Justin May Have a Point: ideas on higher ed, AI, leadership, human behavior and user experience

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  • What Admissions Functions Should Be Automated, and What Must Remain Human?

    Originally published by Advancing Admissions.

    AI has accelerated our thirst for information while shrinking our patience for getting it. We love using AI to research, compare, and make decisions. But something interesting happens when we find ourselves on the receiving end of an AI driven process. Suddenly, we are not quite as enthusiastic.

    This is especially true for major purchases, where trust, emotion, and human connection influence our decisions.

    The fundamentals of why people buy have not changed. We buy emotionally and justify logically. AI does a great job helping with the latter. But the former? That still requires human connection.

    As AI becomes embedded in nearly every industry, leaders face an important question: What should we automate, and what must remain human?

    I wrestled with this question a few years ago as AI began making its way into enrollment management. Like many leaders, I saw big opportunities to improve efficiency, accelerate response times, and do more with less.

    Lots of mistakes were made.

    In my pursuit of efficiency, I automated too much and removed too much of the human element from our early recruitment processes. We became better at processing prospective students, but not necessarily better at building relationships with them.

    That experience forced a hard reset in how I approached automation.

    Just because something can be automated does not mean it should be.

    I also learned that not all friction is bad. Some friction creates opportunities for meaningful conversations, reinforces commitment, and helps students make informed decisions. Remove too much, and you risk inflating your applicant pool while enrollment yield declines.

    More applications looks impressive on a dashboard, but only enrolled students pay tuition.

    Start by Becoming Your Own Prospective Student

    Before deciding what to automate, experience your institution through the eyes of a student.

    Submit inquiry forms and applications that reflect the different personas you serve. See how long it takes to receive a call or personal email. Follow the communication journeys. Are the messages timely and relevant? Are they conversational, or filled with institutional jargon?

    More importantly, ask yourself: Are we communicating what students want to hear, or what our institution thinks is important?

    I am confident you will be surprised by what you discover.

    Once you identify the friction, distinguish between what creates unnecessary work and what creates meaningful engagement. Automate repetitive administrative tasks, routine questions, status updates, and processes that help students move forward without waiting on someone.

    The moments that require empathy, reassurance, and persuasion should be saved for humans.

    Once complete, test, test, and test again… and when you think you are done, test a little more. Think of your AI agents as new employees. They need proper onboarding, training, and ongoing feedback to become efficient, so be sure to build enough time into the process.

    When AI operates independently, it becomes part of your institution’s identity. Every interaction shapes how a prospective student perceives your college. Leaders should be just as concerned about the quality of those interactions as they are about the efficiencies they create.

    Speed Matters, but So Does the Signal It Sends

    How quickly we respond, and how we choose to respond, communicates how much we value the relationship.

    Consider a student who submits an inquiry or application. A personal call that same day signals that their interest matters and that someone is ready to help them take the next step.

    An automated email or text might provide the same information, but it does not communicate the same level of investment.

    Technology can acknowledge an action, but humans make that action feel important.

    In a competitive enrollment environment, this is HUGE.

    The Human Sandwich: Human. AI. Human.

    Out of these lessons came the Human Sandwich framework. The concept is simple: humans start the relationship, AI keeps momentum going, and humans step in when they can make an impact.

    The first human interaction establishes the relationship. Early conversations help us understand a student’s motivations, concerns, and aspirations. This creates a foundation that technology can build upon, rather than attempt to replace.

    AI manages the middle through a Next Best Action framework. Instead of pushing every student through the same predetermined communication sequence, AI evaluates student behavior, engagement, and progress to determine what should happen next, when it should happen, and whether AI or a human should deliver it.

    The last human element is triggered by the moment, not the enrollment stage. AI recognizes when a conversation, reassurance, or intervention could make a difference and brings the right person into the conversation.

    From Funnel Management to Momentum Management

    At its core, the Human Sandwich is about momentum management. An object in motion stays in motion, unless something interrupts progress.

    Motivation is fleeting, and students experience it at different moments. A student might be excited about college on Monday and overwhelmed by the cost on Wednesday. Our enrollment processes should be equipped to recognize these shifts and respond accordingly.

    Consider a student who has been sitting in an admitted status for several weeks. Our traditional response? Send another reminder to “secure your spot” or “complete your next steps.”

    What if the student is not ready to move forward because something else is holding them back?

    Imagine that same student has opened a financial aid email three times and clicked on the net price calculator. Those signals might suggest the student is concerned about affordability and trying to understand how to pay for college.

    The next best action is not another deposit reminder. It might be a conversation with someone who can help the student understand their financial options.

    That is momentum management.

    This is just one example of hundreds of situations prospective students in your enrollment funnel may be navigating at this very moment.

    We often mistake inactivity for a lack of interest when it may be a sign of uncertainty.

    Sending a bunch of transactional emails outlining the steps to enroll will accomplish little if we have not addressed what is preventing the student from moving forward.

    AI can help us identify those signals at scale while humans can provide the connection and reassurance needed to address them.

