Download to see how twenty US universities sequenced their AI rollouts.
Policy-by-pilot, governance-first, federated, and faculty-led — compared on time to first deployment, stakeholder buy-in, risk profile, and institution type they fit. Including why the responsible-looking option is usually the one that stalls.
Five of seventeen publicly documented institutions appointed a dedicated senior AI executive. The brief names them, maps five models for where AI leadership actually sits, and compares three vendor stack shapes on lock-in, FERPA fit, and faculty acceptance.
Five recurring risk patterns — governance theater, permanent sandboxes, lock-in through embeddings, faculty backlash, FERPA exposure via shadow IT — and five concrete recommendations you can take to a board conversation next week.
Most universities did not start their AI program with a policy. They started with one narrow pilot, usually internal data or analytics, and let governance catch up to real usage. Pilot first, policy second is the clearest pattern across the institutions we reviewed — and it runs against how most AI programs get proposed internally.
This brief maps that sequence across four dimensions: governance, AI leadership hires, vendor stacks, and the split between student-facing and internal-facing tooling. It is written for a CIO, VP of IT, or Chief Digital Officer under board pressure to do something with AI, who would rather see what peers did than read another framework.
We are explicit about where the evidence comes from. Two institutions are ones we worked with directly on AI rollouts, anonymized here — that is where the deeper process detail sits. Seventeen are documented from public sources and named, and every counted claim rests on those. It is not a vendor evaluation, not a technology overview, and not an introduction to AI. It assumes you already run a complex institution.
