As universities accelerate AI deployments, workers and campus leaders are pressing for governance frameworks that reduce misuse while preserving learning benefits. Several institutions are moving toward centralized models—such as AI centers of excellence—to manage adoption, risk, and scaling across classrooms, research workflows, and campus operations. Meanwhile, employee advocates are urging clearer constraints on surveillance and data handling rather than tool-by-tool detection. The sector’s near-term focus is shifting toward policies that govern how AI systems access information, how they’re used in decision-making, and how campuses train staff and students to avoid errors and privacy lapses. The combined message: AI adoption is no longer optional, but institutions are trying to move from experimentation to accountable infrastructure before misuse becomes embedded into routine student and staff workflows.