    Rethinking the Role of AI in Admissions

    The real opportunity with AI is not reducing the number of people involved in recruitment. It is allowing our people to spend more time doing what technology cannot: building trust, creating belonging, and helping students make decisions that can change their lives.

    For years, we have designed enrollment processes around stages, communication calendars, and institutional deadlines. We tell students what they need to do next and assume that providing the information is enough to move them forward.

    Students do not experience college enrollment as a series of CRM stages. They experience it as a series of decisions, questions, obstacles, and moments of uncertainty.

    AI gives us an opportunity to redesign that experience around the student rather than the institution.

    The question now is whether each interaction is helping a student move closer to a confident decision.

    The future of enrollment management depends on our ability to understand why students stall and address the barriers preventing them from moving forward.

    As you shop your existing processes, the next challenge is deciding what stays, what gets automated, and what should go away entirely.

    Activity Audit Matrix

    As you shop your existing processes, take a hard look at how your admissions team spends its time. An Activity Audit Matrix can help identify which activities belong to your counselors, which are better suited for AI, and which may no longer serve a purpose.

    We have spent decades building enrollment processes around the limitations of our technology, staffing, and resources. Those limitations are changing, giving us an opportunity to rethink how we engage students, remove friction, and respond to their needs in real time.

    Now comes the harder part: challenging the way we have always done things and having the willingness to redesign them.

    High relationship · High volume

    Human Priority

    • First-gen family calls
    • Scholarship conversations
    • Decision-point check-ins
    • Withdrawal risk conversations

    High relationship · Low volume

    Human + AI

    • Campus visit follow-ups
    • Scholarship interview prep
    • Long-cycle lead nurture conversations
    • High-stakes but infrequent

    Low relationship · High volume

    AI Priority

    • Deadline reminders
    • Status updates
    • FAQ responses
    • Application confirmations
    • Transcript nudges
    • Missing document alerts
    • FAFSA reminders

    Low relationship · Low volume

    Evaluate or Eliminate

    • Mass generic outreach with no segmentation
    • Activities nobody has questioned in years
    • Little evidence of impact

    Activity Audit Matrix: a framework for deciding which admissions activities should remain human, be supported by AI, be automated, or be eliminated. Rows show how much the activity depends on trust, judgment, and empathy. Columns show how often it happens.

    AI Gave Your Team Time Back. Now What?

    One of the harder lessons I learned early in our AI journey was that automating work created an unexpected challenge. Activities that once consumed a large portion of our staff’s day were suddenly being handled by AI, leaving gaps in routines and workflows that had been established over years.

    Here is where institutions need to be intentional. Without redefining expectations, responsibilities, and performance measures, the return on that investment can quickly disappear. People naturally gravitate toward what they know, and simply giving staff more time does not guarantee that time will be spent on activities that produce better results.

    AI creates leverage, but realizing its value requires us to rethink the roles of the people whose work it replaces. That means redirecting their capacity toward higher value activities such as expanding recruitment territories, developing relationships, having meaningful conversion conversations, addressing objections, and helping students overcome barriers to enrollment.

    Our KPIs need to evolve alongside those responsibilities. Instead of emphasizing the number of calls made, emails sent, or follow-ups completed, we should place greater weight on meaningful student engagement, conversion rates, territory growth, application and enrollment yield, and the successful resolution of barriers that prevent students from moving forward.

  • The signals are already there. Are you listening?

    A single email open tells you almost nothing. Three quiet signals at once tell you a student is about to slip away.

    I joined Dr. Paul Orscheln of Park University, Tom Nesbitt of Clinton Community College, and Carolina Recchi of EdSights for a panel called Beyond Traditional Models: New Signals for Driving Student Connection and Yield. Here is what stuck with me.

    Context beats any single signal

    My team does not react to one data point. We look for patterns: a student moving through stages much slower than their peers, or someone opening every financial aid email but never taking the next step. When a stalled application, falling engagement, and an unanswered question show up together, that is when we reach out. The goal is to get to the why, so the conversation can be personal instead of generic.

    The students who look fine sometimes are not

    Some of the students we worried about least had strong grades and good attendance. When we actually listened, we learned that campus life had been built around residential students. Commuters felt like guests at someone else’s party. Nothing in the standard dashboards flagged it. Students told us, once we gave them an easy, low pressure way to say it.

    Technology speeds up whatever process you give it, broken ones included.

    Where AI actually helps

    The real wins are practical. AI can spot patterns across caseloads that no single counselor could track, and it takes administrative work off people’s plates so they can spend their time on conversations that matter. But it has to plug into a process you have already defined. Relationships still come from people.

    One thing to try in the next 90 days

    Add one question to your application asking what worries or motivates the student. It costs almost nothing, and your team walks into every conversation already knowing what matters.

    Read the full EdSights recap and watch more clips →

  • Speed, simplicity, and storytelling: what higher ed can steal from startups

    The colleges that innovate best rarely have the biggest budgets. They have better habits: they move fast, keep things simple, and tell a story worth hearing.

