For many admissions teams, FAFSA submissions have traditionally been viewed as a strong signal of student interest. However, in reality, not all FAFSA submissions indicate serious intent to enroll. As competition for students increases, it's critical to re-evaluate how FAFSA data is interpreted, especially in light of the information colleges can't access.

One important data element that has been affected is FAFSA Position, eliminated about a decade ago, which provided insights into where a student listed a particular college relative to others on their application. For years, many institutions used FAFSA Position to infer a student’s level of interest—assuming that being listed first or second meant the student was highly likely to enroll. While this wasn't a formal tool, the position was often treated as an important clue in the enrollment puzzle. However, due to concerns from the Department of Education about how colleges were using this information, FAFSA Position is no longer available. This change forces institutions to rely on other indicators of interest, and it underscores the need to diversify strategies for assessing commitment.

Why FAFSA Submissions Alone Don't Reflect Engagement

While none of us have assumed that every FAFSA submission equals similar levels of intent - this data blindspot has left us no alternative way of measuring this signal by student. Students now apply to more schools than ever before, and many submit FAFSAs to schools they’re considering only as “backup” options. Completing the FAFSA is simply a procedural step for many students, not a declaration of their commitment to attend a particular institution.

As a result, colleges that heavily weigh FAFSA submissions may end up focusing resources on students who have no serious intention of enrolling. Without the added layer of insight from FAFSA Position, schools are left with less context, making it more difficult to gauge genuine interest based on FAFSA data alone.

The Changing Landscape of College Decision-Making

The dynamics of student decision-making have evolved over the years, but FAFSA submissions are no longer a reliable indicator of where a student will eventually commit. In today’s environment, students submit multiple applications and FAFSA forms, often as a matter of precaution rather than as an expression of intent. In this climate, admissions teams need to be more cautious about assuming that FAFSA submissions equate to real interest.

Additionally, the inability to access FAFSA Position means schools can no longer know where they stand relative to other institutions. This lack of visibility into a student’s preferences highlights the growing importance of using additional methods—such as behavioral data, engagement metrics, and predictive modeling—to better understand and prioritize students who are truly interested in enrolling.

Prioritizing Engagement Over FAFSA Data

The shift away from relying on FAFSA Position presents an opportunity for admissions teams to reframe how they assess student intent. Rather than focusing on FAFSA submissions as a primary indicator, schools should shift their attention to other forms of student engagement. Metrics such as website visits, participation in virtual tours, attendance at campus events, and interactions with admissions officers can provide far more reliable insights into a student’s commitment to your institution.

In addition, colleges can leverage machine learning and predictive analytics to better understand how engagement behaviors correlate with enrollment decisions. By analyzing patterns in student interactions—such as the frequency of communication, the types of inquiries made, or the timing of their engagement—admissions teams can develop a more nuanced understanding of student intent. These insights help schools prioritize their outreach efforts toward students who are most likely to matriculate, rather than relying solely on the submission of financial aid forms.

Diversifying Your Approach to Student Commitment

The end of FAFSA Position data had forced an abandonment of nuance in interpreting the signal of FAFSA submissions. The rise of high-level data science now allows for a reassessment of how colleges interpret FAFSA submissions, making it clear that this form alone should not be the main driver of admissions decisions. While FAFSA remains an important part of the financial aid process, it is no longer a reliable indicator of student commitment. By focusing more on engagement behaviors and leveraging data-driven insights, admissions teams can make smarter, more informed decisions about how to allocate their resources and shape their admit pool.

In today’s competitive landscape, relying on a single metric—like FAFSA—can result in missed opportunities and misallocated efforts. Schools that embrace a broader, more holistic approach to understanding student commitment will be better equipped to build strong, resilient incoming classes.

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For years, admissions teams have counted on the FAFSA as a cornerstone of their financial aid and admissions strategy. With an October 1st release date set in stone, institutions relied on the steady flow of submissions to plan their outreach and shape their admit pool. However, this reliable timeline has now been disrupted, exposing a critical vulnerability for colleges that have been overly dependent on the FAFSA to gauge student interest. The FAFSA delay marks a second consecutive year where the expected cycle has been upended. This pattern should be a clear signal to admissions leaders: it's time to rethink how we manage our admit pools.

