Higher education institutions deploying artificial intelligence are confronting a common barrier: poor data quality and weak governance. Experts warn that AI projects in colleges and universities often fail not because of model architecture but due to fragmented, poorly documented data and lack of operational pipelines. The result is low trust, stalled adoption, and potential compliance risks. Campus IT and institutional research leaders are being advised to prioritize data inventories, metadata standards, and cross‑unit governance frameworks to make AI reliable and scalable for admissions, advising, and research administration.