Universities are rolling out AI tools but often find results disappointing because model performance rests on underlying data quality, governance and operational processes. The piece argues that campus AI projects fail when institutions treat AI as a plug‑and‑play deployment instead of investing in data stewardship, metadata, and access controls. Effective academic AI requires clear data governance, provenance, labeling standards and ongoing operational support; without that, models produce biased or unusable outputs and risk undermining faculty trust. IT leaders and provosts are advised to treat AI adoption as an integrated data initiative—aligning institutional data architectures, privacy compliance and faculty incentives—rather than a standalone pilot.