From policy on paper to practice across the institution.

Most institutions have issued guidance and convened a group for AI adoption. What happens next is harder — in the classroom, the aid office, and the advising appointment. We map what is actually happening across three layers, score your readiness, and deliver a prioritized action plan.

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  • 30%

    of higher ed administrators are unsure whether AI appears in their own institution's strategic plan. 43% say it does, 27% say it doesn't. (Ellucian)

  • 70%

    of AI transformation success comes from organizational design, talent, and change management. Technology infrastructure accounts for 20%, the models themselves 10%. (BCG)

  • 78%

    of universities are integrating AI tools into their administrative processes. (Gitnux)

  • 86%

    of university students use AI in their studies. (Campbell University)

  • 9%

    of university students admit to cheating using AI. (UC Berkeley)

Authority over AI decisions sits in several places at once — not by oversight, but by design. Shared governance gives instructors real authority over their own classrooms and gives schools and departments real authority over their own curriculum and tools. It's how institutions are built. Leadership sets direction. IT manages tools and risk. Instructors, advisors, and administrative staff make the daily calls that shared governance leaves to them, often in the absence of a settled institutional standard. Each decision can be sound on its own terms. But assembling them into one institutional picture is a separate job — and because shared governance was built to distribute authority, not to assemble it, that job belongs to no one by default.

What No One Owns

  • The board asks what the institution's AI position is, and the answer has to be assembled from a dozen places.

  • Departments procure independently, by design, so no single office holds the list of what the institution actually runs.

  • There is no agreed way to tell whether an AI investment worked, so renewal decisions rest on anecdote.

  • The offices that can't afford to be wrong — student finance, HR, admissions — hold back, reasonably, because no one has yet agreed who is accountable when an AI output is wrong.

  • Predictive and early-alert models go live before anyone has named who reviews them for bias or hears a student appeal.

  • Leadership weighs AI in cost and enrolment terms while faculty weigh it in workload and teaching terms, and no forum reconciles the two.

  • Faculty and staff absorb AI integration as unpaid labour, so adoption stalls where the work actually happens.

  • Schools and departments procure independently, by design, so no single office holds the list of what the institution actually runs. A student can hit contradictory AI rules within a single semester — allowed in one class, banned in the next — because no venue exists where school and department policies get reconciled.