Colleges and universities are recalibrating cloud strategies as AI workloads expand beyond research into advising, enrollment and campus operations. IT leaders are weighing on-premises, public cloud and hybrid options to balance latency, cost and data-residency requirements. The shift is pushing governance teams to codify vendor controls, data classification and procurement rules tied to student privacy and FERPA compliance. The challenge is not purely technical. Institutions must update governance frameworks to define who approves models, which data can be used for training, and how liability is allocated across vendors. Vendors and campus leaders told IT outlets that procurement timelines and compliance hurdles are lengthening as legal and privacy teams push for stricter contracts. Experts point to national examples of institutional adoption friction: even countries with massive scale, like India, expose how institutional rules and identity systems (e.g., Aadhaar) shape AI deployment. That case underscores that institutional absorption—changes to processes, roles and incentives—matters as much as chip or model choice. For higher‑ed leaders, the immediate work is operationalizing AI risk registers and aligning cloud decisions with institutional mission and student protections.