What Makes AI Work In Admissions

There’s no shortage of tools today that claim to “use AI.” That phrase appears in product one-pagers, conference booths, and sales decks with increasing frequency. But when it comes to enrollment strategy, it’s worth asking a harder question: does the AI actually work?

For most admissions teams, the answer isn’t about the novelty of the technology. It’s about whether it actually helps their counselors have better conversations with students. If it doesn’t do that, the rest is just packaging.

In many cases, the issue isn’t that AI is being oversold. It’s that it’s being misunderstood. Enrollment leaders are being offered tools that predict things, calculate intent scores, or send automated reminders—but very few of those tools actually improve yield. And even fewer help teams reallocate time in a meaningful way.

What makes AI useful in enrollment isn’t how advanced the model is. It’s whether it drives the right action at the right time.

For example, most predictive models generate a static enrollment probability. This might seem helpful in theory, but in practice, it often misses the most important variable: time. A student who scored 80 last week but hasn’t engaged since might no longer be a priority. Another student who scored 65 two weeks ago but has recently submitted a form, clicked a link, and registered for a virtual event? That’s a different story.

In a rapidly moving funnel, behavior is more important than probability. And timing is more important than profile.

That’s why machine learning models built specifically for enrollment need to do more than score. They need to observe student behaviors over time, measure the gaps between actions, and surface patterns that indicate momentum, hesitation, or drift. It’s not enough to know who a student is. You need to know what they’re doing—and how that behavior is changing.

This kind of AI doesn’t just look at “who clicked” or “who opened.” It examines how long it took them to act. It flags mismatches between expected engagement and actual behavior. It doesn’t just tell you who’s likely to enroll. It helps you decide who needs attention today.

That distinction matters. Because what many teams still lack isn’t data—it’s prioritization.

Every admissions team has felt the pressure of a growing application pool and a finite number of hours. And every director has seen counselors spend far too much time digging through exports or working off static lists that don’t adapt to changing behavior. Those lists may be built with good intentions, but they’re almost always built with outdated information. And in a student decision cycle that changes by the week, that lag can be the difference between a conversation and a missed opportunity.

That’s where effective AI delivers value: not by predicting enrollment in the abstract, but by guiding outreach in the present. When a counselor starts their day with a prioritized list of students, based on real-time behavioral scoring, their time gets more strategic. And when they can intervene at moments of real student uncertainty—before the drift becomes melt—that work has impact.

On the other hand, AI that doesn’t adapt quickly, or that isn’t built around behavioral signals, tends to fall short. Lead scores that don’t update, intent models that ignore timing, or dashboards that require manual analysis all miss the point. They may technically use machine learning. But they don’t help admissions teams work more precisely.

As you head toward NACAC and start hearing AI mentioned across every vendor booth, it helps to bring a few filters with you. If a tool claims to be “AI-powered,” ask how it prioritizes. Ask what data it uses. Ask how often that data updates. Most importantly, ask whether it helps your counselors talk to the right students, at the right time, for the right reason.

Because AI in enrollment isn’t valuable just because it exists. It’s valuable when it improves the decisions that shape a class.

To learn more about how machine learning is actually reshaping fall recruitment, join our August 26 video podcast: crowdcast.io/c/vpjohn