Universities and education leaders are accelerating efforts to manage AI adoption through centralized governance models, including the creation of AI centers of excellence. The approach is intended to coordinate uneven classroom and research experimentation, establish risk controls, and create repeatable standards for deployment and scaling. The emerging model mirrors enterprise governance: cross-functional teams guide tool selection, evaluate academic and operational risks, and set policies for where and how AI is allowed. With generative AI now embedded across curricula and administrative workflows, institutions are focusing on consistency and compliance rather than isolated pilots. At the same time, external pressure is rising on how higher education should safeguard data and protect privacy, as AI systems introduce new security and confidentiality failure modes. The combination of governance gaps and rapid tool diffusion is driving demand for formal frameworks that can be audited and communicated to faculty, staff, and students. How AI CoEs are designed—who owns decision rights, what risk boundaries apply, and which processes are standardized—will likely shape institutional outcomes as AI use expands from learning support to research operations and student-facing services.