I spent a few days this week at AWS Imagine in Chicago watching for one thing: what the sessions would say about where data and AI in higher education are actually headed.
There was no shortage of announcements and demonstrations. Agentic AI ran through the entire event. AWS made its case for model choice through Amazon Bedrock, universities showed new applications, and several institutions described partnerships with technology providers that have grown well past the usual vendor relationship.
The thing I keep coming back to, though, is not a product or a model. It is that the conversation itself has changed.
For the past few years, most of the discussion has been about access to generative AI. Which models should institutions provide? Should faculty and staff use ChatGPT or Copilot? How do we handle academic integrity? What policy do we need?
Those questions have not gone away. But the institutions presenting at Imagine were mostly past them and onto a harder one: how does AI become part of how the institution actually runs and does research?
That change has consequences.
AWS framed 2026 as the year generative AI gives way to agentic AI: systems that do more than produce an answer and can instead reason, plan, work across other systems, and finish part of a workflow inside defined controls.
The distinction is not just marketing. Higher education has spent a lot of time on conversational interfaces. The next round of work is less about what you can ask an AI system and more about which institutional processes it can move forward.
The examples bore that out. Georgia State University described a multi-agent student assistant reported to serve more than 50,000 students. Loyola Marymount University used AI-assisted development to modernize hundreds of AWS Lambda functions. George Washington University reported cutting development time substantially on a complex administrative systems integration. Ohio State showed how agentic-powered application development can work in an enterprise-class environment.
Vanderbilt’s examples were the most operational of the group. One school is using an agentic tool to coordinate testing accommodations across classrooms during a two-week testing window. Another prototype maps degree requirements against course availability so students can build an academic plan.
I find these more interesting than another general-purpose chatbot because each one is attached to a specific institutional outcome.
They also raise the governance bar considerably. A tool that summarizes a document carries a different risk profile than an agent that recommends a student’s schedule or takes action inside institutional systems. Broad AI policy does not cover that. Institutions need governance that addresses data access, decision rights, human oversight, system permissions, auditability, and what happens when the agent gets it wrong.
A second pattern showed up across the more substantive use cases. The value was not coming from the model. It was coming from how the model had been connected to data, applications, workflows, validation, and people.
None of the sophisticated examples were built around a single large language model.
The University of Arizona’s was the clearest illustration. Its research environment combines Amazon Quick (formerly Quick Suite), Amazon Bedrock, Amazon Kendra, Amazon S3, and Amazon SageMaker with a language integration layer, verification models, and human validation. The team reported repeating work that had previously taken years in roughly 14 days, at three times the data output and half the cost.
The lesson is not about those particular AWS services. Institutions shopping for an “AI platform” should be careful about collapsing the decision down to model selection. The durable capability is everything around the model: institutional data, orchestration, applications, governance, and evaluation.
Models will keep changing. The ability to connect them securely to trusted data and useful workflows is much harder to build and much harder to replace.
This is also why model optionality has become a strategic question rather than a technical one. AWS pushed Bedrock’s access to multiple providers hard, and whatever you make of the vendor framing, the underlying question applies no matter whose cloud you are in. Are you building around a model, or building an institutional AI capability that survives the next model?
The Arizona session was the most consequential one I attended.
Most of the higher education AI conversation has focused on teaching, administrative efficiency, and student services, for good reason. But the effect on the research lifecycle could be at least as large.
The Arizona team described compressing analysis timelines while increasing the volume of research they could run, with further work underway on technology commercialization and on computer-simulated clinical trials that model treatment outcomes before any patient is enrolled.
If that proves repeatable, it changes more than productivity. It changes the economics of research. Work that used to require large teams, specialized technical staff, or long analytical cycles may come within reach of smaller groups and investigators with less funding. That could widen participation well beyond institutions with R1 infrastructure.
The same presentation supplied its own counterweight, which is part of why I trust it. Testing more than 20 models, the team found persistent failure patterns, including what they reported as a roughly 7 percent rate of affirming false information. They also described “reverberation,” where a model oscillates between correctly rejecting and incorrectly accepting the same piece of misinformation.
Faster research does not lower the bar for rigor. If anything, it raises the cost of skipping verification because a lab can now generate wrong answers at a volume no one can check by hand.
