Commentary and analysis this week stress that higher education must prioritize data readiness before scaling AI tools. Leaders argue AI offers tutoring, administrative relief and new pedagogies, but success requires automated, validated data systems, unified workflows and interpretability so institutions can act on predictive analytics. Practitioners urge investment in people, governance and granular behavioral data to support enrollment, retention and student success models. Without that groundwork, pilots risk failing and eroding trust among faculty and students.