Artificial intelligence adoption in higher education is often discussed through policy or technology strategy, but much of the real work is happening informally through experimentation, peer learning, and day-to-day problem solving across institutions. This session explores how AI Communities of Practice can help universities move from fragmented experimentation toward more thoughtful and coordinated approaches to AI governance. Drawing from experiences supporting AI initiatives in higher education, the session examines how faculty, staff, and administrators make decisions about AI use while institutional guidance and norms are still evolving. The conversation will focus on professional judgment, trust, ethical uncertainty, institutional culture, and the hidden labor involved in helping colleagues make sense of rapidly changing technologies. Rather than viewing governance only as a top-down policy process, this session considers how governance also develops socially through conversation, collaboration, and shared institutional learning.
I build the AI systems that liberal arts educators worry about. Seven years of designing enterprise cloud and AI platforms for higher education and healthcare has produced an unexpected conviction: the more capable these systems become, the more indispensable the liberal arts skills they cannot replicate. This session names the paradox directly. The same tools that automate writing also raise the bar on judgment about what should be written. The same models that summarize at scale make source criticism a survival skill, not a humanities luxury. Drawing on three production case studies advising, hiring, and public communication .I will trace where AI fails, why it fails in those specific places, and how that maps onto the disciplinary strengths of a liberal arts curriculum. Participants leave with a shared vocabulary for talking to students and employers about the work AI cannot do and the work it makes more valuable.
