Generative artificial intelligence (GenAI) governance and orchestration face complex challenges: diverse use cases, unmanaged risks, limited knowledge retention, and difficulties in measuring impacts. These barriers hinder consistent, scalable, and innovative GenAI adoption. Rather than treating GenAI as a technical tool, this article conceptualizes Generative AI as a practice. We introduce an integrative theoretical framework (DCS), grounded in three knowledge management theories, to align strategic thinking, collaboration, and knowledge structuring. Implemented through the CREW model (Components, Roles, Environments, Workflows), this framework fosters agile, collaborative governance that clarifies responsibilities, tailors deployments, retains knowledge, measures practices, and supports responsible GenAI adoption. The CREW model provides a practical roadmap to optimize GenAI value while mitigating risks. Its theoretical contributions advance the understanding of GenAI governance in organizations.