Institutions deploying AI are confronting a dirty secret: weak data governance undermines model performance and adoption. Higher‑education IT leaders and consultants warn that rolling out AI tools without clear data standards, provenance, and stewardship will produce unreliable results and slow faculty uptake. Experts recommend investments in data cataloging, governance frameworks, and cross‑functional stewardship to make AI usable and trustworthy for teaching, research, and administration. The conversation dovetails with broader sector planning about credentials, workforce alignment, and the infrastructure needed to support compute‑intensive research.