Higher-education leaders are increasingly warning that AI adoption and student-success initiatives will fail without stronger data foundations and clearer governance. In one analysis, authors argue that the sequencing of institutional AI matters because early investments set the data infrastructure and trust norms that determine whether later uses can succeed. Separate reporting and commentary emphasize that student retention declines often reflect operational bottlenecks in data intelligence rather than advising shortages or staffing alone. Institutions that cannot assemble a real-time, 360-degree view of students risk acting reactively when persistence problems escalate. Together, the coverage points to a near-term focus for boards and executives: invest in data readiness, define governance, and align advancement and student-success metrics to reduce friction and maximize intervention timing.