Artificial intelligence has quietly crossed a line in higher education. It is no longer an experiment. It is part of the institutional infrastructure. Generative AI tools draft emails, summarize research, screen applicants, support student services, and are embedded within enterprise SaaS platforms. The faculty are experimenting, the staff is optimizing workflows, and vendors are layering AI into the products that institutions already use. But here is the governance question: Who is overseeing the risk?
In advising our executive team on technology governance, I have seen how quickly AI adoption can outpace oversight structures. AI initiatives are often driven by efficiency and innovation. Governance frameworks tend to follow later—if at all.
For executive leaders, artificial intelligence is not merely a technology issue. AI risk must be included in board conversations, as its widespread capabilities raise fiduciary responsibilities, legal liabilities, and insurance exposures that must be addressed.
Institutional Risk, Not a Departmental Experiment
AI adoption tends to begin in silos. For example, admissions evaluates predictive tools, IT enables generative copilots, and student affairs deploys chatbots. All of these tools are individually manageable, but together they are collectively material to an institution's risk exposure.
Each AI tool introduces risk across multiple domains:
Data privacy when institutional data enters external systems
Bias risk in admissions, financial aid, and academic analytics
Intellectual property and training-data exposure
Vendor liability gaps
Reputational harm from inaccurate outputs
Some institutions enter configuration data, contract drafts, and internal policy content into AI tools without pausing to ask whether that d