Higher education institutions are embracing AI tools but frequently lack the data governance needed to make models reliable and scalable, industry analysts say. An editorial and advisory content warns colleges that AI success depends less on tool selection than on clean, governed, and operationalized institutional data sets. The guidance recommends stronger master‑data practices, provenance tracking, and cross‑functional data stewardship so models used for admissions, advising, and academic analytics can be trusted. Campus leaders should treat data governance as a strategic program: invest in inventories, metadata, and privacy controls before broad AI deployments. Practical steps include cataloging data sources, establishing ownership across registrar, IT and research offices, and piloting AI on well‑scoped use cases with human oversight to measure bias and accuracy before scaling.