2026 EDUCAUSE Top 10: Making Connections

Writing about where higher education is headed in the coming year is difficult when so much of our world is being torn apart. Political violence is erupting on campuses. Tensions around free speech and ideological differences in the classroom are leaving leaders and faculty uncertain (or, sadly, very certain) about their future in academia. And the financial stability of many institutions, and of Minority Serving Institutions (MSIs) in particular, is increasingly strained by reductions in federal support.^Footnote1

Remarkably, this description encapsulates just a few recent weeks of what has already been a tumultuous year for higher education. Indeed, it's hard to know what to say, and any guidance for institutions on traversing the road ahead risks feeling insufficient for the challenges we face.

If there is a north star to guide all of us in the higher education technology community through this turbulent time—one that always seems profoundly bright even in moments like the present one—it's this: We must find ways to cultivate human connection, both within ourselves and with one another.

Our challenges, after all, are deeply human. And so, too, are our ways through them.

It's heartening, then, that the 2026 EDUCAUSE Top 10 tells a decidedly human story about what higher education technology leaders will be focused on in the year ahead. Alongside their ongoing nurturing of institutions' technologies and data, technology leaders will also be nurturing connections among and between the people at their institutions—their senior leaders, staff, faculty, and students.

The 2026 EDUCAUSE Top 10 reminds us that it won't be a silver-bullet solution—or even artificial intelligence (AI)—that will get our institutions to the other side of our present moment. Instead, it will be our institutions themselves joining their many different people and disparate parts together, finding common purpose, and agreeing on what matters most and how best to move forward. And it will be because the people within them connected directly and humanly with one another to listen to and understand one another's needs, passions, and ideas, and to learn how to bring each person's capabilities to life. More than anything else, connections among and between our people hold promise for carrying higher education through 2026.

Community psychologist and jazz drummer Paul Dokecki wrote about jazz as a model for making these collective and individual connections, what he called the "grandeur of the social and the creativity of the person."^Footnote2 Jazz works as an artform precisely because it sits at the tension between those two things, the social and the individual. It balances agreement on tempo, key, mood, and performative standards on the one hand, with improvisation, creativity, and a wild spirit on the other.

It's a tension that effectively captures what higher education needs: the grandeur of the social—the institution uniting its people and drawing strength from shared values and commitments—and the creativity of the person—leaders, staff, faculty, and students, coming alive with their own individual capabilities and thriving in their respective spaces of work and learning.

These are the goals our technology leaders expressed through this year's Top 10.

This report summarizes each of the 10 issues in detail, highlighting how technology leaders are thinking about and planning for them. The Top 10 panel intends for this report to serve as a guide to help you and your institution create the connections you need to move forward in a tumultuous world, together.

Collective Will and Individual Capabilities

Higher education technology leaders are making connections at their institutions in two distinct ways: (1) by fostering a collective will across the institution; and (2) by supporting each person's individual capabilities. Before turning to the issues on the 2026 list, it's worth pausing to explore these two overarching themes, which help set the tone and attune your attention for what follows.

Collective Will

Jazz teaches you to listen. As jazz trumpeter and composer Wynton Marsalis once put it, "To listen, and to follow. Follow the line and anticipate. There is the pressure of time and harmony, a lot of pressure. And you have to be truthful."^Footnote3

Listening, time, and harmony in jazz are deeply social practices that bring the collective of musicians together, creating one sound out of what could otherwise be a disjointed cacophony of noise. Similarly, in 2026, technology leaders will be focused on building a collective will across their institutions, helping to establish shared values, standards, and goals that will enable their institutions to move forward in an agile and coordinated way. This work will be hard for a sector that has traditionally been slow to adapt and siloed across many of its functions. Many institutional leaders and practitioners feel mired in labyrinthine decision-making structures that are ill-suited to a world that is increasingly fast-paced and unpredictable.

"Historically at universities, to change the study line (the curriculum) may take a couple of years to go through all the committees and machinations," explained Jan Hesthaven, president of Karlsruhe Institute of Technology in Germany. "A couple of years is an eternity in the way technologies are developing right now. Two years from now, when we have the new study line, it may well be entirely irrelevant."

The "committees and machinations" that slow down decision-making aren't manifestations of a collective will but rather are symptoms of the absence of one. Shared governance, a common expression of collective will, clarifies decision-making standards, roles, and processes, allowing decision-making to be quicker, not slower.

Much of higher education is siloed—our data, our technology deployments, and even our strategic planning and decision-making. John Lutz, vice chancellor for development and alumni relations at Vanderbilt University, likened this siloing in higher education to patterns he had seen earlier in his career:

I came from a thirty-year career in business, working with a lot of the big banks. As these firms grow and develop lots of successful divisions, things tend to get pretty siloed. So maybe the credit card people would have that info, mortgage people have different info and the private bank people have that info and nobody shares, and it makes them unable to see their clients in a unified way. I expected coming to a university that this kind of effect might be less prominent, but it turns out that it's a fairly common issue at universities, too. One of the best things happening at Vanderbilt is that we have made enormous progress on this issue over the past few years and have developed ways of working across the various schools and units in a nimble and agile way.

A collective institutional will requires de-siloing—connecting resources and knowledge across the campus to shape a shared institution-wide perspective. Fragmented institutions built on disparate ideas about institutional realities will find it more difficult, and maybe even impossible, to prioritize and agree on complex decisions that impact the entire campus community.

In the year ahead, technology leaders will help their institutions become nimbler and less siloed by supporting coordination and collaboration grounded in new data and AI capabilities. These efforts will require institution-wide sharing, access to, and engagement with reliable data and insights, as well as centralized policies and standards around the use of data and technology to help establish consistent, and more agile, practices across the institution. They will also require technology leaders to connect a wide range of campus stakeholders, ensuring that the collective will of the institution is informed by the wealth of knowledge and expertise of its people.

Technology leaders' efforts to support the collective will of the institution will focus on coordinating data analytics for business and operational decision-making (issue #3), expanding and improving access to data across the institution (issue #4), integrating knowledge management into institutional adoption of AI (issue #5), improving technology investment decisions (issue #6), and leveraging forward-looking analytics for institutional agility and planning (issue #8).