    I recently shared three principles borrowed from the startup world that can change how institutions recruit, engage, and keep students. None of them require a big check. All of them require a willingness to remove friction, test quickly, and communicate with purpose.

    1. Speed

    Try something new, measure it, and adopt what works without waiting for the perfect plan. Speed also sends a message. When a student gets a fast answer, they feel valued. When they wait, they start shopping elsewhere.

    2. Simplicity

    Design every process from the student’s side of the screen. Simplify pathways, cut steps, and say things plainly. Every minute you save a student shows up later as satisfaction, engagement, and referrals.

    3. Storytelling

    People choose a place where they can see themselves. Real student journeys, programs tied to purpose, faculty who love what they teach, and communities where students belong make an institution memorable, and memorable is what gets chosen.

    Small, mission driven colleges have an advantage big institutions envy: they can move faster.

    The same three habits work far beyond higher education. Any organization that sells, serves, or needs people to stay can move faster, get simpler, and tell a better story.

    Read the full feature on openPR →

  • From 117 days to instant: what happened when we stopped fighting our own software

    The average student waited 117 days to move through our pipeline. Everything was manual. Now a qualified applicant can be admitted in moments.

    Element451 recently published a customer story on how Richard Bland College rebuilt its admissions operation. Our old system had been customized for us, but it was clunky and working against us. Counselors were buried in paperwork when they should have been in front of students.

    What changed

    80%

    less time spent reviewing applications

    1,000+

    high school visits this year, up from 850

    Minutes

    for tasks that used to take 15 to 30 minutes per student

    We moved a part time processing role into strategic work and pointed our counselors back at the job they were hired for: recruiting.

    The lesson beyond admissions

    Speed is a form of customer service. Every day a person waits for an answer is a day they can talk themselves out of saying yes. Plenty of organizations with a strong offer still lose people to a slow process, and nobody notices because the delay is spread across a dozen small handoffs.

    My north star is simple: give students the best experience possible.

    We are now extending the same approach to Student Success, Residence Life, and Student Accounts.

    Read the full Element451 customer story →

  • Featured: The State of AI in Student Accounts

    Three quarters of staff are already using AI. Almost none of them have permission.

    That is one of the headline findings in Meadow’s 2026 report, The State of AI in Student Accounts, which surveyed 147 professionals who handle tuition billing, payments, collections, and the endless stream of student questions about all three. I was one of the practitioners interviewed for the report.

    What the data says

    • Shadow AI is everywhere. 76% use AI for their own work, but only 14% have department endorsed tools.
    • Privacy is the top worry. 78% name FERPA and data privacy as their leading concern.
    • The wins are unglamorous. Drafting replies to student questions is the most common use, at 46%.
    • Leadership is mostly silent. 63% report no pressure from leaders to adopt or even plan for AI.

    My take

    Admissions was first through the door. Generative tools came first, then more agentic processes, and once the results were visible other offices started paying attention. I expect that momentum to reach the financial side of the house. It is a slow play, but it moves in one direction.

    The trust bar is higher there. But trust gets built the same way it did in admissions: through visible results that people can see and verify for themselves.

    Justin May, in the report

    The bigger lesson applies well beyond student accounts. When your people are already using a tool without permission, the real question is whether you will lead its adoption or let it happen to you.

  • Beyond Incrementalism: why most AI projects produce a digital version of a bad process

    The tools work. The studies prove it. So why do most AI initiatives fail to change anything?

    That question sits at the center of Beyond Incrementalism: Navigating Organizational Change, Behavioral Inertia, and Human Fear in AI-Driven Innovation, a paper I co-authored with Ken Russell, Joe Wilck, Scott Gerard, John Hoag, Becky Scott, John Harrison, and Andrew Sohn through the Barton College Data Analytics Consortium.

    The paper draws on sixteen sessions of the consortium, a group of educators and practitioners from higher education, data science consulting, and industrial engineering, read alongside the research on change management and AI productivity. Its argument is simple and uncomfortable: the biggest bottleneck is organizational. Human behavior resists change, and that resistance outweighs any budget or technical hurdle.

    The ideas worth stealing

    • The incrementalism trap. Organizations swing between tiny tweaks and moonshots. The real value lives in the middle, where most people cannot imagine things.
    • Nine sources of resistance. The paper sorts them into three families: threat based, principled, and experiential. Not all pushback is fear, and treating it all the same backfires.
    • The fluency trap. Polished AI output looks so confident that people stop checking it.
    • Data literacy comes first. Many organizations are not ready for AI because they were never ready for their own data.

    If you digitize a crappy process, you now have a digital crappy process.

    Scott Gerard

    A three step path forward

    1. Fix the process first. If you would not be proud of the manual version, do not automate it.
    2. Commit to scaling, not downsizing. People will not help build a tool they believe will replace them.
    3. Run low stakes, high visibility pilots. Seeing is believing, and small wins in public do more than big promises in private.

    This is the same pattern I see in service, sales, and retention every day. The hardest work lives in the habits, the fears, and the processes nobody has questioned in years.