Dear Sir or Madam, I am writing to share my thoughts on the changes in higher education, particularly the shift to precision enrollment and the use of AI. Also, I want to discuss the challenges of transferring credits for students who have attended multiple institutions and share my belief that institutions need to redefine their relationship with students. #1. Precision Enrollment There has been a major change in the way institutions recruit students. We are moving away from high-volume recruiting to precision enrollment. This change has occurred due in large part to the "demographic cliff" that we will be facing in 2026, which will force institutions to focus on the adult learner as well as the some-college-no-degree population. Therefore, the strategy has shifted from simply filling seats to maximizing the yield of non-traditional pathways. #2. Use of AI The use of AI should eliminate the administrative latency associated with transcript processing and initial triage. However, AI should not be used as the final authority in admissions or academic appeals. The potential for algorithmic bias within the high-stakes gatekeeping of admissions presents a liability for most institutions, and cannot be effectively managed at scale at this time. #3. Transfer Transparency Transfer transparency is now a key competitive driver. Research indicates that the average student loses approximately 40 percent of their credits when they transfer. Institutions that provide instant automated evaluations of transfer credit before students even submit an application will earn the trust of the swirling student population. #4. Human Connection Students build trust with institutions through a human-in-the-loop governance model. Students appreciate automated processes that enable them to receive immediate responses; however, they also need to have the assurance that the human side of the business is verifying the edge cases. It is essential that there is transparency about where AI begins and human ends to provide institutional credibility. #5. Breaking Down Silos We are experiencing the demise of the traditional silos that have existed between Admissions, the Registrar, and Advising. In order for institutions to experience operational success in 2026, they must create a data-sharing ecosystem that enables advisors to see transfer equivalencies real-time, rather than waiting several weeks for a manual audit. #6.
The institutions best positioned for 2026 are treating enrollment strategy as an ecosystem challenge rather than a front-end recruiting problem. The National Center for Education Statistics projects a sustained decline in traditional college-age populations in several regions through the end of the decade, which is already forcing leaders to diversify learner profiles toward adult, reskilling, and transfer populations. In this environment, AI has a legitimate role in improving forecasting, identifying friction points in application and transfer pathways, and supporting faster, more consistent operational decisions, but it should not replace human judgment in admissions or advising, where trust and context matter. Transfer credit transparency is quickly becoming a differentiator because learners expect clarity before committing time or money, and opaque policies now signal institutional risk rather than rigor. The most effective institutions are aligning Admissions, Registrar, and Advising around shared data models and common definitions of student progress, while using automation primarily to remove administrative drag, not to distance staff from learners. Ultimately, enrollment growth in the next phase will favor institutions that clearly connect education pathways to workforce outcomes, demonstrating how credentials translate into employable skills and long-term mobility rather than treating degrees as standalone products.
Enrollment strategy in 2026 is increasingly being treated as a cross-functional operational challenge rather than a single-department function. With the National Student Clearinghouse projecting declines in traditional college-age populations in multiple regions, institutions are shifting focus toward transfer, adult, and workforce-aligned learners to sustain growth. AI can play a strategic role in predictive enrollment modeling, identifying bottlenecks in transfer pathways, and streamlining administrative workflows, but overreliance risks eroding student trust if human judgment is removed from key advising and admissions decisions. Transfer credit transparency has emerged as a competitive advantage, as learners and employers demand clarity on how prior learning translates into program requirements. Effective institutions are integrating Admissions, Registrar, and Advising teams around shared data and automated workflows, using technology to remove administrative friction while preserving personalized support. Ultimately, connecting programs and credentials to measurable workforce outcomes is becoming central to recruitment and retention, turning education pathways into tangible economic value for students.
Institutions facing the 2026 enrollment landscape are responding to demographic shifts by expanding focus beyond traditional students to include transfers, adult learners, and career-focused programs. The National Student Clearinghouse reports that nearly 40% of today's undergraduates are transfer or non-traditional students, making transparent credit policies a key differentiator in attracting and retaining learners. AI can enhance admissions and academic operations by predicting enrollment trends, identifying at-risk students, and streamlining administrative workflows, but overuse risks undermining trust if personal guidance and human oversight are removed. High-performing institutions are aligning Admissions, Registrar, and Advising teams around shared data and automated processes to reduce friction while preserving individualized support. Connecting programs to clear workforce outcomes is increasingly central, allowing learners to see the tangible value of credentials in career advancement, which strengthens recruitment, retention, and institutional reputation.
I have been in the business of running big campuses for 20 years and I can say first-hand that people build their confidence in the small details. AI will help to better route people, monitor queues, and predict demand; however, it should not be the only source of information for high-stakes situations. Schools should have an independent human reviewer conduct reviews at the point of service and maintain an audit trail so teams can provide justification of their actions. A transparency advantage can be gained by developing a unified dashboard for all functions of admissions, registration, and advising; and by creating clear service-level agreements (SLA) and proactively notifying students of the status of their requests. As a result, redundancy in processes will be minimized and the stakeholders will have increased confidence in the processes.