#3.Data Analytics for Operational and Financial Insights
Leveraging data analytics to provide insights into spending patterns, enrollment trends, and areas for cost savings and operational efficiencies

#4.Building a Data-Centric Culture Across the Institution
Expanding and improving data access and unlocking the full potential of data as a strategic asset

#5.Knowledge Management for Safer AI
Mitigating the risks of artificial intelligence by integrating knowledge management into data governance, privacy, and ethics programs

#6.Measured Approaches to New Technologies
Making better technology investment decisions (or choosing not to invest) through clear cost, ROI, and legacy systems assessments

#8.From Reactive to Proactive
Using data for scenario modeling, forecasting, and prediction to strengthen institutional agility and planning

Each of these issues presents institutions with a unique set of challenges, as well as unique benefits. But they also share a common thread: their ability to bring people within the institution together. When that happens, teams and individuals can build stronger connections, share resources, agree on a key and tempo, and effectively bring a song to life.

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Individual Capabilities

Dokecki wrote, "When a musician merely plays technically in rule-bound fashion, the result is not jazz, since, as Duke Ellington said, making music 'don't mean a thing if it ain't got that swing.' Improvisation, creativity, and human development are at the heart of jazz."^Footnote4 Jazz is deeply individual, bringing to life a wild spirit that sings and soars on top of the (collective) music. In 2026, technology leaders will be cultivating that wild spirit by supporting each individual person's capabilities for bringing the institution's technologies and data to life effectively, safely, and creatively.

Individual capabilities are expressions of the ideals of the collective. For example, innovative uses of AI technologies in the classroom can be transformational precisely because they're integrated into the institutional strategy and support systems that enable individuals to adopt and use them. Students' competence as technology users and industry innovators will be strengthened by institutional strictures and guidelines around technology adoption and use, not limited by them.

Christopher Buddle, associate provost for teaching and academic planning at McGill University, described how his institution is approaching AI as tool to empower teaching and learning. "We're working with our IT department and with researchers that are experts in technology to really find the smart ways to do smart things with technology instead of just looking at it as a blunt tool," he explained. "Many people are approaching AI as a threat to the institution and academic integrity, rather than something through which pedagogy can be improved, through which the ways we assess can be improved, and as a net benefit for learners and instructors."

In the year ahead, technology leaders will equip and empower the people across their institution to realize the "net benefits" of the technologies and data that bring the institution's collective will to life. This work will require technology leaders who are skilled at educating and training users in the safe and effective adoption of new AI and other technology capabilities and who can facilitate partnerships with programmatic and academic leaders to ensure students have access to the training they need for their educational journeys and beyond. It will also require technology leaders to be present with and connected to individual technology and data users, to listen to their hopes and frustrations, and to develop resources and supports in response to those things.

Technology leaders' efforts to support individuals' capabilities will focus on building a culture of shared responsibility in cybersecurity (issue #1), enriching each person's understanding and use of AI technologies (issue #2), preparing students to be savvy technology users for the workforce (issue #7), adopting AI to improve the institution's business and administrative functions (issue #9), and ensuring decision-makers know how to interpret and use their institution's data (issue #10).

#1.Collaborative Cybersecurity
Building a cybersecurity culture of shared responsibility, end-user awareness and training, and improved access to security services and supports

#2.The Human Edge of AI
Empowering students, faculty, and staff to engage with artificial intelligence tools critically, creatively, and safely

#7.Technology Literacy for the Future Workforce
Supporting discipline-specific technology training and education to enhance student success with in-demand technology skills

#9.AI-Enabled Efficiencies and Growth
Using artificial intelligence, robotic process automation, and other analytics capabilities to reduce operational costs, streamline processes, and improve strategic and business decision-making

#10.Decision-Maker Data Skills and Literacy
Enhancing the value of institutional data by training and equipping decision-makers to use and interpret it properly

The five issues listed above span a wide range of individual uses: student curriculum, administration, strategic planning, security awareness, the adoption of AI, the interpretation of data, and many other applications. The common thread among these issues, though, is the central role of the individual person in bringing the institution's collective will to life. Taken together, these issues acknowledge that the soul of the institution is expressed through each person's voice—wild, alive with ideas, and empowered to make a difference in the world.

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#1: Collaborative Cybersecurity

Building a cybersecurity culture of shared responsibility, end-user awareness and training, and improved access to security services and supports

James Crooks, Matthew Flower, Justin Gatewood, Frederick Kass, Jessie Minton, and JT Singh

There are few other areas in higher education where connection across the institution is more urgently needed than in cybersecurity.

Cybersecurity is no stranger to the EDUCAUSE Top 10, of course, but this year, technology and cybersecurity leaders are zooming in on the partnerships they're building with institutional stakeholders as the focal point of their efforts. This focus acknowledges that our digital ecosystems have become more widely dispersed across public and personal networks and devices, both on and off campus, and that the institutional strategy for securing and defending those networks and devices must be widely dispersed in equal measure.

In other words, cybersecurity strategy in higher education must become a deeply collaborative and shared commitment for everyone, now more than ever.

Technology and cybersecurity leaders are focusing on two important dimensions of this work in the year ahead. First, stakeholders' security supports must be integrated into their work and not merely layered on top of it (as common measures such as security training, awareness campaigns, and simulation phishing exercises tend to be). Security and privacy measures that fit more naturally into stakeholders' daily work and learning, and whose "why" is evident and meaningful, are more likely to gain buy-in and help overcome barriers of convenience and poor user experience. Integrating practices such as authenticated push multifactor and "least privilege" standards, then, should be done in partnership with, not meted out to, institutional stakeholders to ensure they are grounded in and responsive to the work each person needs to accomplish.^Footnote5 Even measures such as training and awareness campaigns can be more tailored and individualized, helping them feel more a part of the person's daily experiences.

Second, "collaborative cybersecurity" demands security leadership and staff who are engaged and visible to their campus community as an active and familiar presence. Simply "being there" as a friendly face and a helpful presence that campus stakeholders can get to know, well before any security incident occurs, will be paramount. Approachability based on established relationships, after all, can help increase the likelihood that campus stakeholders will reach out proactively when they have security-related questions or needs, and it can help integrate security awareness into the wider culture of the campus community.^Footnote6

Ways to Get Started

Through our panelist interviews and community survey, technology leaders noted some ways institutions might establish a more collaborative approach to cybersecurity:

• Stakeholders are less supportive of security measures when they perceive them as slowing down or complicating the work they're trying to accomplish, especially when they are not engaged in the implementation of those measures. Get to know your users and their critical areas of security vulnerability, understand the work they're trying to accomplish and their points of frustration as users, and work together to identify solutions that ensure security without compromising user experience.
• Continue to offer campus-wide training and awareness campaigns, of course, but experiment with new and potentially more effective approaches to those efforts that fit with stakeholder preferences and needs for engagement. Microlearning opportunities throughout the year, for example, may be more optimal for awareness than larger once-a-year programs. Fun and playful forms of engagement such as gamification can be effective for raising awareness with younger generations of users. The Six Words Project, as one example, helped security leaders at the University of Michigan and UC San Diego create engaging conversations about privacy and cybersecurity across their campuses.^Footnote7

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#2: The Human Edge of AI

Empowering students, faculty, and staff to engage with artificial intelligence tools critically, creatively, and safely

Shelly Belflower, Adam Finkelstein, Jennifer Haas, Muhammad Hossain, Frederick Kass, and Eudora Struble

AI didn't write this report, but it helped, just as it's helping more and more in other areas of work across higher education as users gain greater confidence and ease with it.

