A Corporate Perspective on the 2026 EDUCAUSE Top 10: Gartner

From your perspective, which one or two of the 2026 EDUCAUSE Top 10 issues will have the greatest impact on higher education CIOs?

Most of the issues cluster around two primary themes: (1) the opportunities and risks presented by artificial intelligence (AI), and (2) the importance of data as a strategic asset and our ability to analyze it and make proactive, data-driven decisions that ultimately deliver results for higher education. These results could include gaining business model efficiencies and improving the overall results with students, teaching and learning, and research. What's also interesting is that these two themes are interconnected because data is the foundation that literally fuels AI. So, to tackle AI issues, we have to tackle the data issues.

If I had to pick the most important issue, the one that jumped out at me is issue #2, The Human Edge of AI, which you have described as empowering students, faculty, and staff to engage with artificial intelligence tools critically, creatively, and safely.

That really captures the situation because the advances that AI can bring are so profound that we can't afford to get it wrong. We must find a way to harness the power of this technology while mitigating its risks. This requires us to simultaneously resist our fears and temper our excitement. We must bring AI to bear on our most difficult challenges, such as enhancing the quality of student research and its outcomes and improving the safety of our institutions against cybersecurity risks, which are reflected in issues #9 (AI-Enabled Efficiencies and Growth) and #1 (Collaborative Cybersecurity), respectively. AI can also really be helpful in preparing Technology Literacy for the Future Workforce, which emerged as issue #7. When I think about the top-level mission of higher education, it's not just about educating individual students, which is our primary teaching mission; it's also about our role in the discovery of knowledge and our quest to expand the body of knowledge, which is at the heart of our research mission. Ultimately, we hope to improve society, which we capture at many institutions in the service mission. And we really need to be thinking about data and the use cases for AI across that entire mission spectrum.

Can you share a story or example of how you're experiencing one of the Top 10 issues when you're working with an institution?

It's probably not a surprise to EDUCAUSE members that our most well-read Gartner Education research focuses on AI. We've developed an AI use-case assessment and prioritization prism, and we recently published a popular roadmap to help higher education CIOs accelerate their AI strategy development. We've developed these specific roadmaps for fifteen industries, but the interest in the higher education roadmap is much greater than the roadmaps for the other fourteen industries. This tells me that higher education CIOs globally are ready to embark on this journey to a more formal AI strategy for their institutions.

We take inquiry calls with our clients, and we speak to them at various events, and many of them tell us that they're ready to move beyond AI pilots. They want to move beyond the hype. There is still a lot of hype, but there's also still a lot of trepidation. So, they're looking for something to help them identify AI initiatives that will really bring value to the institution. We're getting a lot of questions around how to develop an AI strategy, when to invest in AI tools and initiatives, and how to secure successful outcomes with this disruptive technology.

I think what's different with AI is that it's rare for a technology to emerge and change things so profoundly that we have to develop a different way of thinking about how we implement and leverage it. I think people have gotten their feet wet with some of the technology, and now they're realizing that they need to be more formal with their strategy and plans—in part because everybody across the entire institution is asking for this.

We just had a meeting with some of our account teams and executive partners that serve the higher education community across Europe, the Middle East, and North America. They said that one of the things that's different about AI is that everybody on campus—from the deans to the president to the person manning the help desk—is asking about it. Because folks from across the institution are so interested in this, it calls for a more deliberate and organized approach.

What immediate or short-term actions should institutions consider for effectively addressing some of the Top 10 issues?

One of the immediate short-term actions institutions should consider—and that most institutions don't yet have—is an enterprise AI strategy. At most institutions, AI adoption is still quite immature. Sure, we have early adopters, and often those tend to be institutions that have the resources needed to move forward. But what most institutions will need to do is carefully identify AI use cases on campus, evaluate the best-suited technologies to meet those needs, and consider how to deploy them to achieve a return on investment. These steps are essential to realizing and maximizing the business value of AI and tempering excitement—something that touches on issue, #6, Measured Approaches to New Technologies. And this doesn't just apply to AI.

