To prevent "shadow AI"—where developers bypass official protocols for unapproved tools—organizations must transition from static policies to integrated engineering workflows. While Stack Overflow research indicates that 84% of developers intend to use AI, there is a significant trust gap due to inaccuracies and the debugging burden of "almost right" outputs. Effective adoption requires moving governance into the actual development lifecycle, such as embedding controls within IDEs, pull requests, and CI/CD pipelines. Rather than mere prohibition, leaders should utilize frameworks like NIST to manage risks through continuous functions: Govern, Map, Measure, and Manage. Technical implementation should involve version-controlled model configurations, automated scanning for secrets in prompts, and role-based access controls to mitigate OWASP risks like prompt injection and sensitive data disclosure. Furthermore, organizations must move beyond vanity metrics like prompt counts, instead measuring engineering outcomes such as cycle time, escaped defects, and rollback rates. Ultimately, responsible AI adoption succeeds when the sanctioned, secure path is more efficient for the developer than improvised workarounds.