Indeed, a growing number of higher education staff, faculty, and students have access to, and experience with, an ever-expanding array of AI-based tools and capabilities to support their work. Some of these users are moving beyond simply using AI to creating AI tools, leveraging new capabilities such as agentic AI to build and train their own systems.

Increasingly, AI is becoming a competency that rests in the hands of each individual person—an individual capability rather than just, or even primarily, an institutional one. It is essential, then, that staff, faculty, and students be trained to vet and thoughtfully adopt the bring-your-own-AI (BYOAI) tools that are not directly provided or supported by the technology team at their institution.

Perhaps nowhere else on campus is this more essential than in the classroom. AI offers seemingly limitless options for both faculty and students in designing and participating in their courses, engaging in teaching and learning, and demonstrating learning and student success. The 2025 EDUCAUSE AI Landscape Study identified "teaching and learning" as the functional area of the institution most focused on AI adoption, with "faculty training" being the single most common element across institutions' AI-related strategic planning efforts.^Footnote8

Whether by choice or not, faculty in particular seem to be on the front lines of AI adoption at their institutions, navigating their own uses of these technologies while also supporting students' uses of them. They are challenged both with creating safe spaces for AI experimentation and assessing its risks and limitations, challenges that require varying levels of education, training, and support from their institution to effectively navigate.

Outside the classroom, opportunities abound for leaders and staff to benefit from AI in the form of more efficient administrative processes and more advanced business analytics and decision-making. Across these use cases, there is immense potential for individuals' use of AI technologies, as well as a persistent risk that those individuals will misunderstand and misuse them. Whether inside or outside the classroom, then, users are most effective, and safest, in developing their AI capabilities when they are guided and supported by their technology teams.

Ways to Get Started

Through our panelist interviews and community survey, technology leaders noted some ways institutions might strengthen the human edge of their AI capabilities:

• Balance clear and consistent AI governance and structures with opportunities for safe experimentation. Walled AI sandboxes can give users space for playing and becoming more familiar with these technologies without fear of "breaking" things or exposing the institution to risk. Makerspaces and AI hack-a-thons can be controlled environments for encouraging exploration of new and creative use cases for AI. Harvard University's AI Sandbox, for example, provides campus users with a safe space for exploring potential uses for these technologies.
• Staff, faculty, and students will need ongoing training, though these efforts can be time- and resource-intensive for overworked and understaffed technology teams. Establishing supported but self-sustaining AI communities of practice and identifying AI champions or exemplars across various departments can help redistribute and integrate AI training and awareness throughout the institution without overburdening technology leadership and staff. Resources such as the EDUCAUSE Higher Education Generative AI Readiness Assessment can help institutions identify opportunities for tapping into these resources across their campus.

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#3: Data Analytics for Operational and Financial Insights

Leveraging data analytics to provide insights into spending patterns, enrollment trends, and areas for cost savings and operational efficiencies

Jennifer Haas, Hemalatha Manickavinayaham, David McMorries, and JT Singh

Financial pressures are intensifying for higher education institutions.

Federal grants and research funds are being cut, and traditional student enrollment trends are threatening tuition revenue declines in the years ahead. Added to these financial pressures, public perceptions of higher education have been souring for decades, and institutions are increasingly under scrutiny to demonstrate meaningful returns on the public's investment.

For many institutions, there are two dimensions to the work of enriching their data insights and decision-making. First, institutions are seeking to better understand and agree on where they need to invest. Institutions may have enrollment targets they want to hit among key student populations, or they may have state funding or accreditation requirements to consider. Improved data infrastructures and insights will be critical for evaluating institutional effectiveness in addressing these priority interests and needs.

Once an institution has agreed on where it should invest, more productive conversations can happen around lower-priority operational and programmatic areas that may not align with the institution's most pressing goals. In times of increasing financial constraints, institutional leaders will need to have difficult conversations about areas where they can no longer invest or where current levels of investment may need to be reduced.  

New data and analytics capabilities in the form of ERPs, CRMs, and BI platforms (just to name a few) offer opportunities for campus-wide discussions about enrollment trends, student success, financial pain points, and potential opportunities for operational and budgetary efficiencies. Equipped with these more advanced capabilities, institutional leaders can more confidently make complicated decisions about resources, investments, and strategic priorities for the future.^Footnote9

Ironically, these new data capabilities also require significant investments and strategic prioritization. Many institutions will have to dedicate significant time and staff resources to cleaning and organizing their data foundations. They will also need to consider investing in more advanced analytics and reporting systems and tools. Perhaps most importantly, and likely as a step that precedes all of this work, institutions will need to consider opportunities for shoring up data analytics staff roles and expertise, as well as opportunities to reskill or upskill existing staff.

Ways to Get Started

Through our panelist interviews and community survey, technology leaders noted some ways institutions might effectively leverage data for operational and financial insights:

• Begin with understanding the data you have and the data you need by evaluating your institution's current levels of maturity in data collection, organization, and management. All data capabilities rest on the foundations you build at the outset, and shaky data foundations will result in shaky outcomes for institutional data-related practices and decisions. Resources such as the EDUCAUSE Analytics Institutional Self-Assessment can help you establish a baseline for your institution's current capabilities.
• CRMs, LMSs, ERPs, and other related data systems can give your institution a leg up with sophisticated analytics and reporting capabilities needed for precisely these kinds of business and operational decisions. Once you understand your data needs (see above), you will be in a good position to identify the best data solutions to support those needs. For example, the EDUCAUSE Demo Day on ERP Solutions is a good resource for those who are starting to think about their institution's solution requirements and potential partners.

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#4: Building a Data-Centric Culture Across the Institution

Expanding and improving data access and unlocking the full potential of data as a strategic asset

Sean Brown, Jennifer Burns, Daniel Ewart, Olivia Kew-Fickus, Diego Leal, and Helen Norris

There's data gold in our institutional mines, but we're all digging separate tunnels and using different tools to get to it.

With access to rich stores of data, more leaders and campus stakeholders are realizing the potential of data for improved planning and decision-making. Indeed, nearly every department within the institution—from academic departments to advancement to facilities to HR—can uniquely benefit from more thoughtful engagement with data for planning and programmatic purposes. But our use of these data is most effective when access is coordinated and shared across the institution. Achieving this kind of coordination requires clear vision and commitment from institutional leaders.