AI strategy should be developed as a foundation for moving forward with any sort of campus-wide initiative. At Gartner, we define the AI strategy as setting the vision and the goals for what your institution wants to achieve with AI in specific alignment with the individual business strategy at your institution. So, it's not about being like everybody else—it's about figuring out how to use this technology to differentiate your institution and deliver the things that make your institution special.

It also means you need to think about the external trends and the competitive environment that you're in and experiencing, and the specific business priorities your institution has. One institution might prioritize enrollment challenges, and another institution might prioritize graduate programs or professional schools. The AI vision will really be the heart of defining what your ambition is with AI, and it will also—or at least it should—help determine the pace of adoption at your institution. Are you going to be an early adopter? Are you going to be a fast follower? Are you going to be an institution that takes more of a wait-and-see approach with AI and the whole ecosystem of AI tools?

The strategy is also about articulating the plan and establishing a communication protocol around AI to make sure that it is deployed in a way that the whole campus community understands. Everyone at the institution should understand the direction so they can align with the goals and timeline and recognize their role in the broader effort. And then once that strategy is developed, it feeds into the other steps of the roadmap. Some of the steps that we see the early adopters moving into right now are things like defining the AI organization. Who does AI on your campus? Are you going to have a chief AI officer who establishes AI governance and even determines what we'd call the AI engineering framework? What is the technology you're going to use, and which vendors and products are you going to use? And then back to the data: What data is ready for it? Where is your data readiness? These are the short-term actions that we're seeing institutions take depending on where they are on that maturity scale.

AI data readiness is key because data is the fuel or the coal that's being put into the AI oven, so to speak. And if the data is bad, you're not going to be successful. So, there's a lot that can be done to get your data in order before you engage in this work simultaneously with other initiatives. The CIO and the IT leaders on a campus are often the most knowledgeable about how good the data is, where it isn't good, and why that is the case. They're often the ones who have the most overall knowledge of where the data is sourced, and they're typically also aware of data governance—where it is working and where it is not. So, they are key to helping the senior-most leadership at the institution understand whether the data is ready and, if not, what needs to be done to get it in shape.

What skills or competencies will institutions need to effectively address the Top 10 issues?

It is obvious to me that these issues touch on the need for both data literacy and AI literacy. And those issues are not only for the faculty and staff within an institution—they're essential skills for us to teach our students. We need to teach them how to use these skills when they leave our institutions and enter the workforce. These issues are specific to every enterprise and will be necessary skills for our graduates to be effective as members of the future workforce and as industry leaders. Data and analytics are everywhere. We've known that for a while, but now we have this AI-is-everywhere situation. And while data and analytics have this enormous potential, their ability to generate value is very much connected to the human ability to understand them and use them effectively.

At Gartner, we define data literacy as the ability to read, write, and communicate about data in a context. In this case, the context is your institution or the business practices within your institution. It requires a clear understanding of what the data source is or was, and what the constructs are. And it really goes beyond understanding the data elements themselves to include understanding the analytical methods that have been brought to bear on them (or can be) and the techniques that can be applied to the data. And when people are data literate, they can correctly identify the right use cases for data application, and that is how you get to business value and outcomes. It sounds easy, but it's very difficult. I'm sure many EDUCAUSE members can relate to the difficulty of defining what an "active student" is and the many definitions of that they may have on their own campus.

So, it helps to remember that even something simple and theoretically concrete can be quite complex. On the other hand, there is AI literacy, which is a much less mature discipline than data literacy. Gartner's definition of AI literacy focuses on being able to effectively and responsibly use AI. It may be in a business context, in the context of your whole institution, or even in the context of an academic discipline, but understanding the fundamental principles of AI and how to apply them is rapidly becoming as important as understanding basic math. Think about when word processing and spreadsheet software were introduced and how quickly we moved to a place where everyone needed to know how to use these tools. That's where we're going quite rapidly with AI.