The Breakdown of FAFSA's Predictability

Historically, the FAFSA’s early October release provided a predictable starting point for assessing student commitment and financial needs. Schools built their outreach, financial aid processing, and admissions decisions around this key date, operating with a sense of security. But as we've seen with recent delays, the assumption that FAFSA timelines are fixed has become increasingly precarious. The December release date, following a similar delay last year, illustrates that colleges can no longer take for granted that FAFSA submissions will arrive when expected.

This shift forces admissions teams to question how much weight they place on FAFSA as a primary marker of student interest. If your strategy depends too heavily on the timing of FAFSA submissions, you risk missing critical engagement opportunities during the application season. Worse, by the time FAFSA data arrives, your institution may have lost touch with key students who moved on to other institutions that engaged them earlier.

The Risks of Relying on FAFSA Timelines

The delayed release of the FAFSA creates two specific risks for schools: missed engagement opportunities with genuinely interested students and misallocated resources on students who have no intent to enroll. Admissions teams have traditionally viewed FAFSA completion as an indication of student commitment—early filers were seen as more engaged and eager to secure their spot. This logic, however, breaks down in an environment where the FAFSA’s release is unpredictable.

Colleges that wait for FAFSA submissions to trigger their next round of outreach are likely to lose students in the interim, as the delay shortens the engagement window. Prospective students who are ready to make decisions in October and November will not have their FAFSA data processed, meaning schools will miss a key opportunity to connect with them at a critical point in their decision-making process. Furthermore, as more students submit FAFSAs late in the cycle, admissions teams may find themselves overloaded with financial aid applications that don't necessarily reflect serious interest. This leads to wasted time and effort pursuing students who have no plans to enroll, detracting from more meaningful engagement with likely candidates.

Engagement Over Aid Forms: What Really Matters

What’s becoming increasingly clear is that FAFSA submissions are no longer the most reliable indicator of student intent. Engagement—both before and after financial aid packages are delivered—offers a much clearer picture of a student’s likelihood to enroll.

Admissions teams should focus on engagement behaviors that reveal a student’s true interest in the institution. For example, how often are students visiting your website or interacting with your admissions team? Are they attending virtual events, campus tours, or responding to personalized outreach? These touchpoints provide valuable signals of interest long before a FAFSA is ever submitted. In a world where the FAFSA is delayed or unreliable, these engagement metrics become far more valuable in predicting yield than a financial aid form.

After the aid package is delivered, the way a student responds can also tell you a great deal about their intent to enroll. Are they asking follow-up questions, engaging with financial aid officers, or taking steps to confirm their spot? The strongest indicators of enrollment are often found in these post-aid interactions, not in whether a student simply submitted a FAFSA. Admissions teams that track and respond to this type of engagement data will be better positioned to shape their admit pool effectively, regardless of FAFSA timing.

Building a More Resilient Strategy

The unpredictability of FAFSA timelines is a wake-up call for colleges to diversify the metrics they rely on for managing their admit pool. Instead of waiting for FAFSA submissions, schools must pivot to a more flexible strategy that emphasizes early and ongoing student engagement.

Data-driven tools, such as machine learning models, can help admissions teams identify key engagement patterns and predict which students are most likely to enroll. These insights enable schools to tailor their outreach, focusing on students who show genuine interest through their behavior rather than simply relying on FAFSA submissions. Institutions that integrate this kind of predictive engagement strategy will not only improve their yield but also ensure they are focusing on the right students, even in a disrupted admissions cycle.

By shifting away from an over-reliance on FAFSA and toward a more comprehensive, engagement-driven approach, schools can better navigate the uncertainties of the admissions landscape. This shift will help colleges connect with the right students at the right time, ultimately leading to stronger classes and more consistent enrollment outcomes.

The disruptions in FAFSA release dates aren’t just logistical challenges; they represent a broader need for admissions teams to rethink how they shape their admit pools. Schools that continue to rely on FAFSA timing as the cornerstone of their strategy risk missing out on meaningful engagement opportunities. By focusing on student behavior and engagement—both before and after aid is delivered—admissions teams can build a more resilient, adaptive strategy that keeps them competitive in an ever-changing landscape.