Indeed, an institution-wide culture around the use of data begins with leadership. The 2024 EDUCAUSE Analytics Landscape Study found that while leaders largely understand the value of data and regularly request it, few data and analytics professionals reported that their leadership is fully committed to, and actively investing in, analytics across the institution.^Footnote10 A lack of investment and support among senior leadership can contribute to uneven engagement with analytics across functional units at the institution, as well as neglect of centralized policies and standards needed for consistent institution-wide engagement with data.

Building an institution-wide data culture will also require a continued focus on integrated systems and unified data, bringing together disparate data sources to give users a more consistent and complete institutional picture for decision-making.^Footnote11 Siloed data sources and data management practices can contribute to fragmented, and even misguided, planning and decision-making, greater data privacy and security risks, and inconsistent data definitions and understanding.

Integrated data sources and institution-wide access will place a higher premium on strong data governance and management practices, along with enhanced monitoring and access controls. With the rapid emergence of new AI capabilities and data needs, such as staff and student access to APIs for workflow automation, institutions, now more than ever, need to establish clear and consistent approaches to organizing and accessing their data. Mature data governance and management can help ensure institution-wide data quality and safety while also providing users with a comprehensive view of the institutional data they need.

Ways to Get Started

Through our panelist interviews and community survey, technology leaders noted some ways institutions might establish a data-centric culture across their institution:

• Institution-wide engagement with data demands technology leaders who are adept at listening and responding to the unique data needs of a wide range of institutional leaders, functional units, and discrete program- and project-based use cases. Begin by seeking to understand the questions stakeholders across the institution need answered, where institutional data may be able to help, and the resources that are needed to get those data to those stakeholders.
• Establish the centralized data governance structures and policies needed to buttress institution-wide engagement with data. User trust in the relevance and quality of institutional data is paramount to getting buy-in and support for using those data, and relevance and quality start with sound governance and management. If you don't have a data governance program in place, establishing one is a clear first step for your institution. The 2023 EDUCAUSE Data Governance Action Plan can help you identify where to begin.

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#5: Knowledge Management for Safer AI

Mitigating the risks of artificial intelligence by integrating knowledge management into data governance, privacy, and ethics programs

Didier Contis, Isabel Gallin, Justin Gatewood, Hemalatha Manickavinayaham, John McGuthry, and Brandon Rich

When enterprise AI solutions fail, as they often seem to, poor data quality is likely a top contributing factor.^Footnote12 AI solutions are only as good as the data they are trained on, and institutions that use incomplete, outdated, or overly complicated data are likely to see disappointing results that weaken campus stakeholder trust in those solutions.

To be most effective, our enterprise AI tools need to "know" us. They must understand, in data form, an institution's unique historical and cultural contexts, its particular business processes and policies, and its array of services, resources, and people. Enterprise AI tools that know these things about an institution can more effectively respond to prompts and arrive at solutions that are accurate and fitted to their institutional environments.

Enterprise AI tools that know us too well, on the other hand, introduce uncomfortable risks most institutions don't (and shouldn't) want. (We all know what happens in 2001: A Space Odyssey.) There are systems and types of data that institutions don't want their AI tools to access and share with others, so privacy and security concerns are paramount.

Knowledge management (KM) can help institutional teams become more familiar with their enterprise AI tools while also restricting those tools' access where needed. KM processes can help an institution organize and document its collective identity, structures, and practices, as well as articulate its values and goals for certain areas of AI inquiry and support. This, in turn, can lead to AI solutions that are more accurate and grounded in the institution's particular context. These KM processes are deeply collaborative and require engagement with stakeholders across the institution who can provide input on, and help evaluate, AI knowledge related to institutional systems, practices, and policies.

As campus-wide engagement with these tools grows, institutions will need to continuously align their KM processes with broader data governance and ethics efforts. This alignment will help to establish appropriate boundaries for AI tools while building consensus on the types of information various stakeholder groups will require from those tools. It will also clarify the types of information stakeholders don't need to—or should not—access.  

Ways to Get Started

Through our panelist interviews and community survey, technology leaders noted some ways institutions might establish knowledge management for AI:

• Form an AI governance body or committee to continually evaluate the accuracy and safety of the institution's AI tools and outputs. In particular, these governing bodies should consider documenting "sensitivity levels" across the institution's data, establishing categories of sensitivity that will help determine which types of data are appropriate for AI use and which are not. The University of Notre Dame's Information Security Policy, for example, includes "security designations" for the institution's data.
• Knowledge management of enterprise AI tools will require clear and updated documentation of institutional policies, practices, services, and resources that can serve as data inputs and the foundation of what our AI tools "know." Many institutions will discover that their existing documentation (e.g., policies, web materials) is outdated, incomplete, or insufficient for reliable AI outputs. Taking stock of the institution's knowledge base and identifying these and other gaps may be a necessary first step for sound KM.

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#6: Measured Approaches to New Technologies

Making better technology investment decisions (or choosing not to invest) through clear cost, ROI, and legacy systems assessments

Didier Contis, Penny Evans-Plants, Daniel Ewart, Olivia Kew-Fickus, and Diego Leal

If you want to see the technology marketplace incubating and growing in real time, browse the GitHub repository and marvel at the sheer volume and velocity of the innovation happening there.

Indeed, the technology solutions market is ever-expanding, and institutions are inundated with new tools—especially AI-based ones—and new features for existing technologies. The constant influx can make the product landscape seem dizzying in its array of options.

Unfortunately, many institutions navigate this array through suboptimal decision-making processes that are deeply ingrained in their technology adoption culture, resulting in dissatisfaction and negative outcomes. Most technology leaders, for example, report that "quick-fix technology purchases" are common at their institution, putting strain on IT staff and decreasing interoperability and efficiency for users.^Footnote13

In a higher education environment characterized by tighter budgets, understaffed technology teams, and overflowing workloads, institutions can no longer afford to be impulsive with their investments. ROI and ROV (return on value) are the guiding principles of the day, and few technology purchasing decisions can be made without first understanding their costs, potential benefits, and impacts. In other words, now more than ever, we must demonstrate value if we want buy-in for the technology decisions we make.

Some institutions may need to adopt a wait-and-see mindset when making purchasing decisions, especially in a volatile technology marketplace.^Footnote14 Resisting a brand-new solution or feature today in favor of a more reliable and advanced "2.0" version tomorrow may make the most sense when funds are tight, as might conducting smaller pilots of solutions before making larger scaled-up, enterprise-level purchases.

