Research findings published in Info-Tech Research Group’s newly released AI Adoption and Impact Study indicate that AI is becoming embedded across the software development lifecycle, with 94% of developers reporting productivity gains and 83% reporting meaningful defect reduction. However, the firm’s study also shows that AI-generated code requires more testing, that security concerns remain a leading barrier to adoption, and that legacy code continues to limit AI’s effectiveness in development workflows.

ARLINGTON, Va., July 20, 2026 /PRNewswire/ — New research data from Info-Tech Research Group shows that while AI is changing how software is planned, built, tested, and delivered, many organizations are still working to align governance, security, and review practices with the rapid pace of adoption. The global IT research and advisory firm’s recently published report, AI Adoption and Impact Study: AI in Software Development June 2026 Top 10 Insights, examines how applications, engineering, and product leaders are using AI across the software development lifecycle and where adoption is delivering measurable value or creating new execution risks.

Info-Tech Research Group's recently published report, AI Adoption and Impact Study: AI in Software Development, June 2026 Top 10 Insights, examines how applications, engineering, and product leaders are using AI across the software development lifecycle and where adoption is delivering measurable value or creating new execution risks.

Based on 578 completed survey responses from leaders in Applications, Engineering, and Product who are actively adopting AI across the software development lifecycle (SDLC), Info-Tech’s report finds that 84% of respondents use AI in the Build phase, applying it to tasks such as analysis, design, development, and testing. At the same time, 67% of developers agree that AI-generated code requires more testing than human-generated code, which highlights a central tension for software leaders: AI is improving productivity and quality outcomes, but the practices needed to review, govern, and secure AI-assisted development are not yet keeping pace.

“AI is no longer sitting at the edge of software development. It is becoming part of how teams build and deliver work every day,” says Brian Jackson, principal research director at Info-Tech Research Group. “The challenge for leaders is that faster code creation does not reduce the need for disciplined delivery. It increases the need for clear review standards, security guardrails, and stronger measurement so organizations can turn AI adoption into sustainable value rather than unmanaged risk.”

Info-Tech’s report shows that productivity gains are already widespread. According to the firm’s survey data, 94% of respondents report meaningful productivity improvements from AI, and organizations with higher AI maturity at the Build phase are more likely to report greater developer productivity. The findings also indicate that AI is helping improve quality, with 83% of respondents reporting meaningful defect reduction in shipped code, including 54% reporting a 2x reduction, 21% reporting a 5x reduction, and 7% reporting a 10x or greater reduction.

However, the report insights also reveal that adoption is advancing faster than process maturity. Among developers using AI at the Build stage, Info-Tech reports that only 37.4% describe their AI maturity level as formal or better. These findings suggest that without standard operating procedures, production-readiness criteria, and clear review requirements for AI-generated code, organizations risk inconsistent quality outcomes as AI adoption scales across teams.

Key Findings From Info-Tech’s AI in Software Development Study
Info-Tech’s AI Adoption and Impact Study: AI in Software Development June 2026 Top 10 Insights report identifies several findings that applications, engineering, and product leaders should consider as they expand AI use across the SDLC:

AI is widely used in the Build phase, but review requirements are increasing. The report finds that 84% of respondents use AI in the Build phase, while 67% agree that AI-generated code requires more testing than human-generated code. The findings indicate that AI may shift developer time from writing code to reviewing and validating code, even as overall efficiency improves.

AI maturity remains behind adoption. The three most popular AI tools used in the Build stage are integrated development environment (IDE) plug-ins, AI chatbots, and agentic tools. Yet only 37.4% of developers using AI at the Build stage report formal procedures or better, suggesting that many teams are using AI in an ad hoc way without mature governance or review practices.

Security and quality concerns continue to limit adoption. Across all organizations surveyed, security and IP concerns are the top AI adoption challenge at 48%, followed by output quality concerns at 42%, skills gaps or training at 34%, data readiness and quality at 26%, and poor tool integration at 24%. Security concerns are especially prominent in medium and large organizations.

Legacy code remains the biggest workflow roadblock. The report finds that 51% of respondents say AI breaks on complex or legacy code, making it the most common personal roadblock to AI use in development workflows. AI code not passing quality gates follows at 46%, while inconsistent skill levels across teams are cited by 36.7% of respondents.

Experience shapes how teams use and govern AI. Practitioners with more than 15 years of experience are nearly twice as likely to use AI security tools as those with 3 to 7 years of experience. They are also more likely to raise security or codebase context concerns, suggesting that experienced practitioners play an important role in applying AI more carefully.

Developer roles are beginning to shift. Less experienced developers are more likely to report speed and efficiency gains from AI, while more veteran developers are more likely to report shifting their time toward reviewing and validating AI-generated code. Info-Tech’s findings suggest that leaders should ensure review processes include appropriately experienced staff before AI-generated outputs move into production.

The report also highlights a perception gap between product, engineering, and applications teams. When asked whether vibe coding with AI guarantees faster releases with zero errors, product respondents were the most likely to agree, while applications practitioners were the most skeptical. Info-Tech notes that this gap creates risk when delivery commitments are made before the teams responsible for quality have assessed the work.

“AI-assisted development can produce real gains, but the closer teams are to the code, the more they recognize the need for scrutiny,” explains Jackson. “Organizations should not treat AI as a shortcut around engineering discipline. They should use it as a reason to make security review, production-readiness standards, and quality measurement more explicit across the SDLC.”

Based on the data findings, Info-Tech recommends that software leaders focus on building practical governance around the highest-volume AI use cases first. This includes defining criteria for when AI-generated code is production-ready, establishing review expectations for security and quality, involving engineering and applications teams in AI adoption decisions, and tracking defects by category to understand where AI is improving outcomes and where human oversight remains critical.

The AI Adoption and Impact Study: AI in Software Development June 2026 Top 10 Insights report is part of Info-Tech’s ongoing AI Adoption and Impact Study. The research provides evidence-based data and expert analysis to help IT leaders understand how AI is affecting software development, where it is creating measurable benefits, and where barriers to integration and value delivery remain.

For exclusive and timely commentary from Info-Tech’s experts, including Brian Jackson, and access to the complete AI Adoption and Impact Study: AI in Software Development June 2026 Top 10 Insights report, please contact [email protected].

About Info-Tech Research Group
Info-Tech Research Group is the “get things done” partner for over 30,000 IT, HR, and marketing leaders worldwide. The fastest growing research and advisory firm, Info-Tech enables leaders to make well-informed decisions and transform their organizations through AI, strategic foresight, step-by-step methodologies, practical tools, industry-leading advisory, and training programs. For nearly 30 years, tens of thousands of private and public organizations have trusted Info-Tech to lead their most important initiatives through periods of change and deliver outcomes that truly matter.

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