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If there’s one takeaway from the AI buzz at NACAC, it’s that while robots aren’t going to be reading your transcripts or taking over your job anytime soon, they can certainly help make it easier. Let’s face it, AI is not the admissions counselor of the future—but it can be the best assistant you’ve ever had. Imagine having a tool that frees you up from guessing games, helps you focus on the right students, and gives you the confidence to approach each interaction with clarity.

But as we wrap up this series, let's look ahead: How will AI continue to reshape admissions? Spoiler alert—it’s not about robot overlords writing your acceptance letters. It’s about making your job more effective, efficient, and, dare we say, a little bit more enjoyable. Here’s how.

AI Enhances Your Expertise, Not Replaces It

At this point, we’ve debunked the myth that AI is coming to steal your job. Instead, AI is here to help you shine. Think of AI as your trusty sidekick, the Robin to your Batman. You’ve got the people skills, intuition, and experience—AI just gives you the data to back up your instincts. By analyzing student behavior and predicting outcomes, AI lets you make smarter decisions, faster.

Imagine knowing exactly when a student is ready to apply or needs a gentle nudge. Instead of wasting time on broad, generic communications, AI helps you focus on personalized, data-driven outreach that matters. You still get to build relationships with students; AI just makes sure you’re doing it at the right time with the right message.

A Future with More Fun, Less Guesswork

One of the greatest things about AI is that it takes the guesswork out of your day-to-day tasks. No more wondering whether a student is serious about applying or what message will resonate. AI has done the heavy lifting, analyzing behavior patterns, predicting next steps, and flagging the students who need your attention most. What does that mean for you? Less time staring at spreadsheets and more time doing the part of the job you love—actually connecting with students.

And let’s be real: AI can also spare you some of the more tedious parts of the job. Did a student suddenly go quiet after showing early interest? AI can tell you when to re-engage, so you're not left in the dark. It’s like having a crystal ball for admissions, except without all the mysterious fog.

The Human Touch Still Matters

As we look to the future, one thing is certain: the human touch will always matter in admissions. Sure, AI can help pinpoint student needs, predict behavior, and personalize outreach, but it can’t replace the empathy, understanding, and personal connection that admissions professionals bring to the table. AI doesn’t get excited about campus tours, and it won’t cheer when a student gets accepted—that’s all you.

AI gives you the insights, but you get to make the magic happen. The future of admissions is not about choosing between humans and machines—it’s about leveraging AI to help you be the best counselor you can be.

The Best of Both Worlds

So, what’s the future of AI in admissions? It’s not about replacing people—it’s about creating a seamless partnership where AI takes care of the data crunching, and you focus on what really matters: building relationships and supporting students as they navigate their educational journey.

In the end, AI isn’t about making things colder or more robotic; it’s about freeing up your time and making your job more human. The future of admissions is brighter (and more efficient) with AI by your side. So go ahead, embrace the partnership—and maybe even have a little fun while you’re at it.

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Introduction
In today’s competitive enrollment landscape, yield and retention hinge on data-driven precision. Traditional methods often fall short, relying on subjective judgment and missing critical engagement signals. enroll ml changes this by identifying behavior-action mismatches, helping admissions leaders spot opportunities and risks in real time for measurable impact.

Understanding Behavior-Action Mismatches
Behavior-action mismatches occur when engagement doesn’t align with commitment indicators, such as:

• High Engagement, No Commitment: High-potential students show interest but haven’t reached a commitment milestone, signaling a prime yield opportunity.
• Low Engagement, Commitment Met: Students with commitment indicators but low engagement, often an early indicator of melt risk.

Challenges with Traditional Methods
For many admissions teams, sifting through high application volumes and disconnected engagement data can feel overwhelming. Traditional models track disconnected behaviors that may correlate with enrollment but don’t provide enough depth to detect patterns. This often leads to delays or missed opportunities for timely intervention.

How enroll ml Closes the Gaps
enroll ml’s machine learning and Outcome Optimization Theory detect behavior-action mismatches in real time, transforming how admissions teams prioritize engagement:

• Multi-Marker Modeling: By analyzing an interconnected range of behaviors—engagement depth, timing, communication patterns—enroll ml precisely identifies high-potential, high-impact students.
• Automated Flagging and Prioritization: enroll ml flags behavior-action mismatches daily, eliminating time spent on manual record review. This prioritization empowers teams to re-engage at-risk students before melt occurs, turning data analysis into focused, real-time action.

