About This Session
What if the most important question in AI adoption isn't "What can AI do?" but rather "What can we do now?" That reframe — simple but consequential — is what unlocked a new approach to capacity and innovation at both Hamilton College and Colgate University.
In this session, two CLAC peer institutions share how they stopped waiting for new headcount, new budgets, or the perfect enterprise platform — and started asking better questions. This isn't a theoretical AI presentation. It's a ground-level field report of what they actually built, what surprised them, and what failed.
The Discovery Framework
Find the Right Work
Focusing discovery on recurring asks, low-value-but-time-consuming tasks, and workflows nobody owns — the real entry points for automation.
Build Without Big Teams
A practical model for developing AI build capacity without a dedicated PM or large teams — validated across two peer institutions.
Side-by-Side Comparison
Two institutions, two approaches to automation — compared side-by-side so attendees can see which model fits their own shop.
The Leadership Case
Practical framing for making the "do more with less" case to campus leadership in ways that actually land.
What You'll Take Away
- A simple discovery question framework adaptable to any CLAC institution
- A side-by-side comparison of automation approaches at two peer schools
- A model for building AI capacity without dedicated PM or large teams
- Practical framing for making the "do more with less" case to campus leadership
- Honest lessons from early automation work — not just what worked, but what didn't
Learning Outcomes
- Apply a replicable discovery framework to identify the right workflows for AI automation at your own institution.
- Compare automation approaches across two peer institutions and determine which model is most relevant to your context.
- Make the internal case for AI-driven capacity building without requiring new headcount, large budgets, or enterprise platform investments.
Session Description
We've all been part of AI presentations that talk in abstract around what AI is, how it could be used, and what we could think about it. This session takes it a step further into actionable things that have actually been done. By focusing discovery on work that was already happening — the recurring asks, the low-value-but-time-consuming tasks, the workflows no one owned — both teams found a practical entry point into AI-driven automation that didn't require a major initiative to get started.
Attendees will leave with a replicable discovery framework, honest lessons from early automation work, and a clearer sense of how to make the case internally for doing more with the same — or less.