About This Session
Not all AI use cases are created equal. This session centers on identifying and stress-testing AI use cases that deliver real value at the scale a small liberal arts institution can actually handle — not aspirational enterprise deployments, but concrete applications that fit the reality of lean teams, limited budgets, and complex governance.
Rather than debating AI in the abstract, Berry College grounds the conversation in specific examples already in play or under serious consideration — surfacing the differences between vendor-provided platforms, locally-enabled innovation, and institution-level infrastructure investments.
Three Paradigms for AI Value
Partnership
Vendor-provided AI solutions and the strategic tradeoffs they carry — what you gain in speed and scale, what you give up in control and customization.
Internal Development
Locally-enabled AI innovation — what's possible for a small team, where the real dependencies lie, and how to build without burning out.
User Enablement
Empowering faculty, staff, and students to use AI tools effectively and responsibly — the training, governance, and support structures that make it work.
Stress-Testing
A framework for evaluating whether a use case is actually worth pursuing — accounting for data, governance, skills, change management, and sustainability.
Learning Outcomes
- Explain the strategic tradeoffs between vendor-provided AI solutions and locally-enabled or internally-developed uses of AI.
- Recognize the dependencies — data, governance, skills, and change management — that influence whether an AI use case can succeed at your institution.
- Describe concrete examples from Berry College of AI currently in use or under exploration, including partnerships, workshops, and architectural investments.