In 2025, the rapid rise of AI copilots in coding has shifted the focus from code generation to validation, with the challenge being to ensure AI-generated code is production-ready, maintainable, and aligned with organizational standards. While AI tools can create functional code quickly, they often overlook deeper issues such as redundancy, compliance risks, and architectural misalignment, necessitating thorough reviews to maintain quality. Tools like Qodo enhance this process by offering context-aware analysis, identifying high-risk areas, enforcing standards, and providing one-click remediation, which significantly reduces manual correction time and prevents potential system failures. The emphasis on code quality has evolved to include metrics like defect density and code churn, combined with AI-driven reviews to ensure changes fit within the larger system context and are sustainable over time. With AI-generated contributions becoming more prevalent, enterprises need a structured approach to measure and maintain code quality, incorporating both traditional metrics and contextual validation to mitigate risks and enhance maintainability. As demonstrated by real-world incidents, without comprehensive review and integration of AI with development context, AI-generated code can lead to unintended behaviors and security vulnerabilities, highlighting the importance of effective oversight and governance in AI-assisted development.