Strategic Impact of Addressing Behavior-Action Mismatches
By automating prioritization, enroll ml can reduce counselor time spent on administrative tasks by up to 30%, freeing them to focus on high-impact engagement with top-yield prospects.

• Enhanced Yield: With immediate engagement, admissions teams lift yield by focusing on high-engagement students nearing commitment.
  • Case in Point: Within days of launch, Columbia College Missouri identified and converted high-potential students previously overlooked, directly boosting yield by X%.
• Reduced Melt: Proactive outreach to at-risk students with commitment indicators minimizes last-minute withdrawals.
  • Case in Point: Roosevelt University significantly reduced melt by re-engaging low-engagement depositors flagged by enroll ml, leading to X% decrease in summer melt.

Takeaway for Enrollment Leaders
Monitoring and responding to behavior-action mismatches directly impacts final yield outcomes. With enroll ml, admissions teams continuously identify actionable signals, strategically unlocking yield and reducing melt. Ready to turn data into yield? With enroll ml’s behavior-action insights, admissions leaders can unlock hidden yield potential and reduce melt, leading to X% gains in yield and X% reductions in time spent on administrative tasks.

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As admissions teams work through the challenges of identifying and engaging prospective students, the next step is figuring out how to approach each individual student. Many of the conversations around AI focus on its role in automating tasks, but one of its most exciting possibilities lies in helping human counselors make better, more informed decisions. Instead of scripting emails or pre-setting communication workflows, AI can act as a decision-support tool that guides counselors on the best way to reach each student.

The Human Touch Meets Data-Driven Insights

The key advantage of using AI in this context is its ability to blend data-driven insights with the counselor’s expertise and intuition. AI doesn’t replace human decision-making; it enhances it. For example, AI can analyze a student’s engagement data—such as how often they open emails, what content they interact with, and how quickly they respond to outreach—to provide counselors with insights on the student’s preferences, needs, and readiness to take the next step.

These insights give counselors a more complete understanding of each student’s profile, allowing them to make outreach more personalized and meaningful. Instead of guessing what message might resonate, counselors can be guided by the data, allowing them to focus on building authentic connections with students, which AI cannot replicate.

Personalizing Outreach for Maximum Impact

AI’s ability to help personalize outreach goes beyond generic recommendations like “send more emails” or “call at this time.” By analyzing behavioral patterns, AI can offer specific recommendations for each individual student, such as:

• Preferred Communication Channels: Some students might prefer email, while others are more likely to respond to text messages or phone calls. AI can analyze a student’s past interactions to suggest the most effective channel for outreach.
• Timing Recommendations: Based on a student’s activity history, AI can provide guidance on the best time to reach out. For example, if a student typically engages with your communications in the evening, AI can recommend sending a targeted message at that time for a higher likelihood of response.
• Tailored Messaging: AI can help counselors identify which topics are most likely to resonate with a particular student. If a student has shown significant interest in financial aid options or specific academic programs, the AI system can suggest focusing on those points in the next conversation.

Enhancing Counselor-Student Relationships

By acting as a decision-support tool, AI enables counselors to spend less time on administrative tasks and more time building relationships with students. Since AI handles the data analysis, counselors are freed up to focus on what they do best—listening to students, understanding their needs, and guiding them through the admissions process in a more personalized and empathetic way.

This approach also helps reduce the risk of “over-automation” in the admissions process. While automated emails and workflows can save time, they often lack the personal touch that students value in their interactions with an institution. With AI acting as a guide, counselors can strike the right balance between automation and human connection, ensuring that students feel seen, heard, and supported.

A Strategic Approach to Engagement

AI can also help admissions teams prioritize their efforts by identifying which students are most likely to benefit from personal outreach at a given time. For instance, if a student is showing signs of disengagement, AI can flag this and prompt a counselor to intervene with a more personalized message or offer additional support. On the flip side, students who are highly engaged and close to making a decision might benefit from a more direct conversation to help them finalize their plans.

