AI Is Moving Faster Than Higher Education. Here’s Who Can Close the Gap.

This is the first piece in a series I am calling The University Intrapreneur. I have spent 20 years inside innovation programs at companies and universities on six continents, and I have watched the same failure happen every time an institution waited for permission it was never going to get.

AI is disrupting education. Most institutions have not caught up, and I put the reason down to arithmetic. A new degree program can take up to 18 months to move from proposal to approval, and a tenure-track faculty search runs 9 to 12 months from posting to a start date. Those timelines protect quality, and they were built for a world where the job a graduate walked into looked the same at the end of the process as at the start.

That world is gone, and no policy closes the distance, because policy moves at the speed the institution already moves. Only people can close it, the ones willing to build inside the cycle while the cycle is still running its slow vote.

Who drives innovation inside a university?

The university intrapreneur can be anybody. The role usually lands on a mid-level faculty or staff member who found a real problem and built a working answer on borrowed time, but it carries no fixed title and no fixed department. The org chart does not account for that person, the budget has no line for the work, and the institution’s ability to change depends on them anyway.

Gifford Pinchot III coined the word intrapreneur in a 1978 white paper written with Elizabeth Pinchot, and called them dreamers who do. Pinchot was writing about corporations, and the university version carries more weight now, because the distance they absorb is wider.

Anyone can be this person: faculty, staff, a department administrator, someone in the library. Most of them already have the talent. What they need is a name for the role, a tool built for the problem in front of them and an institution willing to give support and then step out of the way.

Can a university actually move this fast?

Yes, at a scale that should embarrass anyone who says a campus cannot move. The GI Bill put about eight million veterans through education, and by 1947, half of American college students were veterans.

UTeach, the smaller version of the same instinct, started as one faculty-built route at the University of Texas at Austin in 1997 and now runs at more than 40 universities, years before anyone wrote it into a plan.

Why can’t this wait another year?

More than a third of entry-level jobs now require AI skills, according to the National Association of Colleges and Employers, nearly triple the share that said so in the fall of 2025. That did not take five years. It took two semesters, one hiring cycle, the length of a single academic year.

A university that reviews curriculum on a seven-year cycle cannot answer a demand curve that moves that fast through a committee. It can only answer it through people already inside the building who build before the committee meets. Every graduate walking across the stage this spring is being measured against a bar that moved twice since they declared their major.

I hear the same objection from the hardest AI skeptics on every campus, and I take it seriously: A tool is not a strategy, and adoption numbers are not the same as judgment. They are right about that. But this argument has never been about the tool. It is about whether the institution around the tool can still change shape when the world outside it does. A university that cannot produce people willing to build ahead of policy will lose to one that can, with or without AI in the room.

What can you do this month? 3 moves, in order.

1. Name the role, then find the people already doing it, and name them where others can hear it. Nobody volunteers for something with no name, and nobody keeps building something nobody has celebrated. They come from anywhere: faculty, staff, students. Look for the unofficial tool half a department relies on, the pilot that outlived its funding, the student club that solved something the provost’s office is still studying.

2. Build the channel before you build the lab. One shared, visible place, open to faculty, staff and students alike, where builders say what they are working on before it is finished. Communication is what actually connects them: The campus that talks to itself catches two departments building the same project in parallel before either runs out of runway. Keep the bar to enter low, and give whoever uses it air cover — a named senior sponsor and one sentence you will repeat unchanged when someone objects.

3. Put money behind experimentation, not just permission. AI has cut the cost of standing up a pilot low enough that a department can fund one out of its own discretionary budget instead of waiting a year for a line item. Name a person who can approve that spend without a committee, and put a decision date on the calendar beside their name. A review date lets everyone postpone without feeling like they said no; a name attached to a date forces a yes or a no.

I learned this winning recipe directly, working on measuring AI impact within the California State University system, the nation’s largest public university system and its largest AI deployment, 22 universities and more than 471,000 students. The CSU surveyed its own community in the fall of 2025, with more than 94,000 respondents across students, faculty and staff, the largest study of its kind in higher education. Ninety-five percent of respondents had already used an AI tool. Eighty-two percent of students called AI essential to their profession, and the same share worried about their job security.

Nobody assigned that adoption. It happened because individual people decided the wait was more dangerous than building ahead of policy. Academia has always produced pioneers first and permission second, and I have watched the same pattern repeat on every continent I have worked. The pressure AI puts on a university does not get absorbed by a task force. It gets absorbed by a person, or it does not get absorbed at all.

That is the whole argument of this series. The people best positioned to rebuild a university’s capability are already inside it — not a vendor, not a consultant brought in for a semester. Our job, as leaders, is to find them and connect them to each other before someone outside the building does it instead.

The institutions that survive this decade will not be the ones with the most AI tools installed. They will be the ones that kept producing people willing to build before they were told to.

I am collecting these stories for the rest of this series: the builders nobody has named yet. If that is you, or you know who it is on your campus, tell me. I want to hear it, and I am easy to find.