AI Took Over ASU+GSV 2026 — But It Won’t Solve Higher Ed’s Biggest Challenges Alone

AI didn’t just show up at this year’s ASU+GSV Summit. It took over.

In past years, artificial intelligence had its own dedicated space at the AI Airshow, a pre-conference event focused on emerging tools and forward-looking innovation. This year, that separation dissolved. Not because AI mattered less, but because it now defines the conversation.

AI wasn’t a track. It was just about the entire conference.

Across sessions, vendor showcases, and side conversations, one theme dominated: how AI is reshaping higher education. From student engagement to learning design to institutional strategy, AI has become the default lens institutions use to evaluate their priorities and how they invest for the future.

And yet, what stood out most wasn’t just what was present. It was what was missing.

The missing conversation: structural challenges still exist

For all the attention on AI, there was less focus on the systemic pressures facing higher education:

• Ongoing enrollment declines and demographic shifts
• Increased regulatory scrutiny
• Growing skepticism around the ROI of a degree

These challenges didn’t disappear. Not to say it was completely absent from the conversation, it was simply overshadowed.

AI is being positioned as the one-size-fits-all solution to many of these issues, but it does not address their root causes on its own. That disconnect is one that institutions cannot afford to ignore.

From tools to workflows: AI gets operational

One of the most important shifts emerging from the conference is how institutions are thinking about AI implementation. The conversation has moved beyond tools.

Institutions are now being pushed to rethink AI as part of how work actually gets done. That means embedding AI into core workflows, including:

• Enrollment funnels
• Advising models
• Student support systems
• Operational decision-making

This is a meaningful evolution. But the market has not fully caught up.

Many solutions are still positioned as “easy buttons,” promising outcomes without requiring operational change. That gap between expectation and execution is where many AI initiatives will stall.

AI does not transform outcomes on its own. It requires institutions to redesign workflows, rethink processes, and align teams around new ways of operating.

Data is the divider between progress and stagnation

If there was one clear line separating institutions poised to benefit from AI and those at risk of falling behind, it is data.

AI amplifies what already exists. It does not create capability from nothing.

Institutions with strong data foundations — including integrated systems, accessible insights, and clear governance — are already moving faster. They are scaling what works and translating experimentation into measurable outcomes. Others are stuck in pilot mode.

Without the right infrastructure, AI initiatives remain fragmented and difficult to sustain. This reinforces a broader reality. Data strategy is no longer a back-office function. It is a prerequisite for institutional transformation.

Collegis sees this firsthand. Institutions that invest in unified, actionable data environments are better positioned to drive decision-making, improve enrollment outcomes, and enhance the student experience at scale.

The vendor model is evolving into partnership

Another shift was impossible to miss. The traditional vendor model is breaking down.

AI solutions are not static. They require continuous tuning, iteration, and alignment with institutional context. There is no one-size-fits-all configuration that works across campuses.

As a result, institutions are rethinking what they need from external support. They are not looking for vendors. They are looking for partners.

The value is no longer just in the technology itself. It is in the ability to:

• Adapt solutions over time
• Align with institutional goals and operations
• Provide ongoing strategic and technical guidance

This shift reflects a broader trend across higher education. Sustainable impact comes from integrated partnerships, not point solutions.

AI’s real value is human impact

Despite the heavy focus on technology, the most compelling narrative at ASU+GSV was not about automation. It was about people.

Across sessions, AI was consistently framed as a way to free up staff and faculty time, enabling more meaningful engagement with students. In a sector built on human connection, that framing matters.
But it also raises expectations.

If AI does not lead to better student outcomes — stronger persistence, improved engagement, and clearer pathways to completion — it will quickly lose credibility. Institutions are not investing in AI for efficiency alone. They are investing in impact.

The shift from experimentation to accountability

Taken together, these themes point to a critical inflection point. Higher education is in a phase of rapid experimentation. AI tools are accessible, widely adopted, and full of promise.

But many of the solutions entering the market today lack the operational depth required to deliver sustained results. There are no plug-and-play answers to enrollment challenges or retention gaps.

These are system-level issues. Solving them requires:

• Strong data foundations
• Thoughtful workflow design
• Cross-functional alignment
• Partners who understand how institutions operate

The next phase will not be defined by experimentation. It will be defined by accountability.

AI will reward the institutions that are prepared

For me, the key takeaway from ASU+GSV 2026 is clear: AI will be transformative, but only for institutions prepared to do the work around it.

That involves:

• Investing in data infrastructure
• Rethinking how work gets done
• Building true operating partnerships
• Staying focused on outcomes, not just adoption

Institutions that take this approach will move beyond experimentation and unlock real, measurable impact. Those that do not will risk entering the next phase of the cycle, where expectations collide with reality and skepticism begins to take hold.

That shift may very well define next year’s conversation.