Stronger and more consistent IT governance processes are needed to help teams navigate these complex decisions. A shared institution-wide understanding of the technology solutions already in place, the most pressing technology gaps and debts, and the resource demands and risks that come along with each technology implementation can help technology leaders find alignment and make decisions that best fit the realities of the overall capabilities and needs of their institution.

Ways to Get Started

Through our panelist interviews and community survey, technology leaders noted some ways institutions might develop more measured approaches to technology purchasing decisions:

• Take stock of the technology debt and existing technology ecosystem at your institution before moving forward with substantial new purchases. The institution may not be using an existing system to its full potential, so you may be able to resolve an identified need without having to purchase an additional solution. Removing duplicative technologies and consolidating licenses can help simplify the technology ecosystem and make it easier to identify potential gaps or needs at the institution.
• Demand transparency and accountability from your solution partners and build this expectation into your contracts. Confidence and trust in technology purchasing decisions can be enriched by clear and reliable information from your partners, including clear cost information, details on solution integrations and interoperability, and product roadmaps for the future. The Higher Education Community Vendor Assessment Toolkit (HECVAT) is a valuable tool that your institution can use to more effectively vet solutions before making significant purchases.

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#7: Technology Literacy for the Future Workforce

Supporting discipline-specific technology training and education to enhance student success with in-demand technology skills

Isabel Gallin, Jennifer Haas, John McGuthry, David McMorries, and Jesus Ramirez

What does it mean to be an educated and workforce-prepared student?

While traditional ideas of preparedness emphasize discipline-specific concepts and skills, today's workforce demands fluency with technology, data, and, increasingly, AI. Across many disciplines, students aren't fully educated and prepared to enter the workforce if they cannot meaningfully engage with the technologies and data capabilities that support those disciplines.

A recent National Skills Coalition study analyzed more than 43 million job postings and found that 92 percent of jobs in the United States require digital literacy skills and proficiency across a range of specific industry- and role-based technologies and systems.^Footnote15 The study also found that nearly a third of the U.S. workforce has "little to no" digital literacy skills, revealing a digital skills gap that higher education institutions are increasingly seeing the need to fill.^Footnote16

AI adoption is spreading quickly across the workforce, becoming more necessary than nice-to-have as a skill for future students. According to a recent survey conducted by Microsoft and LinkedIn of more than 31,000 professionals from around the world, "66 percent of leaders say they wouldn't hire someone without AI skills," and "71 percent say they'd rather hire a less experienced candidate with AI skills than a more experienced candidate without them."^Footnote17

Preparing students for jobs in 2026 and beyond requires embedding AI and other technologies as a core competency in their education and training. Importantly, each discipline has its own applications for and challenges with technology, so technology leaders will need to collaborate closely with department and program leaders to design technology literacy efforts that meet specific industry and profession needs. This will demand continuous assessment of the curriculum as well, and academic decision-making processes that can match the agility and speed of the AI marketplace.^Footnote18

Perhaps just as important, institutions will need to construct new bridges to industry partners, who can provide critical guidance on the skills and competencies that are expected of students entering the workforce. These partnerships could even lead to opportunities for students, such as internships and certifications, that could help them achieve the digital proficiency necessary for their future jobs.

Ways to Get Started

Through our panelist interviews and community survey, technology leaders noted some ways institutions might prepare students with the technology literacy they'll need:

• Facilitate conversations among your faculty and department leadership aimed at redesigning curriculum to respond to workforce changes. Program- and discipline-specific technology requirements must be shaped and supported by faculty, not only to help ensure these skills are grounded and integrated into the students' educational journey, but also to promote faculty buy-in rather than resistance. As one survey respondent said, "Discussions at the program or major level will help faculty develop a shared understanding of what their graduates will need and where in the curriculum it makes the most sense to build [technology] skills and literacies."
• Beyond the curriculum, institutions should embed technology skills—and AI skills specifically—into the holistic student experience. Initiatives that enrich student learning and development should incorporate these competencies, and technology leaders should be included in campus-wide conversations with student experience leaders about preparing well-rounded students for the future.

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#8: From Reactive to Proactive

Using data for scenario modeling, forecasting, and prediction to strengthen institutional agility and planning

Karen Bates, Shelly Belflower, and Diego Leal

Higher education has a reputation for always being "on the back foot"—slow to change and innovate and behind on trends that are shaping other industries around the world.

With the wealth of data institutions have at their disposal, and with the emergence of more sophisticated analytics and decision-making capabilities, institutions have an opportunity to shift from using data merely to describe what is or has been to explore what is likely or possible. Strengthening these capabilities can make institutions more responsive and agile in their strategic planning and decision-making and help them avoid being caught off guard by emerging trends.

Proactive use of data is especially applicable to institutional academic and student support functions. By triangulating different sources of student-based data, institutions can gain a deeper understanding of the student journey, enrich educational experiences, and anticipate areas where student support is needed. It is important to note, however, that attempts to make student support more predictive can miss the mark. Models that overlook the emotional, social, and other human dimensions of the student journey risk offering incomplete or misleading characterizations of student needs and success.

In these and other institutional use cases of proactive, data-informed planning and decision-making, institutions must ensure that their guiding principle is beneficence toward the student. Students are not merely data points; they are whole humans, and institutions must balance sources and types of data to support this fact and avoid the harms of incomplete or misleading data. While more quantitative data can be useful for predictive modeling in areas such as enrollment and finances, qualitative data and anecdotal signals of potential changes within and around higher education leave room for nuance and uncertainty, encouraging institutions to cultivate more curiosity, exploration, and listening than traditional predictive analytics approaches have allowed.

While the future is never completely knowable, institutions that have a panoramic view of the possibilities on the horizon will be best prepared to adapt to uncertainty.

Ways to Get Started

Through our panelist interviews and community survey, technology leaders recommended some ways institutions might develop more proactive, future-focused approaches to data for decision-making:

• Enrollments and budgets may be two areas where data are robust and reliable enough to experiment with more quantitative predictive modeling methods. ERPs and other analytics and reporting systems offer increasingly sophisticated, turnkey capabilities, including forecasting and scenario building. These may be the lowest-hanging branches for institutions looking for somewhere to start.
• We live in a volatile social and political environment, and higher education has had a particularly turbulent year. This has created a greater sense of unpredictability in otherwise reliable data sources, making accurate predictions nearly impossible. Institutions should consider trend scanning and scenario development approaches that allow for more open explorations of what may be possible in the future. The EDUCAUSE Horizon Reports, for example, encourage engagement with strategic foresight methods that can support this kind of exploration and planning.

