Experimentation with AI will give way to thoughtful evaluation of, and investment in, uses that help fulfill institutional missions

To paraphrase fictional chaos theorist Ian Malcolm, the world has been so preoccupied with whether we could use artificial intelligence (AI) that we haven't stopped to ask whether we should or, at the very least, why and how we should. In the year ahead, higher education institutions will be prioritizing more purposeful adoption of these capabilities, moving beyond the "could" and focusing on the "should." Of course, there are countless promising possibilities for these new AI capabilities, but there are just as many problematic and unhelpful ones. The challenge for institutions is to consistently and systematically determine where potential use cases fall on that spectrum, anchoring those determinations in both the core values of the institution and its biggest areas of need. It requires introspection, practicality, and a commitment to preserving the heart of the institution while making the most of a generational opportunity for innovation and growth.

Students need durable, transferable learning experiences that emphasize how they think, adapt, and apply skills in a rapidly changing world

Everything everywhere seems to be in flux all at once, and institutions are wrestling with what it means to prepare students for futures that feel increasingly opaque. Some institutions are focused on helping students "learn to learn," making a curricular pivot toward more flexible and durable knowledge and skills and placing greater emphasis on how students learn than on what they learn. Merging liberal arts sensibilities with practical workforce needs can help these institutions offer a more well-rounded and future-ready educational experience. Our rapidly evolving AI and data capabilities, in particular, provide opportunities to challenge student reasoning and can help instructors foster qualities like critical thinking, ethical judgment, and effective collaboration with emerging technologies that will be essential in an ever-changing world.

Security teams can support an institutional culture that values both clear and transparent security boundaries and individual responsibility and discretion

It's always surprising when cybersecurity isn't the number one issue on the EDUCAUSE Top 10. Nevertheless, recent headlines underscore the need for continued vigilance and support. Technology leaders are rethinking the security guidelines and resources they provide to staff, faculty, and students, as well as the most effective ways to communicate them. In particular, a focus on more agile, collaborative, and decentralized approaches can better position the institution and its people—its greatest vulnerability—to navigate a rapidly evolving security landscape. It's no simple task, and it will require an institutional culture of communication, mutual understanding, and trust.

AI governance and management must balance clear guardrails, institutional values, and standards with spaces for autonomy and experimentation in how AI is adopted and used

Before determining where AI adds value, the higher education community has to understand what AI can do. Institutions need safe spaces to experiment with and pilot emerging AI use cases that are untethered from the need for absolute certainty about their feasibility and value. Innovation is a hallmark of higher education, and without experimentation spaces, capacity for growth and forward progress will be diminished. But innovating with powerful tools like AI is risky, and boundaries and safeguards must be in place. Some technology teams are focused on building sandboxes, walled gardens, and other secure structures so experimentation can happen safely without putting the institution, its data, or its people at risk. For institutions without the resources to build these structures themselves, consortia and other shared resource networks offer access to pre-made, lower-cost experimentation spaces.

Usability, accessibility, and the human experience are vital aspects in the design of systems and tools

Compliance is merely the starting point for an institution's journey toward digital equity and access. Certainly, efforts to comply with federal, state, and accreditation requirements are critical and go a long way toward improving technology experiences at the institution. Beyond compliance, though, deeper outreach and listening across campus can help technology teams gain a deeper understanding of the diverse technology experiences and needs of the populations they serve and identify opportunities for better, more equitable design. Through existing frameworks such as Universal Design for Learning (UDL), colleges and universities can proactively anticipate and prepare for user and learner variability and needs. In these ways, even as technology adoption and change continue to accelerate, institutions can evolve their technology systems beyond mere checklists into fuller, more adaptive expressions of the institution's core values.

Exploring new, innovative ways of working will help technology teams sustain and evolve their services and impact in an uncertain future

Only three things in life are certain: death, taxes, and higher education technology teams being expected to do more with less. Indeed, technology leaders and teams are perennially overextended and burned out trying to manage work that exceeds available resources.^* These challenges are increasingly exacerbated by an enrollment cliff that is no longer just a prediction and by budgeting and hiring challenges that seem to be intensifying for many institutions. Technology teams are facing these realities head-on by exploring innovative ways to organize and do their work, including new administrative uses for AI and cross-campus partnerships that pool resources, expertise, and capacity. It's time to leave the untenable position of "doing more with less" behind and embrace the potential of "doing differently."

^* Nicole Muscanell, 2026 EDUCAUSE Workforce Report: Can Higher Education Break the Cycle of Reactivity? EDUCAUSE, July 2026).

The ability to scale and evolve data and analytics capabilities alongside rapidly evolving technologies depends on a foundation of trustworthy and adaptive data governance

Data governance remains essential to institutional data strategy, even as many colleges and universities struggle to implement and manage it effectively amid shifting institutional priorities and resource constraints. As institutions adopt emerging AI technologies and analytics capabilities, the need for strong data governance and trustworthy data becomes even more urgent. In the year ahead, technology teams will be focused on implementing approaches to data governance that can keep up with the rapidly evolving data environment. For many institutions, the solution will be to design approaches that balance collective commitment with local autonomy, focusing on de-siloing and standardizing data sources and analytics tools while equipping local users to become more data literate and self-sufficient.

Institutional leaders need data and analytics to understand and respond to demographic and other factors impacting enrollments

The enrollment cliff is no longer hypothetical. For many institutions, the cliff is here, now, reflected in declining student numbers and diminishing resources. Senior leaders and enrollment management teams need to better understand which students they're losing or could lose, which students they're gaining or could gain, and how those demographic shifts impact the mission, programs, and operations of the institution. Mature data and analytics capabilities can help build that understanding and enable leaders to make proactive decisions. Campus technology teams are critical partners in establishing the trusted data foundations that support those capabilities.

Technology teams must support AI training and resources that accommodate different levels of stakeholder familiarity, comfort, and readiness

For many of us, feelings about AI adoption, its value, its costs, and its environmental and social impacts are very complex. Depending on the use case, those feelings can range from exuberant to terrified. Institutional change management with AI, then, must therefore proceed with extra measures of human connection and collaboration and without the operating assumption that everyone is ready and willing to move forward. At a minimum, there may be opportunities to tailor AI-related resources and support to accommodate a range of preferences and levels of readiness and comfort. Perhaps even more radically, technology leaders have the opportunity to listen to skeptics and resistors rather than dismiss them, thoughtfully and collaboratively interrogate new technologies, and ensure that any steps forward are taken as a unified institutional community through shared commitments, ownership, and understanding. After all, higher education is built on curiosity, inquiry, and collaboration—qualities that offer a path forward to responsible and supportive AI adoption.

Simplified, integrated technology systems and processes provide easier student, faculty, and staff access to important services and support

Campus technology users want their technology experiences to be simple and uncomplicated. Students, for example, report that streamlined, consistent technology implementations across their campus and course experiences are important for smoothing their educational journey.^* Technology teams are therefore focusing on reducing their technology clutter and technical debt, integrating disjointed systems and data across the institution, and listening to and addressing users' most pressing pain points and needs. The potential result is a digital ecosystem that meets modern user expectations for ease, enabling staff, faculty, and students to focus on the work that matters most.

^*Kristen Gay and Nicole Muscanell, 2026 EDUCAUSE Students and Technology Report: Steady Through Change (EDUCAUSE, March 2026).