Principles without application are just intentions
The technology and risks may look different across functional areas, but the principles remain the same. Applying them consistently starts with a common set of questions: Who owns the outcome? What data is the tool drawing on, and how reliable is it? Who reviews AI-generated decisions before they affect people, and what happens when something goes wrong?
The examples below are not an exhaustive list of how to apply each principle, but they show how those questions translate into practice across three areas where AI is creating new opportunities and new responsibilities.
Enrollment and student success
AI is reshaping how institutions recruit, retain, and support students, from identifying prospective applicants to flagging at-risk students before they disengage. Applying AI intentionally means anchoring every tool to a defined student outcome, ensuring human advisors remain in the loop for high-stakes decisions, and continuously auditing results for equity gaps and unintended consequences.
Research administration
AI can accelerate literature review, grant identification, and regulatory compliance, but research institutions face heightened scrutiny around data privacy and security. Responsible adoption depends on establishing clear data governance policies, auditing existing data for completeness and compliance, and building institutional consensus on how AI-generated insights will be reviewed and validated before they inform decisions.
Finance and operations
AI is making inroads into budgeting, procurement, and administrative workflows. As it matures, moving from tools that respond to prompts to systems that autonomously initiate and complete multi-step tasks, the nature of oversight must also evolve. Institutions need to decide which decisions AI can support or execute, where human review remains essential, and how exceptions and errors will be identified and addressed.
