AI coding assistants are changing the software development process, but the results aren't entirely positive. While 81% of developers report feeling more productive with these tools, new research indicates problems like growing instability and duplicate code, alongside faster delivery cycles. Engineering leaders must figure out how to utilize AI effectively without compromising the quality and maintainability of their projects. Measuring AI's impact requires looking beyond just productivity metrics. A poor developer experience can highlight underlying issues in workflows that may go unnoticed. This article emphasizes the importance of addressing these hidden risks to ensure that teams can work efficiently without accruing excessive technical debt. To navigate these challenges, the article suggests practical strategies for AI-assisted teams. It outlines a roadmap for sustainable AI adoption, focusing on finding a balance between speed and stability. The push for faster development shouldn't come at the cost of quality, and leaders are urged to consider the long-term implications of their AI integration choices.