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#9: AI-Enabled Efficiencies and Growth

Using artificial intelligence, robotic process automation, and other analytics capabilities to reduce operational costs, streamline processes, and improve strategic and business decision-making

Karen Bates, Jennifer Burns, Muhammad Hossain, John McGuthry, Jesus Ramirez, and Brandon Rich

Technology professionals consistently report that their workloads exceed capacity, their teams are understaffed, and that, despite these realities, their institution still demands they maintain the same levels of service and continue to do more with less.^Footnote19

For many institutions, there doesn't appear to be much relief on the horizon. Even if budget and staffing levels were to return to those of more prosperous days, colleges and universities would still be challenged to attract and retain talent, especially in regions where demographic, geographic, or economic barriers exist and increasingly frustrate efforts to build a thriving workforce.

It's past time, then, for institutions to find pathways for doing less with less—enabling leaders and staff to pursue their most important work in healthier, more efficient, and more meaningful ways.

AI could be a compelling solution for some institutions, offering the promise of automated administrative processes that reduce staff burden, enable faster planning and decision-making, and increase staff capacity for tasks they typically have little to no time for. The cascading benefits of automation and other analytics capabilities to institutional operations and administration could translate into significant time and cost savings and free staff capacity to explore meaningful programmatic investments and pursue professional development.

These new capabilities, however, aren't without risks and detractors. Some interpret "efficiencies" as code for "reductions in staff" that threaten the long-term job security of certain segments of the higher education workforce. And not all aspects of new AI and other solution adoptions will seem "efficient," as they will still demand significant investments in infrastructure and tooling, as well as technology staff capacity for training, services, and support.

And, as with most applications of new data-driven capabilities, privacy and security concerns remain paramount. Institutions need to be intentional about how they govern and safely implement these critical institutional functions, some of which depend on access to sensitive institutional data.

Ways to Get Started

Through our panelist interviews and community survey, technology leaders noted some ways institutions might realize opportunities for AI technologies in their work:

• A more thoughtful, collaborative change-management process for adopting AI solutions can help ensure institutions use AI not just to cut corners and complete tasks faster but to address real needs and priorities. Listening to users is key to identifying "deal-breaker" use cases (e.g., ensuring that real human contact with students is not eliminated completely). Exploring EDUCAUSE Prosci change management programs is a great way to start engaging in this process.
• Staff discomfort and frustration with using new AI capabilities can slow down or halt adoption altogether. Look for quick-win opportunities that help staff build confidence and familiarity in using these tools to support their most pressing needs. They can then expand this foundation to other use cases.

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#10: Decision-Maker Data Skills and Literacy

Enhancing the value of institutional data by training and equipping decision-makers to use and interpret it properly

Penny Evans-Plants, Adam Finkelstein, Matthew Flower, Jessie Minton, and Helen Norris

A fundamental goal of higher education is to train and equip students to process, interpret, and act on information. Critical thinking, reasoning, analysis, and informed decision-making are central to that goal—and, for many, represent the hallmarks of a successful educational journey. Likewise, the value of institutional data is strengthened when the people who use it—campus leaders, faculty, and staff—are equipped to interpret it and apply it effectively.

With an abundance of data and advanced analytics capabilities at their disposal, higher education institutions should be data literacy and decision-making exemplars for the rest of the world, embodying best practices in thoughtful analysis and sound judgment.

"We do this in our teaching as faculty members, and we absolutely do this in our research, so why would we not do this in the operations of our own institutions?" asks Adam Finkelstein, associate director of learning environments at McGill University.

Indeed, many institutions struggle with the same challenges facing the rest of the world in understanding and using data. Institutional data are fragmented and incomplete—siloed and scattered across systems. Institutional leaders and teams are stymied by barriers to data access and analysis and led astray by decisions based more on assumptions or intuition than on facts. Smaller institutions in particular report limited data and analytics support for staff, including access to training, policies and guidelines, and communities of practice.^Footnote20

Again, emerging AI capabilities can help provide quicker access to data and insights, which can be especially valuable for institutions lacking internal resources and support. And opportunities abound for institutions to combine efforts to support common needs, as demonstrated by Harvard University's Strategic Data Project and its recently established Minority Serving Institution (MSI) Data Community of Practice.

Of course, data-informed decision-making relies on more than user literacy. The data must be accurate and trustworthy, accessible to the stakeholders who need it, and supported by reliable analytics and reporting tools. But sound data governance and analytics structures can only get an institution so far. Ultimately, how individual leaders engage with these data will determine their effectiveness in positively impacting their work.

Ways to Get Started

Through our panelist interviews and community survey, technology leaders recommended a few ways institutions might support decision-maker data skills and literacy:

• Better understand the current level of data literacy and data-informed decision-making maturity at your institution. Use surveys, interviews, dashboard utilization, other behavioral data, and even informal conversations to understand stakeholders' current attitudes about using data and their needs and frustrations around accessing and interpreting data. Then, use those insights to design solutions that address those needs and frustrations.
• Establish more scalable and easily accessible data literacy training efforts for leadership and staff. Communities of practice and data champions embedded within individual departments or teams can help create peer-based spaces for learning about and engaging with data without overburdening technology and institutional research teams or intimidating potential data users who may not know where to start.
• Articulate in plain language the questions you need data to answer at your institution, and make data engagement and insights as seamless and turnkey as possible. Dashboards and other reporting tools that combine data visualizations and clear summaries of insights can help engage data users across a range of literacy levels and provide simple entry points for exploring institutional data and addressing decision-makers' most pressing data needs and opportunities.

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Making Your Next Connection

Strengthening the institution's collective will and equipping its people with the capabilities they need to thrive and be successful in their work and learning. These are the driving themes for technology leaders in the year ahead. These are also the keys to helping institutions chart a path forward in a turbulent and unpredictable world.

Thankfully, you don't have to chart these paths alone. EDUCAUSE is one of many supports at your side, and we are confident that the insights and resources offered in the 2026 EDUCAUSE Top 10 report can help you move your institution forward.

To conclude with one additional resource you might consider, the EDUCAUSE Connect platform offers more than seventy distinct professional communities you can engage with today—or anytime. You can pose questions to your peers, share ideas and resources, or simply browse through the discussions that are relevant to you and your institution.

Here are just a few  groups you might consider engaging with on EDUCAUSE Connect:

• AI
• Cybersecurity and Privacy Awareness and Education
• Data Governance Community Group
• Instructional Technologies
• IT Accessibility
• IT Procurement and Supplier Management
• IT Service Management
• Organizational Change Management
• Project Management
• Small Colleges
• Young Professionals

Perhaps the greatest resource we have is one another. The higher education technology community is a deep and vibrant wellspring of wisdom. The lessons and challenges we share with one another and the personal connections we make will serve as our greatest sources of strength on the journey ahead.

