The allure of increased developer productivity is driving rapid AI adoption, but the output often masks deep-seated risks. Ari Glaizel, associate vice president of research development at Info-Tech, notes that AI-generated code introduces a unique class of errors because the technology lacks a fundamental understanding of business context and long-term operational impact. Because this code often appears polished, it frequently bypasses the scrutiny usually applied to human-written scripts.

Info-Tech’s new blueprint, Defend Against Defects and Technical Debt in Your AI-Generated Code, outlines a governance framework to counter these hazards. The firm identifies four primary threats: an overreliance on AI output that weakens verification, the emergence of inconsistent coding standards, the introduction of novel defect patterns, and a disconnect between technical correctness and actual business requirements.

To mitigate these dangers, the firm proposes a phased approach: defining specific use cases for AI across the development lifecycle, auditing delivery pipelines to embed non-functional requirements, and establishing formal AI-specific pull request checklists. By centralizing human accountability, development teams can leverage AI efficiency while maintaining the security and maintainability standards required for enterprise-grade software.