Colleges and universities struggle to buy artificial intelligence tools effectively, according to a new EDUCAUSE QuickPoll. The survey found that AI procurement remains difficult because institutional AI governance structures lag behind rapidly evolving technology.
Procurement professionals report that buying decisions require careful navigation of competing vendor claims, unclear regulatory landscapes, and institutional uncertainty about AI policy. Many institutions lack coherent frameworks for evaluating AI tools against their own strategic goals.
The EDUCAUSE poll identifies two critical success factors. First, procurement teams must anchor purchases to documented institutional AI strategy rather than treating AI adoption as ad-hoc technology spending. Second, they should prioritize vendors who commit to transparency about how their systems work, what data they collect, and how they handle bias and privacy.
The timing matters. AI tools reshape teaching, learning, and administration faster than most institutions can develop governance policies. Vendors release new capabilities monthly. Budget cycles and procurement timelines operate on annual schedules. This mismatch creates pressure to buy without full institutional alignment.
The poll underscores a structural problem in higher education. Procurement teams traditionally evaluate tools based on cost, features, and vendor reliability. AI tools introduce new dimensions: algorithmic fairness, data governance, transparency requirements, and alignment with emerging regulatory frameworks like the EU AI Act. Standard procurement checklists do not capture these factors.
Institutions that succeed tend to establish AI governance committees before launching broad procurement initiatives. These committees clarify whether the institution will allow AI in admissions, grading, advising, or research. They define what transparency and accountability mean locally. They build vendor evaluation criteria that include governance requirements alongside technical specifications.
The EDUCAUSE findings suggest procurement professionals should treat AI purchasing differently than traditional software. Working backward from institutional AI strategy, rather than forward from vendor pitches, changes what questions procurement teams ask. It shifts focus from "Does this product work?" to "Does this product align with what we