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Methodology

Our work on the EDCUAUSE Top 10 begins with a series of interviews with senior institutional leaders nominated by a Top 10 panelist from their institution. These interviews set the stage for everything that comes after, providing a rough blueprint of the trends, challenges, and opportunities that are most important to higher education right now. In each of these interviews, we ask leaders to think about where they'd like their institution to be in the next few years, what they hope to accomplish, and the position they'd like their institution to be in. We then ask them to consider the following questions:

1. What are the biggest strategic opportunities that are going to help your institution reach that ideal state?
2. What are going to be the biggest barriers to your institution in reaching that ideal state?
3. How do you see higher education transforming over the next five years or so and what impact do you think it will have on your institution?
4. What role will technology need to play in transformational change at your institution?

Following these leader interviews, we provide Top 10 panelists with a summary of the key ideas identified by leaders, from which the panelists draft a list of twenty-five different issue statements. These issue statements go through several rounds of panelist reviews and EDUCAUSE staff synthesis before they are voted on by EDUCAUSE members.

For issue voting, we disseminated a survey to the EDUCAUSE general membership through an email campaign from June 16 to 30, 2025. The survey asked respondents to rate each of the twenty-five issue statements on a scale from one (not at all important) to ten (extremely important). This survey yielded 450 complete responses, which were used in our analysis to determine our final ranking of the Top 10 issues listed in this report.

Once the Top 10 list was determined, we conducted a series of interviews with each of the Top 10 panelists to elicit additional insights on the importance and implications of each issue for higher education institutions. In each of these interviews, we asked panelists the following questions:

1. Why does this issue belong in the Top 10 this year? Why is it so important for higher education in the year ahead?
2. Can you share a story or example of how you're experiencing this issue at your institution? How do you imagine these experiences could be unique compared to other types of institutions?
3. What immediate, short-term actions do you think institutions should consider, and what skills or competencies will they need, for effectively addressing this issue?
4. Imagine we've traveled a few years into the future, and we've discovered that your institution has completely and effectively addressed this issue. Describe what that looks like.

Acknowledgments

I am deeply appreciative of the time, knowledge, and experience our panelists and their institutional leaders contributed to the Top 10 this year. You are all busy people with so many important things going on, and that you dedicated your time to giving this project its shape and life this year is not lost on me. I am so grateful to you, and I hope this report helps shine a light on all the important challenges and opportunities you helped to surface. Thank you, all!

To the EDUCAUSE member community who voted and ranked these issues, as you do every year, I so appreciate you lending your time, effort, and voices to the effort. I hope you find this report to be a faithful reflection of your experiences in your day-to-day work and a light that illuminates the path ahead of you.

There are so many EDUCAUSE staff involved in this project every year, and I'm so grateful to each person for bringing their passion and expertise to the work. Belle McDonald manages all the many moving parts of this incredibly complex project, and there would be no Top 10 without her, period, full stop. Thank you, Belle!

Jamie Reeves continues to oversee and support this project in almost too many ways to count year in and year out, and its success is a direct expression of her dedication to, passion for, and sleepless nights fretting over the work. Thank you, Jamie!

Jenay Robert, Nicole Muscanell, Kristen Gay, Jaclyn Smith, and Sean Burns, my partners in crime on the EDUCAUSE research team, have supported our data collection and analysis efforts at various points along the way, always with a careful eye to detail and rigor. Thank you, team!

Zach Peil makes everything we do at EDUCAUSE look so much better, and his visual elements and graphics in this report are simply beautiful. Thank you, Zach!

Our partners on the publications team have brought immeasurable value through their editorial support, as always. To Karen Henry, for your thorough and thoughtful guidance through the creation of this report and all the months of work leading up to it, and to Greg Dobbin, for your support with issue review and refinement—thank you both!

The fact that you're reading this report at all is thanks in no small part to the folks on our marketing and web teams, Connie Ferger, Marc Stith, and Becky Harold, who work tirelessly to make sure resources like the Top 10 are findable, consumable, and shareable. Thank you, all!

And John O'Brien and Crista Copp were always willing to step in when needed to provide feedback, offer suggestions, and help keep things on track with our larger vision for this work. Thank you, John and Crista!

Finally, to Susan Grajek, long-time EDUCAUSE Top 10 author. It will never not feel strange working on a Top 10 report without you. I've enjoyed the opportunity to both honor and build on your dedicated work over the years. Thank you, Susan!

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Appendix A: 2026 EDUCAUSE Top 10 Panelists and Institutional Leaders

Panelists

• Karen Bates, Interim Chief Digital and Information Officer, London Business School and UCISA Trustee
• Shelly Belflower, Director of IT Software Licensing and Contract Management, Weber State University
• Sean Brown, Director of Strategy and Operations for Campus Reimagined, Florida State University
• Jennifer Burns, Associate VP Information Technology, The University of British Columbia
• Didier Contis, Vice Provost for Information Technology and Chief Information Officer, University of Wisconsin-Madison
• James Crooks, Chief Information Officer, Durham University and Deputy Chair, UCISA
• Penny Evans-Plants, Chief Information Officer, Berry College
• Daniel Ewart, Vice President for Information Technology and Chief Information Officer, University of Idaho
• Adam Finkelstein, Associate Director of Learning Environments, McGill University
• Matthew Flower, Assistant Director and Head of Digital Architecture/Information Security, University of Wolverhampton and UCISA Trustee
• Isabel Gallin, Vice President of EUNIS and Relationship Manager at Karlsruhe Institute for Technology, Germany
• Justin Gatewood, Chief Information Security Officer, California Community Colleges Technology Center
• Jennifer Haas, Chief Information Officer and Vice President for Information Technology Services, Macalester College
• Muhammad Hossain, Director of Instructional Technology, Claflin University
• Frederick Kass, Associate Chief Information Officer for IT Operations, Amherst College
• Olivia Kew-Fickus, Chief Data Officer, Vanderbilt University
• Diego Leal, Executive Lead of the Digital Educational Laboratory, Universidad de Los Andes
• Hemalatha Manickavinayaham, Associate Vice President of Planning and Digital Transformation, California State University, Sacramento
• John McGuthry, Vice President and Chief Information Officer, California State Polytechnic University, Pomona
• David McMorries, Chief Information Security Officer, Oregon State University
• Jessie Minton, Vice Chancellor for Technology and Chief Information Officer, Washington University in St. Louis
• Helen Norris, Vice President and Chief Information Officer, Chapman University
• Jesus Ramirez, Director of Student Products, University of California, Los Angeles
• Brandon Rich, Director of AI Enablement, University of Notre Dame
• JT Singh, Senior Associate Vice President and Chief Information Officer, West Chester University of Pennsylvania
• Eudora Struble, Director of Technology Accessibility, Wake Forest University

