Higher education leaders are moving from AI “posture” to “placement” in strategy and budgets, but the limiting factor is increasingly data readiness rather than model availability. A new editorial argues that institutions’ first transformational AI investments determine governance norms, trust, and confidence for later initiatives. The same analysis ties AI adoption to downstream metrics issues in advancement: it cites declines in alumni donor pipelines among institutions and notes a shift in U.S. News rankings, which removed alumni participation rates from methodology—reducing a longstanding proxy used in fundraising evaluation. The editorial’s core message for trustees and presidents is that boards should sequence AI investments around data infrastructure and advancement measurement capabilities so institutions can demonstrate outcomes rather than just deploying tools.