Artificial Intelligence (AI) is increasingly being adopted across businesses, with nearly half of enterprise-scale companies actively deploying it. While there are concerns about new risks from AI, it's crucial that fear and skepticism don't hinder progress. Companies should approach their AI adoption open-mindedly and prioritize quality control to minimize disruption and risk while maximizing productivity and innovation. A "trust but verify" approach is recommended, where AI output is verified with human review. This can be achieved by pairing the approach with Sonar's Clean Code solutions like SonarQube, SonarCloud, and SonarLint. AI has the potential to greatly enhance productivity by automating mundane tasks, allowing employees more time for creative thinking and problem-solving. However, it also poses risks such as creating a gap between individuals who leverage AI effectively and those who don't, leading to misalignment within teams. Furthermore, if not properly managed, the use of AI in software development could exacerbate existing issues with bad code and technical debt. To mitigate these risks, companies should approach the adoption of AI coding assistants and tools with an eye toward quality control. All code, whether human or AI-generated, must be properly analyzed and tested before being put into production. Developers should turn to AI for volume and automation of mundane tasks but must have the right checks in place to ensure their code remains a foundational business asset. Establishing safeguards is crucial when harnessing AI for good. Companies need to understand where and how AI is being used, think through their investments, and put in place easily adaptable governance as things continue to rapidly change. Trusted frameworks like NIST's Secure Software Development Framework can be a great starting point. Sonar's powerful code analysis tools - SonarQube, SonarCloud, SonarLint - enable developers to integrate with popular coding environments and CI/CD pipelines for in-depth insight into the quality, maintainability, reliability, and security of their code no matter if human or AI-generated. By taking a "trust but verify" approach across all aspects of AI use, organizations can ensure they are leveraging this technology effectively while minimizing risks.