Dr. Hernán Londoño is Lenovo’s Chief Technology and Innovation Strategist and a visionary leader in education and technology. With over 20 years as a CTO and CISO in higher education, he has driven transformative initiatives in cybersecurity, AI, and innovation. A published author on AI and cybersecurity, Dr. Londoño serves on advisory boards, including those of various universities academic programs, and the National Applied AI Consortium. Recognized with prestigious awards like Dell Technologies’ Game Changer Award, he holds a Ph.D. in Computer Science and a Post-Doctorate Certificate from Harvard. His current research explores the human impact of AI on cybersecurity risk.
Four years have passed since I wrote my first article about the fragility of the higher education system in the US. At the time I wrote Avoiding the Edge of the Cliff, the lens through which I saw the realities affecting higher education was a bit different. The perceptions and observations that informed my writing then came from multiple decades of experiencing the system from within. Fast forward four years, now my lens is wider. For the past four plus years (almost feels like ten years instead), through multiple strategy roles with two of the largest global technology companies, I have accrued interactions and provided strategic advice to dozens, perhaps hundreds of leaders from institutions of all sizes, denominations, missions, and operating modalities. This includes presidents, provosts, governing boards, cabinet leaders, CXOs of all types, academic and administrative staff and more. I can say with some level of precision that many of the perceptions outlined in that first article appear to be accurate. At least the current reality seems to provide some degree of validation for what I wrote at that time. The fact that I may have been right then, is of course not entirely positive; especially considering that even through a narrow lens I was able to capture challenges with such fidelity. An unscientific deduction I can make from that phenomenon is that such challenging realities were already acute enough then to be easily perceived without much difficulty.
In my opinion, there are perhaps two metrics which together function as fairly precise indicator to gauge how challenging a business segment is. Such metrics are the number of organizations leaving the segment (as of closing), and the rate at which they exit. According to the Higher Ed Dive Team 2024 article How many colleges and universities have closed since 2016? | Higher Ed Dive, over 100 colleges have closed or merged over just the past 8 years. This is a staggering number in 8 years. However, when one looks at just the past 4 years, it is easy to spot a recent acceleration of the closure phenomenon. As stated in Closed Colleges: List, Statistics, and Major Closures | BestColleges, “At least 72 public or nonprofit colleges have closed, merged, or announced closures or mergers since March 2020.” Surely an argument can be made that the recent pandemic had a significant influence on this acceleration. To that I say, yes absolutely the truth. I however think that this is a textbook case of correlation versus causation. The preconditions for these closures to take place were set in motion long ago, the pandemic did not cause the closures, it just accelerated them. It is worth mentioning that these closures are seen more significantly in private, for-profit institutions. Though not exclusively.
In terms of current pressures, I don’t think much has changed since my first article. Maybe what has changed is that this new lens that I am now using to glean my observations through has offered me the chance to sharpen my focus. Things appear to be clearer. Enough has been written about the enrollment cliff, so I won’t belabor that point, but I will highlight perhaps one thing. What’s now known with more clarity is how the cliff’s effects will be felt differently in various regions of the US. This is important because some institutions will have to react faster and more decisively than others. Couple this decline in enrollment, predicted to be precipitous in some cases, with what Moody describes as a “hidden liability” in its August 2024 higher education segment rating (Sector_In-Depth-Higher-Education-US-Pentup.pdf). This is approximately 950B in deferred maintenance costs. This figure applies to the higher education segment as a whole. As if this wasn’t enough, as stated by the 2024 Commonfund Higher Education Price Index report, the cost of operations for the higher education sector as measured by the Higher Education Price Index (HEPI) continues to grow at a rate that outpaces the inflation rate for all other goods as measured by the Consumer Price Index (CPI). This by the way has been a consistent trend since the year 2000, and surely for eight out of the past ten years. One significant outcome of HEPI outpacing CPI is the growing perception by consumers of the decline of the value provided by a higher education degree. It is a return on investment calculation people are now more consistently making about everything they consume, education not excluded. One more element to add context to the financial picture of the segment is based on NACUBO’s 2023 Tuition Discount Study. The study points to two important things. The first one is that tuition discounting in private schools has reached an all-time high at 53.9%. This is the average; many institutions go beyond this average just to be able to compete. The second one is that the growth in discounts seems to be an important contributing factor in a significant decline in revenue of 5.4%. Taking all these lingering pressures into account, the credit rating agency Fitch summarizes higher education outlook very simply as “deteriorating” for 2025 Deteriorating Outlook to Intensify for U.S. Colleges in 2025. This is a natural consequence of institutions’ inability to find efficiencies to offset the negative effect of raising operating costs, versus declining revenues. I’d argue, this is an unsustainable trajectory. Enough said.
