A new higher education technology briefing reframed institutional AI readiness around one operational prerequisite: data readiness. The piece argues that campuses must treat AI readiness as a data governance problem—asking whether data is structured, governed, and usable before building any AI roadmap. The framing is aimed at boards and senior leaders who want an “AI plan” but may lack the underlying governance and data-quality foundations needed to support AI initiatives responsibly. While details are not presented as a formal policy action, the message aligns with growing higher ed demand for AI oversight and internal compliance controls, especially where student data and research workflows intersect. For campus leaders, the practical takeaway is to prioritize data governance and risk controls in early AI strategy work, rather than treating compute and model selection as the first-order issue.