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

Our panelists and survey respondents highlighted several other approaches they're using to determine the value of AI at their institutions:

• Pilot a smaller set of targeted use cases. Institutions don't have to go all-in on system-wide AI adoption. Instead, they may be more comfortable identifying pockets of use aligned with the institution's biggest strategic priorities, where the benefits of AI can be more easily isolated and tested. One survey respondent, for example, shared that they are focusing "targeted pilot programs on high-friction operational areas . . . [where] we aim to definitively measure the impact of these technologies."
• Develop rubrics or frameworks that can be distributed across campus and help technology users determine when and for what purposes AI is a feasible and helpful solution. These decision supports may include considerations such as mission or goal alignment, whether the use case will truly save time and effort or improve a task or process, the funds and other resources required to support it, and whether it could introduce significant risks or harms.

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

Our panelists and survey respondents highlighted several other approaches to future-proofing students:

• Seek out opportunities to partner with academic leadership and student-support teams to incorporate technology engagement into students' discipline-specific curricula and campus experiences. As one survey respondent shared, "I see our role as helping academic and student support teams embed durable capabilities into the student experience, including digital fluency, data literacy, ethical AI use, collaboration, adaptability, and problem-solving. This work includes supporting faculty with learning technologies, AI guidance, applied learning environments, and tools that help students practice authentic work."
• Evaluate and evolve campus-wide technology training programs to focus not only on raising staff, faculty, and student awareness of the specific tools that scaffold their campus experiences, but also on helping them become more confident and agile users of those tools. Success for these programs should be measured not by how comfortable a user feels with any one device or system, but by how comfortable they are pivoting across a range of tools, digital environments, and circumstances. As one survey respondent said, "Technology skills alone are no longer sufficient. We are supporting initiatives that help students develop adaptability, critical thinking, digital fluency, and responsible AI usage."

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

Our panelists and survey respondents surfaced a few ideas for supporting institutional cybersecurity efforts:

• Focus on strengthening the presence of security teams. When security teams are positioned as partners and collaborators who listen to and understand the needs and challenges of key stakeholders, they are more likely to be viewed as a trusted source of support. As Josh Callahan, CISO at California State University, put it, "We have to build bridges, build relationships, meet people where they are, and really focus on enabling the business functions and teaching and learning functions that need to happen."
• Training and awareness campaigns will remain a priority for security teams in the coming year, but many institutions are exploring ways to evolve these programs and make them more impactful for users. Making these programs practical and approachable, even incorporating play and humor, can help make the lessons "stick." Most importantly, emphasizing the relevance and "why" of security practices and policies can improve user engagement and long-term buy-in.

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

Our panelists and survey respondents shared how they're balancing AI standards with safe institutional spaces for autonomy and experimentation:

• Involve stakeholders and perspectives across the institution to help design and implement responsive, supportive AI governance structures. Shared ownership of this work can help ensure AI adoption and use are grounded in a clear understanding of ethical boundaries and the unique needs of individual stakeholders. One survey respondent, for example, described their work in this area: "Our strategy includes an AI Steering Committee that crosses divisional boundaries and explicit leadership for teaching and learning, research, and operations. This shared governance approach is working well for us."
• Involve faculty and academic leadership in establishing clear expectations for when and how to engage in AI-related practices and experimentation, ensuring accountability while maintaining space for faculty-level decision-making. Some institutions support this through clear documentation requirements (e.g., syllabi statements and faculty AI use disclosure statements). These requirements preserve faculty autonomy in determining where the opportunities for AI might be while also ensuring those pursuits are documented, visible to, and monitored by the appropriate security and governance bodies.

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

Our panelists and survey respondents provided insights into their efforts to design more human-centric technology experiences:

• Human-centered design efforts require actual humans in the loop of technology procurement, development, and implementation decisions. Technology leaders and teams are focused on processes that incorporate direct conversation and input from across their campuses, whether those conversations include formal listening sessions and research or more casual community experiences. As one survey respondent shared, "I am spending more money on food these days to host casual environments that feel like luncheons with work time. That's where we demo the technology and invite people to connect over croissants while talking about the possibilities of the tools."
• For many technology leaders and teams, compliance with federal, state, and accreditation regulations and requirements is a critical driver and enabler of institution-wide review and improvement of technology accessibility and usability. These requirements serve as a sufficient "stick" for starting important conversations, and cross-campus steering bodies can help ensure consistent practices across the institution and gain buy-in from senior leadership and key stakeholders because of these efforts.

