Procurement departments across higher education institutions face mounting obstacles when buying artificial intelligence tools and services, according to recent EDUCAUSE polling data. The central problem stems from two interconnected challenges: the absence of clear AI governance frameworks at many colleges and universities, combined with the breakneck speed at which AI technology evolves.
The survey findings highlight a disconnect between rapid institutional adoption of AI tools and the slower pace of policy development to manage them. Procurement professionals report difficulty evaluating vendors, assessing risk, and ensuring compliance with emerging standards when institutional AI strategy remains undefined or incomplete.
The EDUCAUSE data suggests two pathways forward. First, institutions must anchor their AI procurement decisions to a documented, institution-wide AI strategy that clarifies values, acceptable use cases, and risk tolerance. Without this anchor, purchasing decisions become ad hoc and fragmented across departments.
Second, procurement teams should prioritize vendors demonstrating genuine commitment to transparency. This includes clear documentation of how AI systems work, where training data originates, potential biases embedded in algorithms, and security practices. Vendors willing to answer detailed technical questions and share governance practices prove more reliable partners than those offering opaque solutions.
The timing matters. Higher education institutions are simultaneously deploying AI for student recruitment, academic advising, plagiarism detection, and administrative functions. Yet many lack the governance infrastructure to manage these deployments responsibly. Procurement professionals occupy a critical gate-keeping role. They can either rubber-stamp purchases or enforce standards that protect institutional interests.
EDUCAUSE guidance aligns procurement strategy with broader institutional priorities rather than treating AI purchases as isolated transactions. This approach reduces duplicative spending, prevents conflicting systems from talking past each other, and establishes accountability chains when AI tools produce unwanted outcomes.
Institutions beginning this work should start by defining their AI governance structure, identifying who holds decision-making authority, and establishing criteria for vendor evaluation. Procurement teams equipped with clear