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AI use for work is already common across higher education, but institutional guidance and support have not kept pace. In EDUCAUSE’s 2026 report, 94% of surveyed higher-ed professionals said they had used AI tools for work in the prior six months, while only 54% knew of policies or guidelines meant to guide that use. For CIOs, the job is therefore bigger than approving software: it is to help the institution identify valuable uses, set clear rules, protect data, build staff capability, and keep governance current as tools change.
How colleges and universities are using AI
The evidence points to work across the institution, not just student use in coursework. EDUCAUSE’s 2026 report covers AI software and software features broadly, rather than generative AI alone. Respondents identified opportunities in automating repetitive processes, offloading administrative burdens, and analyzing large datasets.
| Reported opportunity | Share of EDUCAUSE survey respondents |
|---|---|
| Automating repetitive processes | 70% — EDUCAUSE, 2026 |
| Offloading administrative burdens | 65% — EDUCAUSE, 2026 |
| Analyzing large datasets | 60% — EDUCAUSE, 2026 |
These are respondent-reported opportunity areas, not measured proof that a particular tool saves time or improves outcomes. They do, however, show why institutional AI planning touches administrative operations, data work, teaching, and research as well as classroom practice.
Why AI strategy is now a CIO concern
AI adoption has moved beyond isolated experimentation because employees are already using tools, including tools their institutions did not supply. In EDUCAUSE’s 2026 survey, 56% of respondents said they had used work-related AI tools not provided by their institution. That figure does not establish misuse; it signals that institutions may need to understand what is already in use and offer a safe, supported path where appropriate.
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The strategic challenge is intensified by uneven awareness. Having a policy is not the same as users knowing it exists or understanding what it permits. EDUCAUSE’s 2026 findings make that distinction visible: only 54% of respondents knew of relevant guidance, despite widespread work use. A CIO can help close that gap by treating communication and workforce support as operational parts of governance, not as afterthoughts once a policy is approved.
The barriers CIOs need to address
Respondents to EDUCAUSE’s 2026 survey most often named the pace of change, insufficient AI expertise, and a lack of best practices as challenges. Time to learn and the range of risks were also frequently cited.
| Reported challenge | Share of respondents |
|---|---|
| Pace of change | 60% — EDUCAUSE, 2026 |
| Lack of AI expertise | 55% — EDUCAUSE, 2026 |
| Lack of best practices | 48% — EDUCAUSE, 2026 |
| Limited time to learn AI skills | 46% — EDUCAUSE, 2026 |
| Number of AI risks | 41% — EDUCAUSE, 2026 |
These barriers call for a repeatable institutional process rather than a one-time announcement. Product capabilities and terms can change; local expertise may be uneven; and different use cases carry different risks. Governance must be understandable enough for employees to follow and flexible enough to be revisited.
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How to govern and procure AI tools
Procurement is one place where strategy becomes concrete. EDUCAUSE’s May 2025 QuickPoll found that respondents most often selected keeping up with product change and insufficient institutional AI governance as procurement challenges. This was a smaller, less formal poll than EDUCAUSE’s longer research surveys: it drew 270 responses from member groups between May 12 and 14, 2025, and its results should not be generalized to every campus.
| Procurement challenge | Share of QuickPoll respondents |
|---|---|
| Keeping up with product change | 45% — EDUCAUSE, 2025 QuickPoll |
| Insufficient institutional AI governance | 40% — EDUCAUSE, 2025 QuickPoll |
For tools that handle institutional work, review the actual use case and the product’s current data practices, contract language, and capabilities. EDUCAUSE’s 2025 QuickPoll respondents most commonly selected institutional data security and compliance with laws and regulations governing data as review factors; whether institutional data are used to train AI models was also a frequent concern.
| Procurement review factor | Share of QuickPoll respondents |
|---|---|
| Institutional data security | 86% — EDUCAUSE, 2025 QuickPoll |
| Compliance with laws and regulations governing data | 86% — EDUCAUSE, 2025 QuickPoll |
| Whether institutional data are used to train AI models | 77% — EDUCAUSE, 2025 QuickPoll |
Those factors are a starting point, not a universal pass/fail checklist. Local review should also consider accessibility, intellectual property and copyright, accuracy and reliability for the task, contract transparency, integration and support, total cost, and whether a human review path is available for consequential decisions. Compare options against the same use-case requirements rather than selecting a vendor based on broad claims about AI.
Review should not end at purchase. EDUCAUSE’s QuickPoll noted that fewer than half of respondents whose institutions had AI-related procurement processes said products were reviewed on an ongoing basis after initial procurement. A tool’s functions, data handling, or terms may change, so institutions need a way to revisit approvals and respond to material changes.
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Technology teams and cybersecurity or data-privacy staff are essential to procurement review, but they cannot represent every affected interest. EDUCAUSE’s 2025 procurement poll found teaching and learning professionals were included less often, even as AI use affects teaching and learning. EDUCAUSE’s policy discussion also points to voices that can be missed, including faculty, accessibility services, human resources, counseling, and groups supporting minoritized students.
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Include stakeholders according to the decision’s impact. A tool used to analyze research data may require different expertise from a system used in student support or employee workflows. Involving the people who understand the work, the data, and the affected communities can reveal risks and constraints that a technical review alone will miss.
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AI policy should not be reduced to a ban-or-permit statement about classroom use. EDUCAUSE’s 2024 action-plan framework spans data governance, training and infrastructure, academic integrity, assessment, student communication, competencies, bias, and accessibility. That breadth reflects the fact that AI affects institutional systems and work practices as well as teaching.
Make guidance usable by connecting institutional principles to practical decisions: which tools are supported, what information must not be entered, when human review is required, how teaching staff should communicate expectations, and where employees can get help. The 2025 EDUCAUSE Review policy discussion reported that fewer than 40% of surveyed institutions had AI acceptable-use policies, citing the 2025 AI Landscape Study. This is a secondary report of that study’s finding, not a measure of policy quality or employee awareness.
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Support the workforce and measure whether AI helps
Training is necessary, but it should be paired with realistic expectations about time and responsibility. EDUCAUSE recommends in-house training or access to third-party professional development. Institutions can also clarify AI-related responsibilities in job descriptions so that added work is recognized rather than quietly layered onto existing workloads.
Define an outcome before deploying a tool: for example, whether a workflow becomes faster, whether analysis improves, or whether staff capacity is freed for higher-value work. Then weigh the result against cost, workload, accuracy, and risk. In EDUCAUSE’s 2026 survey, just 13% of respondents said their institutions measured return on investment for work-related AI tools. Without a defined measure, adoption can become an activity rather than an evaluated institutional investment.
What the survey findings can—and cannot—show
EDUCAUSE’s 2026 report, The Impact of AI on Work in Higher Education, was published January 12, 2026. Its survey was conducted with AIR, NACUBO, and CUPA-HR from September 29 to October 13, 2025, and 1,960 responses met the report’s inclusion criteria. Respondents worked at higher education institutions and answered core questions about role, responsibility, attitude, and policy awareness.
The results describe those respondents; they are not a census of all higher education employees and do not establish that AI caused a particular outcome. EDUCAUSE notes standard survey limitations, including a constrained sample frame and response biases. The 2025 AI Landscape Study also surveyed institutional strategy and leadership, policy, use cases, workforce, and the digital divide; its survey ran November 4–18, 2024. EDUCAUSE says access to the full report is reserved for members.
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