Colleges and universities are being asked to make binding, multi year decisions about enterprise AI vendors faster than most institutional processes were built to handle.
This guide is a vendor diagnostic tool designed to help presidents, chancellors, and provosts assess enterprise AI agreements before they sign them. It focuses on what matters most at the point of decision: data use, governance, institutional risk, and long term control.
The guide offers:
- A clear decision framework for evaluating enterprise AI vendors
- Critical questions to press vendors on data, models, and downstream use
- A practical review checklist for CIOs, CISOs, and General Counsel to work through together
The goal is not to recommend a specific AI solution or vendor. The goal is to help your campus make better decisions, surface hidden tradeoffs, and avoid agreements that create unintended academic, legal, or reputational risk over time.
We developed this tool based on direct conversations with higher education leaders who are negotiating AI contracts right now. It is shared openly so institutions can adapt it, pressure test vendors more effectively, and strengthen internal decision making.
This resource is free and intended for senior leadership teams facing high stakes enterprise AI decisions and looking for a clear, credible place to start.
How would you evolve this further to make it even more effective? Are there tools you'd like to share to help others in the field navigate this challenging moment? We are happy to amplify and elevate tools that work.
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