AI as an Essential Partner for Admissions Counselors

AI’s role as a decision-support tool in admissions is one of its most powerful applications. Rather than replacing counselors or relying on generic automation, AI enhances the human touch by providing data-driven insights that lead to smarter, more effective outreach. By guiding counselors on how to communicate with students in a personalized and timely way, AI helps foster stronger connections and ultimately leads to better enrollment outcomes.

Next week, I’ll wrap up this series by looking ahead to how AI will continue to shape the future of admissions, combining human expertise with advanced technology for a truly transformative approach to student engagement.

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At NACAC, there was plenty of discussion around using AI to automate tasks, but few touched on AI's ability to track and predict where students are in their decision-making process. This is one of AI’s most powerful, yet underutilized, capabilities in admissions. Understanding what stage a student is at—whether they’re still exploring options, actively researching, or ready to apply—enables admissions teams to tailor their communications, making every interaction more meaningful.

The Challenge: Understanding Student Decision-Making

Admissions teams often rely on broad, linear models to track where students are in the funnel—prospect, inquiry, applicant, admit, and enrolled. While these categories are helpful, they don’t account for the complexity of individual student behavior. Some students may linger in the exploration phase for months, while others fast-track to applying after a single campus tour.

Knowing exactly where a student stands isn’t always clear with traditional methods. You might rely on surface-level cues—did they submit an inquiry form? Have they opened emails?—but these only provide part of the story. This is where AI can step in to give a clearer, more nuanced picture.

AI's Ability to Predict the Student Journey

AI excels at processing large amounts of data quickly and spotting patterns humans might miss. By analyzing behavior like how often a student visits your website, what pages they view, and when they open emails, AI can track subtle signals that indicate their progress through the decision-making process. AI models can even predict future behaviors, offering insights into where a student is headed and what kind of interaction they’ll need next.

Here are some ways AI can help map the student journey:

• Behavioral Analysis: AI looks at the frequency and nature of student interactions—such as visiting specific academic program pages or registering for events—to estimate how far along a student is in their journey.
• Predictive Staging: Based on past behaviors, AI can predict a student’s next steps. For example, if a student starts spending more time on financial aid or housing pages, AI might suggest they’re getting ready to make a decision, prompting your admissions team to send a personalized outreach.
• Engagement Milestones: AI helps track when students hit certain engagement milestones, like signing up for an info session or downloading an admissions brochure. These milestones help admissions teams know when it’s time to switch from general engagement to more specific, personalized communication.

Why Predicting Decision Stages Matters

Understanding where a student is in their journey allows admissions teams to deliver the right message at the right time. A student still exploring their options might need a broad overview of campus life, while a student deep into the decision phase might appreciate personalized insights into scholarships or financial aid opportunities.

Instead of bombarding every student with the same set of emails or phone calls, AI enables admissions teams to focus their efforts more strategically. This targeted approach can make the student experience feel more personal and relevant, which in turn increases the chances of them moving forward in the process.

A More Tailored Approach to Engagement

AI doesn't just help predict where students are—it also helps you communicate with them more effectively. A student in the early stages of exploration likely won’t respond well to an email asking them to apply now, but they might engage with content highlighting the academic programs or campus life they’re interested in. Conversely, a student nearing the end of their decision-making process will likely appreciate direct guidance on deadlines, financial aid, or next steps for enrollment.

By meeting students where they are, you’re not just improving your chances of conversion—you’re improving their experience with your institution. A more tailored, relevant approach to engagement shows students that you understand their needs and are there to support them throughout their journey.

AI and the Power of Predicting Student Behavior

AI’s ability to map and predict the student decision journey is a game-changer for admissions teams. By analyzing behavior patterns and engagement milestones, AI provides invaluable insights into how students are progressing through the funnel. This allows for more personalized and strategic communication, ultimately improving the chances of turning prospects into enrolled students.

Next week, I’ll explore how AI can help admissions teams craft the perfect approach for each individual student, providing guidance on the best way to engage based on their unique profile and preferences.

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At the NACAC conference, AI was all the rage, but most of the conversation focused on automating tasks like processing applications or sending emails. While automation is helpful, there’s a bigger opportunity being overlooked: AI’s ability to help admissions teams figure out which students to engage with and when. That’s where its real value lies.

