Campus AI workflow
ProductionBiweekly sessions where campus staff bring an AI idea and leave with a scoped use case and a recommendation on whether to proceed.
- BiweeklyCadenceEvery other Friday
- 5-60MinutesPresenter-selected
- AllAudienceStaff, faculty, researchers
Accountability
How this use case is governed
- Service owner
- TritonAI program and solutions teams
- Human oversight
- A TritonAI facilitator and campus service owner confirm the scope and recommendation.
- Measurement plan
- Time to decision, completeness of intake, appropriate routing, and participant usefulness.
- Data boundary
- Public description; discovery notes follow the meeting's approved handling
Product media
See the workflow in action
Silent screen recording showing presenter intake, agenda generation, and the recording archive.
- Presenter Intake
- Agenda Generation
- Recording Archive
How it works
How the workflow fits
The problem it addresses, what it actually does, and how far along it is.
Problem
“Can AI help?” is too broad to answer. Before anyone can say yes or no, someone has to name the person who would use it, the task, where the correct answer comes from, and how you would know it worked.
Solution
The production workflow captures presenter submissions, meeting length, topic context, upcoming sessions, and archive metadata. It runs on a biweekly cadence (every other Friday) with 5–60 minute presenter-selected sessions open to staff, faculty, and researchers. The platform generates agendas from submissions and maintains a recording archive so individual experiments become shared institutional learning.
Current status
In production. The tool runs the sessions and keeps the archive. People still decide whether an idea should proceed, change direction, or stop.