The pipeline of 18-year old high school graduates will decrease because graduates will peak in 2025 and thereafter trends downward for many years. The schools that emerge victorious will not supplement their lead generation through lead purchases, they will augment their entire funnel by providing faster assistance with the FAFSA, faster processing of transcripts and providing transparent policies surrounding transfers. Transfer activity is currently being utilized as a means of relieving pressure; transfer enrollment increased by 4.4% during fall of 2024, and transparency is the means by which to recruit students. Artificial intelligence should be utilized to scan the documents provided by prospective students to identify any missing items, and provide suggestions for what the next steps are. AI should not be used to determine admission, provide financial aid or grant college credit. Many prospective students will build trust in an institution based upon the ability of that institution to provide transparency in the decision making processes of the institution and allow the student to appeal any decisions made by the institution to a human being in a clean and clear manner.
By 2026, enrollment is not just a matter of marketing it is also a matter of throughput and as a result schools need to start behaving like high trust service teams, particularly given the impending cliff that is about to hit them. Students will not wait three days to get an answer to a transfer inquiry, they will just leave. AI should be used for triage, not for judgement calls. The use of AI to summarise files, draft messages and route files among the admissions team, registrar's office and academic advisors is appropriate. However, the ultimate decision on admission, credit transfer or exceptions must be a human being who is able to provide an explanation of the reasons for the decision. The degree to which students can see transparency related to transfer credit has become a competitive advantage in enrollment marketing with students being able to see "42 credits will transfer" prior to committing to enrolling therefore reducing friction and melt (students dropping out). The major vendors in this area are Transferology, Parchment, National Student Clearinghouse, and EAB-like advising solutions. Because of this, schools that have established and follow policies in a timely manner will be successful in transferring credit.
In 2026, the enrollment cliff is less of a surprise and more of a forcing function. Leaders are shifting from broad top-of-funnel growth to precision work focused on protecting yield and retention. This begins with segmenting by intent and readiness, not just by geography or test scores. Teams should run weekly funnel health reviews tied to student-level signals. The focus should be on speed to first contact and quality of follow-up. Build micro pathways for adult and transfer learners with clear timelines. Align scholarships with persistence milestones instead of just entry. Connecting Admissions with Advising early helps prospects visualize a viable first-term schedule, reducing melt and improving confidence when demand is volatile.
As an EdTech founder, I've watched schools struggle with enrollment paperwork as student populations change. We saw transfer students waiting days for credit approval, so we automated the whole process. What used to take hours now happens in minutes, and students can see exactly where their application stands. But some decisions still need people - you can't let an AI handle sensitive student issues. The sweet spot is letting software handle the boring admin stuff while humans focus on talking with students and figuring out the tricky cases. If you have any questions, feel free to reach out to my personal email
I've worked with educational tech, and here's what I've found. When paperwork gets automated, staff have time to actually talk to students. With so many students transferring, schools need to make their credit policies easy to find online. When you can show students how their classes line up with real jobs, they have a much easier time finding work after graduation. If you have any questions, feel free to reach out to my personal email
Higher education institutions have begun to narrow their recruitment focus and streamline the transitions/activities of students throughout their time at college in response to the demographic drop in enrollment (the so-called "demographic enrollment cliff"). Institutions are moving away from broad "one size fits all" approaches and instead target specific demographic segments (i.e., target adult learners/target transfer students) while improving clarity of credit measurement (e.g., increased transfer credit transparency), and providing clarity in length of time to graduate. Further, greater levels of transparency associated with transfer credit are emerging as a source of competitive advantage as students increasingly prioritize predictability and workforce alignment in their educational pursuits as opposed to simply the reputation or brand value of institutions. Institutions are transitioning to a more clearly defined set of pathways for students and utilizing expedited evaluation practices by developing stronger coordinating relationships among: Admissions Office, Registrar's Office, and Advising Office. The role of artificial intelligence is being used as a support function rather than a decision-maker and assisting institutions in streamlining inquiry processes, identifying bottlenecks in the student experience, and increasing internal collaboration among departments, while also maintaining human involvement in high stakes decision-making processes that impact student trust. The challenge for colleges/universities is to achieve a balance between automating routine processes, allowing institutional staff to focus on those interactions that require higher levels of judgment and empathy when working with students; thereby providing greater opportunities to develop relationships and build trust among students and departments. The key to successfully addressing the current demographic enrollment cliff will be the institutions which successfully align automation with the overall transparency associated with the various educational pathways offered, while ensuring that there is a clear alignment/connection to workforce outcomes through each pathway.
Due to the decrease of available transferring students, schools are now placing their priorities on managing student enrollment from start-to-finish as well as recruiting students. As a result, schools are focusing on reducing friction in the enrollment process and increasing speed at which decisions are made regarding whether or not a student will be accepted into an institution, particularly for transfer students. Furthermore, as many have cited uncertainty regarding transfer credits as being one of the main barriers for adult and mobile learners completing their degrees, transparency of transfer credit will be a major differentiating factor between institutions.Use of AI to automate processes can greatly increase efficiency in administering enrollment; however, institutions should not use AI to replace human decision-making, only to assist human decision-making. Artificial Intelligence has been proven effective in triaging application processing, identifying missing documentation in application packages and standardising communications. Final application decisions and complex application decisions must always be made by human staff members in order to maintain customer trust. Institutions that successfully combine automation, transparency and clear appeal processes will have the greatest likelihood of enhancing efficiency while providing students with confidence.