With this “deteriorating” projection ahead, there is a strong argument to be made about some type of reaction. On the one hand, the trust and sense of value in higher education needs to be restored, and on the other hand, and in parallel, significant improvements need to be made to stop the current and projected financial situation from getting even worse. An oversimplified view I have of these two elements is that institutions need to improve and/or innovate their product, that is academic offerings, as well as they need to improve their operating efficiency. This is a very tactical view, and it is so purposely, so that it is explicitly understood that this reaction needs to go beyond shelved and inactionable strategic plans, endless academic discussions, SWOT analyses and reports of all kinds. Having had the fortune in my career of existing, often at the same time, in the higher education strategic and tactical worlds, I’ve had a long-held view that at some point “we need to land the plane”. I use this metaphor often and in a very colloquial way to signify that at some point for every plan, for every analysis, for every discussion there needs to be a “finish line” of sorts that needs to be crossed, and some element of tangibility needs to be attained, even at the risk or expense of failure. In previous years I have proposed such tactical approaches in An Inflection Point for the Creation of New Cybersecurity Operating Models in Higher Education | EDUCAUSE Review and Avoiding the College Enrollment Cliff With AI | EDUCAUSE Review.
Particularly as it relates to AI, because of the roles I’ve had for the past four years, coupled with my academic formation, and the years I was a CTO in higher education, my views on the potential positive impact of AI have expanded significantly. I have not only studied AI, I have also deployed it and advised others on it. Hinged on this experience, I am a very strong proponent that AI can significantly improve institutional and operational efficiency. Unfortunately, and perhaps customarily, the higher education segment, despite the challenges and pressures explored earlier in this article, is not funding, adopting and deploying AI for operational efficiency nearly at the rate others are. In a recent 2024 study (Now decides next: Insights from the leading edge of generative AI adoption) Deloitte found that 91% of surveyed organizations expect their productivity to increase because of the adoption of AI. The same study found that 42% of the surveyed organizations are already reporting efficiency, productivity, and cost reduction being achieved with the use of AI. This is very tangible; this means that almost half of organizations in other segments are attaining that tangibility I mentioned earlier. Conversely, a survey conducted by Forbes (Higher Ed Leadership Is Excited About AI – But Investment Is Lacking) found that only 21% of surveyed institutions believe they are prepared to fund and deploy AI operationally.
Educause in its more recent report of top 10 priorities for 2025 (2025 EDUCAUSE Top 10 | EDUCAUSE), organizes higher education top priorities in groups, and in doing so it defines the “Competent Institution”. This group of priorities is all about institutional efficiency, agility, streamlining, and modernizing both process and infrastructure. This “Competent Institution” grouping of priorities is without a doubt a very fertile ground to plant seeds for efficiency improvement. By direct correlation, this in my opinion is where AI thrives. In writing the final part of this article I want to provide some measure clarity around the topic of AI use cases and their impact on efficiency gains. By far, the most discussed and deployed AI use case is the one of a digital assistant. With the advent of Generative AI, and the ease with which Retrieval Augment Generation (RAG) can be done, it is not surprising its deployment is so prevalent. Having seen the case operationally in many settings I can surely say it helps to increase workforce productivity. These digital assistants also have an effect of improving customer (student/faculty/staff) experience, which counts towards institutional success. These productivity gains sometimes result in efficiency gains as well. I however feel that the efficiency gains from digital assistants in higher education might not provide a significant enough contribution to help change the current trajectory of many institutions at risk. The more significant efficiency gains may come from other applications of AI, for example (not exhaustively):
- Optimization of physical plant utilization.
- Implementation of real-time/near-real-time processing to support the recruitment/enrollment/graduation journey from prospect through matriculated student, and eventually through graduation.
- The use of sensors and edge compute/AI to optimize how services are rendered in cafeterias, parking lots, tutoring and counseling centers, libraries etc., to optimize service provisioning and with that user experience, as well as to minimize waste.
- Optimization of class scheduling to adjust the ratio between the number of sections and number of students per section, as well as the number of classroom and buildings in use at any given time.
- Personalization of marketing efforts and fundraising activity to create a more unique experience for the constituent groups being serviced, while enhancing, and really optimizing internal operational efficiencies.
Any one of the previously mentioned examples, and there are more, may require significant planning and investment in some cases, however, the results are directly proportional to the level of effort. The key to all this is to start somewhere. The clock is ticking.