AI is no longer a “pilot project” living in one office with a brave intern and a lot of hope. It’s showing up everywhere work happens: agendas, advising notes, curricula, web pages, policies, procurement, and planning. The trick is to treat AI like other campus infrastructure—governed, documented, and focused on real bottlenecks, not on novelty.
Twenty-Four Ways Institutions Can Use AI Now
Presidents, Chancellors, and Cabinet Leaders
1. Turn board packets into decision briefs. Use AI to summarize long PDFs and flag decisions, risks, and open questions, then have a human verify key claims before it hits trustees. Governance and human accountability are core themes in the National Institute of Standards and Technology’s (NIST) framework.
2. Run scenario planning faster. Ask AI to generate enrollment, staffing, and budget “what-ifs,” then send the assumptions to IR/finance for validation.
3. Create a one-page “AI rules of the road.” Make it plain-English: approved tools, banned data, disclosure expectations, who to call. EDUCAUSE recommends institution-wide policies and guidelines that span operations and pedagogy.
4. Accelerate strategic planning inputs. Use AI to cluster themes from listening tours, surveys, and open-ended comments, then use leaders’ judgment to set priorities.
5. Shrink the speechwriting grind. Draft remarks in your voice from bullet points, then edit for accuracy, tone, and institutional positioning.
Provosts, Deans, and Other Academic Leaders
6. Speed up program review prep. Summarize assessment reports, accreditation language, and labor-market inputs into a single comparative memo, then verify sources and numbers.
7. Build “policy-to-practice” toolkits. Convert new guidance—AI, accessibility, research compliance—into checklists, FAQ pages, and training prompts for chairs and faculty.
8. Standardize syllabi and course shells. Use AI to generate consistent course policies, learning-outcome language, and student support links across departments, with faculty control of content.
9. Triage email and meeting overload. Draft replies, agendas, and follow-up task lists, then review. EDUCAUSE’s 2026 work-focused research underscores how broadly AI is touching day-to-day higher education work.
10. Improve internal communications. Rewrite dense announcements into versions for faculty, staff, students, and families, including translated drafts when appropriate.
Faculty and Instructional Teams
11. Design assignments that “teach with AI,” not “fight AI.” Generate alternative prompts, scaffolding steps, and rubrics that emphasize process, reflection, and source-grounding, then pilot and revise. UNESCO’s guidance calls for human-centered, age-appropriate pedagogical design and validation.
12. Create formative feedback faster. Use AI for first-pass comments on structure, clarity, and argument flow, then add your expertise and final grading judgment.
13. Generate practice questions and retrieval activities. Create low-stakes quizzes and working examples aligned to course outcomes, then check for correctness.
14. Differentiate instruction without multiplying prep time. Draft explanations at different levels—intro, intermediate, advanced—and for different modalities: text, audio script, discussion prompt.
15. Support research workflows. Summarize articles, compare methods sections, and draft literature-map outlines while maintaining clear boundaries around copyrighted content and confidential data.
Student Affairs, Advising, and Student Success
16. Draft advising follow-ups and action plans. Turn session notes into a structured email covering next steps, deadlines, campus resources, and who to contact. Keep identifiable student data out of unapproved tools and align practice with FERPA expectations.
17. Improve self-service answers. Use AI to rewrite policy pages into plain-English FAQs—financial aid, registration, conduct, housing—then have the policy owner certify accuracy.
18. Triage service tickets. Auto-classify incoming requests across IT, registrar, counseling referrals, and facilities, and suggest routing and reply templates.
19. Identify process friction. Analyze where students get stuck—forms abandoned, repeat questions, late submissions—and propose fixes, then validate with frontline staff before changing workflows.
Enrollment, Marketing, and Communications
20. Produce multi-channel content efficiently. Generate first drafts for web, email, SMS, and social from one approved message, then edit for brand voice and compliance.
21. Personalize ethically. Create segmented variants for first-generation students, transfer students, or adult learners using non-sensitive attributes and approved data practices.
22. Optimize websites for clarity. Use AI to flag confusing navigation, jargon, and missing calls to action, then A/B test changes.
Human Resources, Finance, Procurement, and Operations
23. Speed up policy and job-description updates. Draft role expectations and performance criteria for “AI-enabled work,” including training pathways and boundaries. EDUCAUSE’s work research was conducted with partners including CUPA-HR and NACUBO, reflecting cross-functional demand.
24. Draft procurement requirements that protect you later. Require disclosure of data use, retention, training, model updates, audit logs, and incident reporting, aligning with a risk-management approach like the NIST AI Risk Management Framework.
Six Guardrails to Make AI Tools Safer
Set acceptable-use policies for work. Decide what data is allowed, which tools are approved, and what requires human review. EDUCAUSE, a nonprofit that supports technology and digital transformation in higher education, has urged institutions to close policy and guideline gaps as AI use spreads across campus operations.
Treat student and employee data as “high-risk by default.” Use privacy-by-design, minimize data shared, and align training and practice with Family Educational Rights and Privacy Act (FERPA) expectations.
Use a risk framework. NIST’s AI Risk Management Framework is built around governing, mapping, measuring, and managing AI risks.
Assume generative tools can hallucinate. Require citation checks, spot-checking, and a human sign-off for public-facing or consequential work. UNESCO explicitly flags validation and data privacy as central challenges.
Procure like you mean it. Map what you already have—Microsoft, Google, LMS, CRM—before buying point solutions. Ithaka S+R has tracked the fast-moving higher education generative AI product landscape and the resulting complexity.
Measure outcomes, not adoption. Productivity gains are real in many contexts, but they show up as cycle-time reductions and fewer errors, not “we launched a chatbot.” Stanford’s AI Index summarizes a growing body of research linking AI to productivity gains.
Choose tasks that are high-volume, low-risk, and easy to verify. Then expand toward higher-stakes use cases only after you have policies, training, and audit processes in place.
