Your Institution Is Probably Not Training All the Right People on AI
Why staff AI training deserves its own strategy, and a framework to get started.
When we talk about AI training in higher education, we almost always mean faculty.
That makes sense on the surface. Faculty are the ones redesigning courses, rethinking assessments, and navigating students' use of AI in their classrooms. But in interview after interview across our research, more than 30 institutions, we kept hearing a version of the same admission: staff have been left out of the conversation.
Not because anyone made a deliberate choice to exclude them. It just happened the way most things happen in higher education: training resources followed existing structures, and those structures were built around faculty.
At one institution, a leader told us plainly: "We've never done anything strictly for staff as far as anything in their operations. It was always geared more toward teaching." At another, training was technically open to everyone, but the content, examples, and use cases were built for the classroom. Staff showed up, listened politely, and walked away with very little they could apply on Monday morning.
Meanwhile, those same staff members are already using AI. They're drafting communications with ChatGPT. They're summarizing reports. They're experimenting with tools they found on their own. Sometimes with student data, sometimes without realizing the risks involved.
This isn't a future problem. It's happening right now, and the gap between what staff are doing and what institutions have prepared them to do is widening every month.
The Training Gap No One Talks About
Higher education has a well-developed infrastructure for faculty professional development. Teaching and learning centers, faculty fellows programs, course release time for innovation. These structures exist because institutions have invested in them over decades.
Staff professional development, by contrast, tends to be episodic, compliance-oriented, and rarely role-specific. Add AI to the mix and the disparity becomes hard to ignore.
Our research surfaced several patterns that help explain why this gap persists:
AI training budgets follow existing structures. Most institutions fund faculty development through established channels: teaching and learning centers, provost offices, faculty senate initiatives. Staff development often sits in HR, which may have no AI expertise and limited training budgets. When AI training dollars appear, they tend to flow toward faculty almost by default.
"Open to all" doesn't mean "designed for all." Several institutions told us their AI training sessions were open to staff. But the content (prompt engineering for course design, AI in assessment, academic integrity implications) spoke to faculty concerns. An enrollment processor, a financial aid counselor, or an HR generalist would find very little that translated to their daily work.
Staff capacity makes participation harder. One leader described the challenge at small institutions: "Many of those places are like one-person departments. They can't stop and try to learn. Sometimes they can't even get away to professional development because they're the only one in the department." Faculty at least have built-in rhythms (semester breaks, professional development days, sabbaticals) that create windows for learning. Many staff roles don't have those structures.
Why This Matters More Than You Think
Staff AI training isn't a nice-to-have. It's both a risk management issue and a strategic opportunity that most institutions haven't fully addressed yet.
Risk: Staff handle sensitive data every day: student records, financial information, health disclosures, enrollment files. When they use AI tools without guidance, the risk isn't theoretical. An advisor entering student information into a free AI tool to draft a communication is a FERPA exposure. An IR analyst running institutional data through an unvetted platform raises real security questions.
Opportunity: The institutions in our research that were furthest along in AI adoption weren't just training faculty. They were thinking about their whole workforce. One president observed that at a small institution with minimal staff, figuring out ways AI could assist staff members and automate processes could be "game changing." The efficiency gains aren't just about doing existing work faster. They're about freeing up capacity in organizations that are already stretched thin.
Credibility: Colleges and universities position themselves as engines of workforce development, preparing students for AI-influenced careers. That message rings hollow if the institution's own workforce lacks the skills to navigate the same landscape.
What Different Staff Roles Actually Need
One of the biggest mistakes institutions make is treating staff AI training as a single initiative: one workshop, one webinar, one set of slides. But the AI needs of a student-facing advisor, a marketing coordinator, an IR analyst, and an IT administrator are fundamentally different.
That's why we developed the Guide to Developing Staff AI Training, a free framework that helps institutions identify what different staff roles need so they can build or source training that fits their context.
The framework starts with six foundational competencies that every staff member needs regardless of role:
Understanding what generative AI is and isn't that these tools predict plausible responses rather than retrieve verified facts
Basic prompt literacy functional competence in getting useful results, not expert-level engineering
Data privacy and security awareness what should never be entered into AI tools
Institutional policy knowledge what's permitted, what requires approval, where to go with questions
Recognizing AI-generated content both for quality control and for identifying AI-generated materials they receive
Knowing when to use and when not to use AI judgment about which tasks benefit from AI and which require human accountability
Beyond these fundamentals, the guide provides a role-specific competency matrix and detailed profiles for four common staff roles (student-facing advisors, marketing and communications, institutional research, and IT and infrastructure) with priority competencies, key risks, and training considerations for each.
“Staff AI training is not a parallel track to faculty efforts. It is a necessary complement.”
Getting Started: Five Steps
The framework is designed to be a starting point, not a prescriptive program. Here's the sequence we recommend:
Assess your current state. Where are staff already demonstrating AI competencies? Where are the biggest gaps? A brief survey or focus groups can establish baseline understanding.
Prioritize by risk and opportunity. You can't train everyone on everything at once. Where does lack of training pose the greatest institutional risk? Where would training unlock the most value?
Sequence realistically. Foundational training for all staff first, then role-specific depth. Avoid comprehensive plans that never launch.
Decide on build, buy, or curate. For foundational competencies, quality external resources may already exist. Role-specific training may require internal development.
Measure and iterate. Define what success looks like before training begins (policy compliance, behavior change, staff confidence) and adjust as you learn.
The Guide to Developing Staff AI Training is available now as a free resource. Download it, share it with your HR team, your division heads, and anyone responsible for professional development beyond the classroom.
And if this topic resonates, join us for our next webinar:
Building AI Capacity: Training, Roles, and Culture
April 21, 2026 | 2-3 pm ET
We'll be exploring how institutions are approaching AI training at scale, including who delivers training, how it connects to institutional strategy, and what's working for staff, not just faculty. We'll hear from institutional leaders who are testing innovative models, from peer trainers to student AI coaches.
We'd love to hear how your institution is handling staff AI training, or where you're getting stuck. Reach out at support@t3advisory.com or find us on LinkedIn.
This blog post is part of an ongoing research initiative led by T3 Advisory in partnership with Complete College America.
This content originally appeared as an article on T3 Advisory’s LinkedIn.