The Wrong Question About AI In Admissions

One of the most persistent questions enrollment leaders still ask about artificial intelligence is whether it will replace counselors. It comes up in strategy meetings, at conferences, and in quiet conversations between teams trying to understand what this moment means for the profession. It’s a fair question, but it’s also the wrong one.

The more relevant and urgent question is: What work are we asking counselors to do today that AI is already doing better and how can we redirect counselor time to the work only they can do?

When people worry about AI replacing humans, they’re usually imagining a future in which machines are having student conversations or making admissions decisions. But that’s not what’s actually happening. Not even close.

The real shift is subtler, and it’s happening right now. AI is not coming for human connection. It’s coming for the clutter.

In most admissions offices, the average counselor is spending far too much time doing things that are only tangentially related to recruitment. Hours are lost every week inside CRMs, sorting through records, updating call logs, building queries, and trying to make sense of static data exports. None of this is what counselors were hired to do, and none of it is where they’re most valuable. And yet, it’s where their time is going.

This is where AI, and specifically machine learning, has already started to reshape the admissions role. Not by replacing people, but by absorbing the administrative overhead that keeps them from doing what matters.

In the institutions we work with, AI is being used to surface high-priority students based on real engagement, not intuition. It’s identifying melt risk based on timing gaps and behavioral drop-off, not just demographics. It’s flagging students who are behaving like they’re interested but haven’t deposited—and those who have deposited but are starting to drift. None of this requires new staff. It requires a better way to interpret what’s already in the CRM.

The technology doesn’t guess who will enroll. It helps teams decide who to engage, when to engage them, and why that timing matters. That’s a strategic upgrade, not a staffing replacement.

This shift matters now more than ever, because the demands on enrollment teams keep increasing while the support structures around them stay flat. Counselors are working harder, but often less effectively, simply because there’s no system in place to direct their energy to the students who most need it that day. That’s not a personnel problem. That’s a prioritization problem.

And that’s where machine learning fits. It solves for focus.

As we approach NACAC and prepare for another cycle of innovation talk on the conference floor, this is the lens we recommend: focus less on what AI might do in the future, and more on what it’s already doing today in forward-looking admissions offices. The institutions getting ahead aren’t asking whether AI will take jobs. They’re asking how it can give time back to their teams and help them reach more students with less friction and more relevance.

If your team is still spending hours sorting spreadsheets or chasing incomplete checklists, the opportunity isn’t hypothetical. It’s operational.

We’ll be unpacking exactly that in our August video podcast: what AI is actually doing today inside enrollment offices, how it’s being implemented responsibly, and what to look for as more vendors add AI to their pitch decks. This isn’t a hype conversation. It’s a strategic one. And it’s one worth having before your team hits the road this fall.

Want to explore how machine learning is supporting counselor effectiveness—not replacing it? Join us on August 26 at 2 p.m. (ET) for the video podcast: crowdcast.io/c/vpjohn