Campus forecast 2026: How agentic AI could change the way universities run

Artificial intelligence in universities has so far behaved like a helpful junior colleague—eager, fast, and dependent on constant instruction. That era, according to a new forecast, is about to end.

In Predictions 2026: Insights for Online & Professional Education, a report by UPCEA, the US-based online and professional education association, the next phase of AI is framed not as smarter assistance but as independent execution.“...there is a second wave of AI coming our way as we approach 2026. Agentic AI is a version that becomes a 24/7 project manager. It can understand a high-level goal, create a multi-step plan, execute that plan across different software systems, and learn from its mistakes without human prompting.

This is the version of AI that can lighten the load of faculty, staff and students on a continuing basis. It performs at a high level, works at computer speed around the clock, and reliably reports back to humans as requested.

This will save time and money for universities, and even accomplish work that would have been too expensive or time consuming in the past.”The prediction comes from Ray Schroeder, Senior Fellow at UPCEA, and it points to a structural shift universities are already beginning to grapple with: AI that does not wait to be asked, but works towards goals.

What this prediction is really about

Schroeder is not forecasting a better chatbot or a more fluent tutor. He is describing a change in agency. Until now, campus AI systems have mostly answered questions. Agentic AI changes the relationship by taking responsibility for outcomes. You no longer ask the system to draft an email or summarise a report; you ask it to reduce dropout rates, shorten admissions cycles, or improve student support—and it decides what steps are required to get there.That distinction matters because universities are not struggling with ideas. They are struggling with execution. Fragmented systems, understaffed offices, endless follow-ups, and compliance-heavy processes quietly drain academic and administrative time. Agentic AI, in this framing, is not a teaching innovation, it is an operational one.

From assistance to action: What agentic AI will change

The first wave of AI improved individual productivity. The second aims to reshape how institutions function.

Agentic AI systems are built to move across the digital infrastructure universities already rely on—Learning Management Systems (LMS), student information databases, scheduling software, CRMs, help desks—and stitch them together into continuous workflows. What human staff currently achieve through emails, reminders, spreadsheets, and manual coordination, an agentic system can pursue relentlessly and at scale.This is why the metaphor of a ‘project manager’ resonates. The system is not valued for brilliance, but for follow-through.

How agentic AI actually works

At its core, agentic AI runs on a simple loop. A goal is defined. The system breaks it into steps. It chooses which tools and data sources to access. It executes actions. It checks whether the result moved the goal forward. Then it adjusts and continues.In a university setting—where delays rarely happen because people do not know what to do, but because no one owns the entire chain—this ability to hold the full process together is powerful.

It is also unsettling. Delegating action, even partially, forces institutions to decide how much authority they are truly willing to hand over.

How universities can use agentic AI

Agentic AI will not debut as a robot lecturer or an automated dean. It is likely to enter quietly, through the back doors of the institution. Admissions offices buried under follow-ups, student support teams overwhelmed by queries, academic units struggling with course reviews and compliance checks—these are the spaces where autonomy feels like relief rather than threat.

Early deployments may focus on speeding up decisions, reducing dropped hand-offs, and smoothing routine interactions.In UPCEA’s framing, this is where AI stops being experimental and becomes infrastructure.

The promise that makes universities lean in

The most obvious promise is time. Administrative drag has long crowded out the core work of teaching and mentoring. If agentic systems can absorb coordination, documentation, and routine decision-making, human effort can be redirected to higher-value tasks.But the deeper promise is capacity. Universities have historically avoided personalised follow-ups, continuous monitoring, and real-time interventions because they were too labour-intensive. Agentic AI makes those ambitions suddenly affordable. This is what Schroeder means when he talks about work that was previously “too expensive or too time consuming” to attempt. Efficiency is the headline. Reach is the revolution.

Why autonomy raises the stakes

The moment AI systems are allowed to act, risk stops being theoretical. A chatbot that gives a wrong answer is an inconvenience. An agent that sends an incorrect message, alters a record, or triggers an automated intervention affects real people—quickly and at scale. Bias becomes procedural. Privacy concerns become systemic. Errors propagate faster than committees can react.This is why agentic AI will force universities to confront governance rather than novelty.

The question will no longer be whether or not the the tool works, but who is accountable when it doesn’t.

The illusion universities must avoid

Institutions chasing “AI transformation” without clarity risk buying the worst of both worlds. They may pay for systems marketed as autonomous, discover they offer only shallow automation, and still inherit the risks associated with delegating action. In that scenario, universities absorb the danger of autonomy without receiving its dividends.The hype cycle only sharpens this risk. “Agentic” has already become a loose label, and separating genuine capability from inflated promises will matter more than glossy demos.

Agentic AI and governance in higher education

Agentic AI may drag universities into unfamiliar terrain: The delegation of action itself. Chatbots could be tolerated because they mostly talked. Agents are harder because they do. Once AI systems begin triggering workflows, updating records, or prioritising cases, the old institutional habit of fixing problems after the fact collapses.Suddenly, the unglamorous questions dominate. Who has access? What can the system change directly, and what must it only recommend? Where is the off-switch? When a student challenges an outcome, can the institution explain how it happened, or will it point to a vendor contract and a dashboard?The universities that manage this transition well will treat agentic AI the way they treat research ethics or financial controls: Cautious with authority, explicit about boundaries, and obsessive about accountability.

Those that rush may rediscover a painful truth: Nothing scales like a mistake, and autonomous systems make mistakes at computer speed.

What this means on campus

For students, the immediate experience may improve. Responses get faster. Paperwork moves. Fewer requests vanish into inboxes. At the same time, students may increasingly find themselves negotiating outcomes with systems they cannot see, understand, or easily contest.For faculty, the best version is liberating. Administrative sludge recedes.

Time returns to teaching, mentoring, and scholarship. The tension lies elsewhere: In subtle pressure to align human judgement with machine-managed workflows, and the quiet expectation that academic life should now move at algorithmic speed.For administrators, agentic AI promises scalability and efficiency. But it also concentrates responsibility. When an autonomous system misfires, the explanation will not be “the AI did it.”

It will be that the institution allowed it.

Bottom line

Agentic AI is not another edtech upgrade. It is a shift in how work, responsibility, and authority are distributed inside the university. By 2026, institutions will not be divided by who uses AI, but by who governs it well. The real test will not be technological sophistication, but institutional maturity: The ability to delegate without abdicating responsibility.