What I took from the session is not autonomous AI replacing researchers. It is researchers investigating far more than they could before, paired with much stronger validation and reproducibility mechanisms.
Vanderbilt offered the other idea worth stealing.
One participant described the philosophy behind its AI work as treating the university as a lab. Real institutional problems become the material that faculty, students, staff, and technology partners experiment on.
That framing deserves more attention than it gets. Plenty of institutions approach AI adoption through committees, technology evaluations, and policy discussions. Those are necessary. They do not produce organizational learning on their own.
Vanderbilt’s Amplify Staff Fellows program is one answer. In its first academic year, roughly a dozen staff members per cohort worked with peers and the university’s Generative AI Initiative team to explore generative AI and build applications tied to their own jobs, with the better ones positioned to scale across the institution.
That gives an institution something most AI strategies lack: a repeatable way to find use cases from the people who understand the work. Rather than predicting centrally which applications will pay off, you build a governed environment where faculty, staff, and students keep surfacing problems, testing them, and reporting what worked.
Institutions that build that loop will probably outrun institutions that simply buy the most sophisticated tools.
My favorite demonstration of the week was also the narrowest.
The University of Chicago showed a Virtual Communication Coach for graduate students. A student practices a talk at their computer and gets feedback on pace, filler words, eye contact, and gestures, then fields questions from simulated audiences: academics, employers, or the general public. It was reportedly built in about six weeks through an Amazon-supported student development program.
What makes it good is not the technology. It is the clarity of the problem. A graduate student has to speak differently to a dissertation committee than to an employer or an investor, and practicing that repeatedly with faculty or peers does not scale. AI gives them somewhere low-stakes to fail. Focus groups found the simulated Q&A the most valuable part because students had to answer on the spot instead of reciting something rehearsed.
That is worth remembering when the strategy conversation starts. The best institutional AI applications may not begin with a mandate to transform the university with AI. They begin with a smaller question: what is something valuable our students, faculty, or staff cannot do often enough, fast enough, or cheaply enough today?
I kept circling one question all week. Why are some institutions already shipping useful AI applications while others are still debating which enterprise chatbot to license?
It is not access. Nearly every institution can reach capable models now.
The difference looks like the institutional ability to learn. Can the institution identify a problem worth solving, get domain experts and technologists and students in the same room, build a prototype quickly, test it with real users, and measure whether it improved anything? Can it stop the ones that did not work without turning them into multiyear projects, and carry what it learned into the next attempt? That last part is rarer than it sounds. Most institutions are better at starting things than stopping them.
Vanderbilt’s partnership model is one version of the flywheel. Its cloud innovation lab, regional technology alliance, and generative AI center connect students, employers, faculty, researchers, and technology providers around real problems. The innovation hub that Rutgers, RWJBarnabas Health, and AWS described during the keynote works on similar logic, organizing four- to 10-week interdisciplinary prototyping efforts around clinical and operational challenges.
Structures will differ by institution. The principle does not. AI adoption is an organizational learning problem more than a technology deployment problem.
I left Chicago more convinced that the next phase of higher education AI will not be decided by which institution has the most powerful model. Access to capable models is becoming ordinary. Building the institutional machinery around them is not.
For most institutions, that means a list of unglamorous work: trusted data foundations, an architecture that tolerates model change, clear governance and decision rights, a way to validate AI output, a safe place for faculty and staff to experiment, and a path from successful prototype into production. None of it demos well.
It also means treating fewer demonstrations as evidence of transformation. Arizona’s findings on model fallibility are a reminder to keep close. Capability and reliability are different questions, and a system can accelerate work while still needing continuous auditing and human judgment.
There are three things I would push on if I were sitting in a cabinet meeting next week. Pick one process rather than one tool and ask what an agent would need permission to touch to move it forward. Decide who signs off when an AI-assisted decision affects a student. And find out whether your data are in any condition to support this work because the institutions doing the impressive things spent years on that part before AI made it interesting.
The institutions that handle this well probably will not be the ones that deploy AI everywhere first. They will be the ones that get good at deciding where AI belongs, where it does not, how its performance gets measured, and what the institution learns from every deployment.
That, more than anything announced in Chicago, is the transition actually underway.
Be sure to follow Alpha Hamadou Ibrahim on LinkedIn to catch all his great industry insights.
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