Institutional Leaders

• Ebrahim Adia, Vice Chancellor, University of Wolverhampton
• Gage Averill, Provost and Vice President Academic, The University of British Columbia
• Jon Boeckenstedt, Vice Provost of Enrollment Management, Oregon State University
• Christopher Buddle, Associate Provost for Teaching and Academic Planning, McGill University
• Rick Burnette, Senior Vice Provost and Chief Strategy Officer and Institutional Data Administrator, Florida State University
• Soraya Coley, President, California State Polytechnic University, Pomona
• Shannon Cullinan, Executive Vice President, University of Notre Dame
• Dr. Michael Elliott, President, Amherst College
• Brian Erb, Chief Operating Officer and Chief Financial Officer, Berry College
• Sergei Guriev, Dean, Professor of Economics, and Chief Executive Officer, London Business School
• Jan S. Hesthaven, President, Karlsruhe Institute of Technology
• Torrey Lawrence, Provost and Executive Vice President, University of Idaho
• John Lutz, Vice Chancellor for Development and Alumni Relations, Vanderbilt University
• Steve McLaughlin, Former Provost and Executive Vice President for Academic Affairs, Georgia Institute of Technology (now President at Cooper Union)
• Karen O'Brien, Vice-Chancellor and Warden, Durham University
• Jeffery Osgood, Executive Vice President and Provost, West Chester University of Pennsylvania
• Suzanne Rivera, President, Macalester College
• Silvia Caro Spinel, Vice President of Academic Affairs, Universidad de Los Andes
• Daniele Struppa, President and Donald Bren Presidential Chair in Mathematics, Chapman University
• Frank Wada, Assistant Vice Chancellor for Academic Partnerships, University of California, Los Angeles

Appendix B: 2026 Top 10 Survey Respondent Demographics

Which of the following best describes your primary position/role at your institution?

Professional/Staff	5.6%
Manager	13.3%
Director	37.6%
Executive/VP/SVP	19.1%
C-level	24.4%
TOTAL	100.0%

What is your institution's preferred overall approach to adopting technology?

Early	38.2%
Mid	43.7%
Late	18.1%
TOTAL	100.0%

Student FTE Fall Enrollment 2023

Less than 2,000	13.1%
2,000–3,999	15.7%
4,000–7,999	13.8%
8,000–14,999	18.3%
15,000+	39.2%
TOTAL	100.0%

Carnegie Classification 2021

Associate's	12.0%
Bachelor's	8.9%
Private Doctoral	21.1%
Public Doctoral	34.4%
Private Master's	8.7%
Public Master's	11.2%
Special Focus	3.8%
TOTAL	100.0%

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Notes

1. Sara Weissman, "Education Department Moves to End Funding for Minority-Serving Institutions," Inside Higher Ed, September 11, 2025. Jump back to footnote 1 in the text.↩
2. Paul Dokecki, The Tragi-Comic Professional: Basic Considerations for Ethical Reflective-Generative Practice (Duquesne University Press, 1996). Jump back to footnote 2 in the text.↩
3. Peter Aspden, "Wynton Marsalis: How Music Makes a Difference," Financial Times, October 15, 2020. Jump back to footnote 3 in the text.↩
4. Dokecki, The Tragi-Comic Professional. Jump back to footnote 4 in the text.↩
5. Narendran Vaideeswaran, "Intro to the Principle of Least Privilege (POLP)," Crowdstrike, January 8, 2025. Jump back to footnote 5 in the text.↩
6. Lance Spitzner, "Why a Strong Security Culture?" SANS, September 20, 2021. Jump back to footnote 6 in the text.↩
7. Michael Corn and Sol Berman, "Privacy and Security: The Six Words Project," EDUCAUSE Exchange, produced by Gerry Bayne, EDUCAUSE Review, September 2, 2020. Jump back to footnote 7 in the text.↩
8. Jenay Robert and Mark McCormack, 2025 EDUCAUSE AI Landscape Study: Into the Digital AI Divide, (EDUCAUSE, February 2025). Jump back to footnote 8 in the text.↩
9. Jenay Robert, Nicole Muscanell, and Kim Arnold, 2025 EDUCAUSE Horizon Report, Data and Analytics Edition (EDUCAUSE, 2025). Jump back to footnote 9 in the text.↩
10. Nicole Muscanell, 2024 EDUCAUSE Analytics Landscape Study, (EDUCAUSE, September 2024). Jump back to footnote 10 in the text.↩
11. Jenay Robert, 2024 EDUCAUSE Horizon Action Plan: Unified Data Models, (EDUCAUSE, March 2024). Jump back to footnote 11 in the text.↩
12. Enterprise Data Leaders: Our AI Ambitions Are Stalling Out on Silos, (Reltio, December 2024). Jump back to footnote 12 in the text.↩
13. Mark McCormack, "EDUCAUSE QuickPoll Results: The Varied and Compounding Effects of Institutional Debt," EDUCAUSE Review, February 3, 2025. Jump back to footnote 13 in the text.↩
14. Ethan Mollick, "The Lazy Tyranny of the Wait Calculation," One Useful Thing (blog), January 16, 2024 Jump back to footnote 14 in the text.↩
15. Amanda Bergson-Shilcock, Roderick Taylor, with Nye Hodge, Closing the Digital Skill Divide: The Payoff for Workers, Business, and the Economy, (National Skills Coalition, February 2023). Jump back to footnote 15 in the text.↩
16. Lauren Coffey, "Digital Media Literacy Becoming a Graduation Requirement," Inside Higher Ed, March 26, 2024. Jump back to footnote 16 in the text.↩
17. Microsoft and LinkedIn, 2024 Work Trend Index Annual Report (Microsoft, May 2024), 10, 13. Jump back to footnote 17 in the text.↩
18. Creating the AI-Enabled Community College: A Road Map for Using Generative AI to Accelerate Student Success, (Achieving the Dream, July 2025). Jump back to footnote 18 in the text.↩
19. Mark McCormack, 2025 EDUCAUSE Technology Leadership Workforce in Higher Education, (EDUCAUSE, July 2025) Jump back to footnote 19 in the text.↩
20. Muscanell, 2024 EDUCAUSE Analytics Landscape Study. Jump back to footnote 20 in the text.↩

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Mark McCormack is Senior Director of Research & Insights at EDUCAUSE.