Agentic AI is getting smarter. The question is whether higher education knows what to ask of it.
Every new wave of technology seems to arrive with the same basic promise: better tools, better information, better decisions. But if the last couple of decades have taught us anything, it is that information usually is not the hardest part. Judgment is. I was reminded of that during a cabinet meeting in the middle of COVID, when all of us were gathered in one of our largest rooms, wearing masks and staying six feet apart, and our president asked me, “Tom, what are we planning to do with analytics?” It was a fair question for the moment, but also one I had heard before in different clothes. In the early 2000s, it was Business Intelligence. Then it became analytics. Now it is AI, and increasingly agentic AI. The terms change. The tools get smarter. But the real challenge remains stubbornly the same: not whether institutions can get answers, but whether their leaders know what questions matter most. As AI lowers the barrier to inquiry and makes it easier to explore across multiple systems in plain language, the competitive advantage will not go to the institutions with the flashiest tools, but to the ones whose leaders understand the real drivers of their business well enough to ask better questions.
A big part of that challenge is that higher education likes to talk about AI as if institutions are all trying to solve the same problems. They are not. Stanford and Michigan are not asking the same questions as a public regional trying to stabilize enrollment, or a small private liberal arts college trying to protect its identity while keeping the lights on. Their missions may overlap in broad ways, but their operating realities, economic pressures, and strategic drivers differ significantly. That matters because the value of AI will not come from giving every institution the same shiny set of agents. It will come from how well leaders understand the specific problems most worth solving in their own environment.
That raises a more practical question than most vendor demos ever will: if your institution really had a capable genie in the bottle, what would you ask it to do? Not in the abstract, and not in the language of product marketing, but in terms of the few things that would actually move the needle. Would you ask it to help you better understand which students are most at risk of leaving? To reduce the administrative drag that slows research or drains staff time? To connect enrollment, aid, retention, and financial sustainability in ways that are harder to see today? That is where this gets real. The real promise of agentic AI is not just that it can answer more questions. It is that leaders may finally be able to explore the ones that matter most, more directly and more often. And if that is true, then the next question becomes a simple one: what are your three wishes?
The answers, of course, should not be the same. A high-profile R1, a public regional or less prominent research institution, a private liberal arts college, and a community college do not wake up each morning facing the same pressures, economics, or definitions of success. So if agentic AI is the genie, the wishes ought to be very different, too.
For high-profile R1s, one wish may be to reduce the administrative drag surrounding research, grants, compliance, and other complex workflows that consume enormous time but do little to advance the mission itself. Another may be to connect information across large, decentralized environments so leaders can see patterns earlier, act with more confidence, and avoid letting scale become its own form of friction.
For public regionals and less prominent research institutions, the wishes may be more directly tied to enrollment stability, student persistence, program demand, and financial resilience. These institutions often need clearer visibility into which students are most at risk of stopping out, which programs are creating real value, and where limited resources can be deployed to have the greatest impact. Their questions are no less strategic. They are simply being asked under tighter constraints.
For private liberal arts colleges, the wishes may revolve around connecting enrollment, discounting, retention, student experience, and long-term sustainability in ways that are often discussed separately but lived together. These institutions are frequently trying to preserve a distinctive identity while confronting hard economic realities. For them, the value of AI may lie less in scale and more in helping leadership see the interdependence of choices before small tradeoffs become much bigger problems.
And for community colleges, the wishes may be even more immediate and mission-centered: how to better support access, progression, transfer, workforce alignment, and student success for populations whose lives are often far more complex than traditional institutional models assume. Community colleges sit at the intersection of education, local labor markets, and public service. The questions they need answered are often less about prestige and more about practical outcomes, responsiveness, and removing friction for students who have very little margin for error.
This is also where the conversation shifts from technology to leadership. For years, one of the practical limits on inquiry was friction. If a leader wanted an answer, someone in IT, IR, or another administrative office usually had to go find the data, run the report, interpret the output, and bring it back. As AI reduces that friction, leaders will be able to ask more questions, more quickly, and across more systems. That is powerful. But it also means institutional self-awareness matters even more. AI may democratize access to answers, but it does not democratize judgment.
A smarter genie will not rescue an institution from fuzzy priorities. In fact, it may expose them. If leaders do not understand what drives enrollment, retention, student success, research productivity, operating efficiency, or financial sustainability in their own context, then more powerful agents may simply produce faster noise. The opportunity here is real, but so is the discipline it requires. The art will not be in buying the magic lamp. It will be in knowing what to wish for.
That is why the competitive advantage in agentic AI is unlikely to come from the technology alone. It will come from leadership teams that understand their institution well enough to aim that technology at the questions that matter most. Same genie. Better wishes.
Before you go shopping for a smarter genie: what are your three wishes?
Originally posted by Thomas Battaglia on LinkedIn. Be sure to follow him there to catch all his great industry insights.
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