A new policy-and-operations critique argues universities are treating AI primarily as an academic integrity issue, while students are already building “AI fluency” for core tasks like learning, advising, and demonstrating knowledge. The piece warns that institutional AI responses remain fragmented, with uneven faculty guidance and policies that focus on misuse rather than enterprise strategy. The argument extends beyond classroom concerns to campus operations, including recruitment, admissions decisions, and risk assessment—areas where policy inconsistency can erode institutional trust. It also points to limits of AI detection technologies and cautions against relying on imperfect tools as if they were definitive. For higher education leaders, the immediate takeaway is governance: boards and senior leadership are being urged to build enterprise-wide AI architecture and coherent learning assessment policies that match how students are already using AI.