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

Our panelists and survey respondents highlighted how they're working to evaluate and address the constraints they're facing at their institution:

• Some technology teams are pushing back against the assumption that current team resources and processes are already maximized, leaving no room to work better and smarter. This year, technology leaders and teams are evaluating opportunities for greater sustainability that already exist and are available to them now. As one survey respondent said, "Innovation often means making better use of the tools, data, and partnerships we already have instead of starting large projects that may be difficult to maintain."
• AI and modern automation solutions are a potential game changer for many technology teams in this area, enabling efficiencies across time-consuming administrative tasks and helping reduce redundancies in processes and workflows. This opportunity demands careful consideration, though, and technology teams are focused on outlining and documenting clear guidelines for when and how AI and automation can ease resource constraints and free up staff time.

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

Our panelists and survey respondents reflected on the challenges and opportunities they are currently facing with data governance:

• Many institutions remain siloed in their data-related practices and processes, which limits their ability to use data consistently and adapt to changing needs. For some technology teams, the task ahead is to de-silo the institution by improving cross-campus communication around data availability and use and by establishing data governance committees that include representatives from across the campus community. One of our survey respondents shared an example of what this looks like at their institution: "We have created a high-level task force that is made up of the CTO, CFO/COO, Chief Librarian/Head of Research Hub, Director of Faculty Affairs, and two representatives from other departments in their role as data analysts."
• Although data governance is a familiar topic, some institutions are far behind in standing up a proper data governance program, and others don't have one at all. For these institutions, the challenge this year is figuring out where to start. It can be daunting, especially with the rapid expansion of available data and analytics tools across the institution. Heather Woods, CIO at Smith College, advised, "Take it one step at a time. This is a really big issue, and we can get stuck trying to come up with a comprehensive plan before we start taking action. So, pick a direction and keep moving forward."

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

Our panelists and survey respondents reflected on their current efforts in mapping and understanding enrollment trends:

• Hard numbers from various institutional data sources tell only part of the story of student engagement and enrollment trends. Reviewing input from stakeholders across campus departments and academic units can help data users and decision-makers better understand what's behind the numbers and add nuance where the numbers can't. As one survey respondent explained, "Measurements and metrics need to reflect these diverse internal perspectives as well as the diverse needs of our learners."
• Technology teams are partnering with enrollment-management colleagues to adopt more sophisticated AI, machine learning, and predictive analytics approaches. Robust CRM tools, for example, can provide more sophisticated views into the institution's data, and some technology leaders said they are planning to update their current CRM or invest in a new system in the coming year. One survey respondent shared, "Changing CRMs will impact our ability to monitor enrollment changes. We plan to use the new CRM to better track trends."

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

Our panelists and survey respondents shared insights about how they're working to understand and address the various levels of AI engagement across their campuses:

• Some technology teams are beginning by assessing their campus community's adoption of and comfort with using AI tools. Through surveys, informal listening sessions, and tool adoption/behavioral data, they are identifying gaps, challenges, and opportunities to help shape future AI training and education efforts. One survey respondent noted, for example, "[We're] planning to synthesize our internal climate survey and take QR assessments across the campus to formalize our stakeholder level of interest and possible projects to enhance AI."
• For technology teams with the resources to support it, customized AI training and education efforts can be more responsive to the needs of specific roles, departments, and individuals across the campus. For example, some technology teams are offering tailored training for administrative, academic, and research staff and providing AI adoption recommendations to fit those particular use cases. One survey respondent said, "The most effective AI support will not be one-size-fits-all. It will need to be contextual, approachable, and connected to real work."

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

Our panelists and survey respondents highlighted other efforts to reduce technology friction at their institutions:

• Students want consistent tools across their course experiences and feel frustrated when they have to toggle between different tools from one course to another. For some technology teams, the task ahead is to support more consistent tool and system use across academic departments and course experiences for students, as well as in faculty and staff work environments.
• Some institutions are vetting or implementing new ERPs to integrate their major administrative systems, effectively reducing the number of systems needed to perform common administrative tasks. Beyond ERPs, technology teams are focused more generally on integrating existing tools and systems and on more effectively vetting potential new tools for interoperability. One survey respondent said that "demanding integration assurances" from partners will be essential before purchasing and implementing new solutions.