Artificial intelligence is now central to strategic conversations across higher education. Institutions are experimenting with generative tools, predictive analytics, and new forms of automation across enrollment and student information systems.
But the most important question is not, “What do these tools do?”
It is:
Where should AI operate — and what authority should it have?
In higher education institutions, AI can assist an individual, automate tasks for a team, or become embedded directly inside institutional systems such as the Student Information System (SIS) and enrollment management platforms.
The capabilities may look similar. The institutional impact is not.
An AI tool that drafts student communications operates very differently from one that validates admissions records inside an SIS. An automated reminder system carries different implications than a model that influences financial aid eligibility, enrollment classification, or institutional reporting.
The difference is not the model. It is the scope of authority and proximity to systems of record.
In practice, AI in higher education operates in three distinct contexts.
At this level, AI assists faculty, staff, and administrators in their daily work. It enhances cognition but does not alter institutional systems such as the SIS or financial aid platforms.
Common uses include:
Often described as generative AI (GenAI), large language models (LLMs), or copilots, these tools create, evaluate, or predict — but always at the direction of an individual.
They do not write to the SIS.
They do not modify official student records.
They do not initiate institutional workflow steps
The risk profile is limited. The governance footprint is minimal.
In this context, AI performs defined tasks automatically on behalf of an individual or team. It operates alongside institutional systems —not inside them.
Examples include:
Often described as agentic AI or intelligent automation,these systems execute within bounded authority.
AI can create tasks, route notifications, and trigger alerts. But it does not write authoritative data into the Student Information System, finalize admissions decisions, or modify financial aid records.
Autonomous execution changes workflow state. It does not change system state.
The impact is task-level automation — not institutional record control.
The most consequential use of AI in higher education occurs when it is embedded directly into institutional systems and official processes.
Here, AI operates inside the Student Information System and related enrollment and financial aid infrastructure.
Examples include:
Often referred to as operational AI or intelligent document processing (IDP), this category carries institutional authority.
AI outputs influence:
Embedded AI changes system state.
Its logic must be governed. Its outputs must be auditable.
When AI operates at this level, it becomes part of institutional accountability.
Across all three contexts, AI in higher education can create, evaluate, and predict.
What changes is scope of authority.
As AI moves closer to student information systems,enrollment management platforms, and financial aid systems, the institutional stakes increase.
AI will continue to evolve. But for higher education leaders, the defining question remains the same: where does this system operate, and what authority does it carry? Clarity about scope separates experimentation from strategy — and determines how deliberately AI is integrated into institutional systems of record.