The Challenge: Prioritizing the Right Students

In any admissions cycle, you’re often inundated with student inquiries, applications, and expressions of interest. From the student who’s just starting to explore your school to the one on the verge of applying, how do you know who deserves your attention? Traditional methods, such as prioritizing based on GPA or test scores, don’t always give you the complete picture of a student's potential to enroll. Moreover, these methods often miss the nuance of where students are in their decision-making process.

That’s where AI steps in—not to replace your expertise, but to help you focus your efforts. By analyzing student behavior—like how often a student visits your website, what they click on, and how they interact with your communications—AI can predict who’s ready to engage and who might need more nurturing. This saves your admissions team time and allows for more personalized outreach.

How AI Identifies Key Prospects

AI does more than crunch numbers. It spots patterns in student behavior and engagement that humans might miss. For example, AI can track not just whether a student opens an email but how often they return to your website afterward, how much time they spend on specific program pages, and whether they sign up for a virtual tour or webinar. All these actions are indicators of interest, but they also provide a deeper sense of where the student is in their decision journey.

Here are some specific ways AI can help you identify key prospects:

• Engagement Scoring: AI assigns scores to students based on how engaged they are with your institution. A student who clicks on multiple emails, revisits the website, and signs up for events will score higher, indicating they may be ready for more focused outreach.
• Behavior Tracking: AI can look beyond simple interactions and assess deeper behavior patterns. For example, a student who consistently asks about specific programs or scholarships is likely more serious about enrolling than one who’s only browsing general information.
• Predictive Timing: AI can help admissions teams understand when students are most likely to respond to communication. Maybe a student opens every email in the early morning—AI will track that and suggest sending future emails during that window.

Smart Outreach: Prioritizing for Impact

One of AI’s biggest advantages is helping admissions teams focus on the right students at the right time. Instead of casting a wide net with mass communications, AI allows for targeted engagement. For instance, students who show high levels of engagement can be flagged for immediate follow-up, while those showing lower engagement can be put into nurturing campaigns to keep them warm without wasting time on direct outreach.

Some practical benefits of AI in outreach include:

• Optimizing Time and Resources: AI helps admissions counselors prioritize high-value students, freeing up time to focus on those most likely to apply and enroll.
• Personalized Communication: By tracking the specific needs and interests of students, AI helps you craft messages that speak directly to their unique situations, leading to more meaningful interactions.
• Intervention Before Drop-Off: AI doesn’t just highlight students who are ready to engage; it can also flag those who are disengaging, allowing for proactive outreach to keep them in the funnel.

Rethinking Your Engagement Strategy

With AI’s insights, admissions leaders can uncover students who might not have been flagged based on traditional criteria. Maybe they don’t have the highest GPA, but their behavior shows a deep interest in your programs. By digging into these hidden signals, you’re not only broadening your pool of high-potential students but also making your engagement strategy more inclusive.

This kind of data-driven, personalized approach helps ensure that you’re not missing out on great prospects simply because they didn’t meet the standard academic criteria.

AI’s Role in Identifying Key Prospects

AI is transforming the way admissions teams engage with prospective students by helping to identify who to focus on and when to reach out. With a data-driven understanding of student behavior, you can personalize your outreach and maximize the impact of your efforts.

Next week, I'll explore how AI can help map the entire student journey, allowing admissions teams to tailor their communications even more effectively.

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After attending the NACAC conference, it’s clear that AI is a hot topic in admissions. Session after session showcased AI’s potential to automate tasks like reading transcripts and sending mass emails. But that conversation misses a critical point: AI’s real power is in guiding human decisions, not just replacing human tasks.

While automation has its place, the true value of AI in admissions lies in its ability to help us understand which students need attention, where they are in their decision-making journey, and how best to approach them. AI can analyze patterns in behavior and engagement, providing admissions teams with a data-driven way to make smarter decisions.

Rather than scripting emails or processing applications, AI can empower counselors with insights on when and how to engage with individual students. It gives humans the tools to personalize their outreach, make timely decisions, and enhance relationships—all with greater confidence.

The future of AI in admissions isn’t about turning the work over to machines. It’s about using machine learning to make better, more informed human decisions. By expanding the AI conversation beyond automation, admissions leaders can unlock new opportunities to connect with the right students at the right time, ultimately improving enrollment outcomes.

Next week, I’ll give examples of how AI can help you pinpoint which students to focus on for more impactful engagement.

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