Of course, there's going to be various depths of knowledge that different roles within an institution need, but there's also going to be a basic, foundational level of knowledge that will be required for all roles. Generative AI, which is likely to have an even more profound impact, will have special applications in certain areas of the institution. For example, I'm working with some research institutions that are wondering whether faculty have the foundational AI knowledge they need or should have to use GenAI ethically and responsibly and do things with it such as automate various research activities that were previously considered labor-intensive for human work. Or faculty may be using AI to complete tasks that humans couldn't realistically accomplish in the past due to the sheer volume of data.

I wouldn't consider an enterprise-wide AI literacy training initiative without having a realistic assessment of where your workforce stands with data and AI literacy. Consider your readiness to support ongoing training in both of these areas because that's what's going to be needed—this won't be a one-and-done thing.

How do you see the 2026 Top 10 issues changing the relationship between technology providers and the higher education community in the future?

The short answer is that I think it's going to get quite complicated. Higher education is going to be increasingly offered AI-enhanced technologies, particularly those that will be embedded in cloud solutions for campuses. This will include things such as the ERP, the SIS, the CRM, the LMS, and a whole host of other core systems. The possibilities are almost endless. And GenAI, and even agentic AI, which is rapidly heading our way, are expected to have a major impact on things like ERP applications over the next few years.

These advances could create some significant changes in the vendor landscape. We may see new vendors becoming more dominant in our industry, including some we haven't encountered before. I think it's also going to create a lot of confusion about what AI to use, when and how one AI from a vendor should be used, and how that AI should interact with an AI function provided by another vendor. It reminds me of when portals first came out. Every product came with its own portal, and then we had the portal wars—which portal do we use? This portal or that portal? This is going to be like the portal wars on steroids, and that's why I say it's going to get more complicated.

We know vendors are racing to adopt some form of AI into their products, but what's not clear yet is how they're going to monetize those AI investments. We need to think about how colleges and universities are actually going to be able to adopt this technology and afford it. And that's going to require a very careful evaluation of investment decisions. This takes us back to issue #6, Measured Approaches to New Technologies, which you defined as making better technology investment decisions and choosing what not to invest in. That's going to be a very important skill—the ability to be discriminating and choosy about what you're going to do because you probably won't be able to afford to do everything. Imagine walking around Times Square in New York City with all the big signs and lights blinking at you. It's going to be important for you to understand the path you're taking.

We are seeing that there's growth in all types of partnerships that folks are considering. We're getting numerous inquiries from our clients about the different types of partnerships they could form with vendors—not only with technology vendors but with industry partners and players that may employ future graduates. We've long had partnerships on the research side where we've looked at, for example, a pharmaceutical company that partners with the medical school of an institution and does research, but we're seeing things move beyond research and the traditional sort of partnerships. These new types of partnerships will provide opportunities for students to get real-life experience with the technologies they will use and environments they will navigate when they leave the institution. It's an exciting time, and I think the possibilities are endless in terms of how these partnerships could be constructed. Interest in partnerships is not just coming from institutions but from industry players as well. We've seen interest from a range of industries, from manufacturing to technology and even agriculture. All kinds of industries are expressing interest in exploring how the academy can connect with them and create value—a one-plus-one-equals-three situation.

When I looked back at the Top 10 issues from last year, I realized that we seemed a bit pessimistic—a natural sort of trough in our industry. In reflecting on the 2026 Top 10 issues and talking through them, I honestly feel that we're in a more optimistic place. We're starting to move forward with tools, techniques, and abilities that we've never had before to solve some of the big issues that underlie the higher education business model. These new technologies are enabling us to achieve our research mission, our service mission, and our teaching and learning mission. Despite what we may read and hear each day, I'm very optimistic about the future of higher education.

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