Arrival, Not Adoption: What the Distinction Actually Demands of Leaders
Part 2 of 2
A follow-up to "This Is Not an Adoption Technology: Reflections from the AASCU Symposium for Provosts"
Yesterday I shared reflections from the AASCU Symposium for Provosts, including a framing from Erin Mote of InnovateEDU that I have not been able to stop thinking about: AI is not an adoption technology. It is an arrival technology.
Today I want to unpack what that distinction actually means, and what it demands of the leaders who accept it.
What Is an Arrival Technology?
An arrival technology is one that has already changed the conditions your institution operates in, regardless of whether you have formally engaged with it.
With an adoption technology, the sequence goes: you become aware of it, you evaluate it, you decide whether and how to bring it in. Your engagement is the activation point. Nothing fundamental changes at your institution until you choose to act.
Arrival is different. An arrival technology reshapes the environment before you decide anything. Your students are already using it, often more fluently than your faculty or staff. Your accreditors are developing positions on it. Your peer institutions are investing in it. Employers are beginning to expect graduates to have navigated it. The vendors have already mapped your procurement process. The question is no longer whether AI affects your institution. It is whether you are shaping how.
This distinction matters because adoption gives you a kind of permission structure that arrival does not. Adoption says: move carefully, build consensus, run a pilot, wait and see. There is an "if" in adoption, or at minimum a "when," and the "when" is yours to control.
Arrival removes that permission. It does not mean there is nothing to decide. It means the decision is no longer whether to engage. It is what kind of institution you are going to be in this moment, and whether your people will feel led through it or left to navigate it alone.
But Arrival Does Not Make the Human Work Optional
Here is where I want to push back gently on one possible reading of the arrival framing.
Arrival does not mean urgency overrides everything else. It does not mean you sprint past the people who need to be brought along. In fact, accepted carelessly, the arrival framing can become its own kind of pressure, one more force pushing institutions to move faster than their faculty, staff, and students can meaningfully absorb.
The honest reality is that the pace of this moment is not entirely in your control. Competitive pressure, student expectations, accreditor guidance, vendor timelines, and funding cycles are all moving whether or not your institution is ready. That is precisely what makes it an arrival technology.
But the human work, the acknowledgment of fear, the communication of vision, the co-creation of direction with the people who will have to live inside it, is not something you can defer until the pace slows down. It has to happen alongside the speed, not after it. Leaders who move fast and bring their people along are in a fundamentally different position than leaders who move fast and leave their people behind.
The goal is not to slow down. It is to lead in a way that people can follow.
What Arrival Actually Demands
If you accept the arrival framing, a few things follow.
Widen the aperture. Our research at T3 consistently shows that AI adoption is concentrated where barriers are lowest, not where the value is highest. Teaching and learning gets most of the attention. Financial aid, operations, HR, and student-facing staff roles rarely come up in the AI conversation, and yet these are often where the highest-value opportunities and the most significant risks live. A provost operating with an arrival mindset is not asking "where are we piloting AI?" They are asking "where is AI already operating in our institution, and who is accountable for how it is being used?"
Name the principles before the policy. Institutions that try to govern AI by writing comprehensive policy first tend to produce documents that are outdated before they are implemented. What works better is establishing a clear set of values and guardrails: what AI is for at your institution, what it is not for, and what principles guide decision-making when the answer is not obvious. This is faster to produce, easier to communicate, and more durable over time.
Treat students as partners, not subjects. The student AI ambassadors from Coppin State University who joined us at the AASCU symposium were not there to be showcased. They were there because they have something to contribute. Students at regional public universities are already living inside the moment that institutions are still strategizing about. That is an asset, if leaders are willing to use it.
Ask for what the moment actually requires. As I noted yesterday, resource constraints are among the most significant barriers to progress at regional public universities, and significant pools of funding are currently available for institutions willing to make the case. Arrival-sized problems require arrival-sized asks: not pilot programs, but sustained infrastructure. Not one-time workshops, but funded leadership capacity. Not a task force, but a governance structure with real authority and real resources behind it.
Bring your people along at the pace the situation demands, not the pace that is comfortable. This is the hardest part. The goal is not to wait until everyone is ready, because that moment will not come. The goal is to move with enough transparency, enough communication, and enough genuine inclusion that the people who are not yet ready can see where you are going and trust that you are not leaving them behind.
The Question Underneath All of This
At its core, the adoption-versus-arrival distinction is not really about technology. It is about whether your institution is in a reactive posture or a generative one.
Adoption institutions are waiting to see how things shake out. Arrival institutions are deciding what kind of place they want to be in this moment, and building toward that.
Regional public universities, the ones that showed up in that room in Washington, are not institutions that can afford a reactive posture. Their students cannot afford it. And the good news is that the resources, the community, and the partnerships to support a more generative approach are more available right now than many leaders realize.
The technology has arrived. The question is what you are going to build with it.
T3 Advisory's full research on AI adoption in higher education, including our AI Adoption Rubric, survey toolkit, and implementation guides, is available free at t3advisory.com. Read Part 1 of this series here: [link]