survey respondents come from organizations with at least 500 employees — most with 2,000 or more — and more than half hold C-suite or senior leadership roles. Whether you’re trying to make the case for AI governance investment, benchmark your team’s adoption maturity, or understand the security implications of AI-generated code at scale, the data here speaks directly to your challenges.

Because the adoption curve has already moved past the question of whether to use AI coding assistants — 97% of survey respondents are actively using them today. The question your organization needs to answer is how to scale that usage without creating security debt or governance blind spots you can’t see until it’s too late. AI is accelerating code generation faster than most teams can review, secure, or govern it. This report gives you the industry data to understand exactly where that gap exists and what the organizations closing it are doing differently.

The report includes a deep profile of trending methods in AI-assisted development and concrete recommendations for building the governance and security infrastructure your AI-powered development program demands. You’ll be able to benchmark your team’s current AI adoption maturity against 831 of your peers, identify the specific workflow stages where AI-generated code is creating the most risk, and build a stronger internal business case for the tooling, processes, and oversight models that unlock AI’s full efficiency potential — not just part of it.