The conversation in higher education around AI has shifted.
A few years ago, faculty and administrators were asking whether AI belonged on campus at all. Today, most leaders have moved past that question. Students are using AI tools, regardless of whether institutions sanction them. Employers are already factoring AI fluency into how they evaluate graduates.
The debate about adoption is largely over. What remains unsettled is the harder question underneath it: what does this actually require of us?
That question is easy to defer. AI feels like a technology problem, and technology problems can feel like someone else’s domain. But what’s becoming clear, especially in professional and continuing education, is that the implications extend well beyond any IT department or curriculum committee. They reach into how we think about the purpose of a degree, how we assess students, and whether the learning models we’ve inherited still fit the world our graduates are entering.
Those are not new questions. AI has simply made them urgent.
S. Sriram, Associate Dean for Graduate Programs at the University of Michigan Ross School of Business, frames it this way: “AI is not a new disruption landing on an otherwise stable system. Instead, it is an accelerant, surfacing structural questions that have long been present in higher education.”
That framing resonates with what we see across Noodle’s university partners. The stress points AI is exposing were already there: assessment models that reward task completion over judgment, curricula that lag behind industry by years, and faculty development structures that assume a slower pace of change. AI has compressed the timeline for addressing all of them.
Business education illustrates how this looks in practice. Ross graduates are entering fields where AI already shapes how strategy is developed, content is produced, and decisions are made.
A student without AI fluency is not merely missing a skill; they are missing a fundamental way of working. As Sriram puts it, “A student who leaves a top business school today without genuine AI fluency is entering a job market that has already moved on.”
The same dynamic is arriving, at varying speeds, in health professions, law, engineering, and public policy. The specifics vary. The underlying challenge does not.
In my own work with university partners, a consistent pattern emerges. During the initial Discovery process, the AI conversation surfaces quickly, and it rarely starts with curriculum. It starts with capacity; faculty who feel unsupported, staff managing more with less, and leaders who know change is necessary but aren’t sure where to begin or what to prioritize. The technology question sits on top of a more fundamental one: do we have the conditions in place to adapt at all?
That’s where the real work is: not in identifying the right AI tools, but in building the organizational readiness to use any of them well.
What remains durable in all of this is worth naming. The experience of learning in community, with real peers and diverse perspectives, is something AI cannot replicate.
Furthermore, the signaling function of a credential from a trusted institution has not diminished. If anything, employers need greater assurance that a graduate possesses genuine judgment rather than just access to powerful tools. The ability to apply knowledge in ambiguous, real-world situations remains irreducibly human. AI raises the bar on that work; it doesn’t replace it.
For leaders in professional and continuing education, the most useful questions right now may not be about tools at all. They’re about design. To move forward, institutions must address three critical inquiries:
- Industry alignment: Are we preparing graduates for the industries that exist today, or the ones we remember?
- Authentic assessment: What does evaluation look like when AI can complete most traditional assignments?
- Institutional support: Are we providing faculty the time and resources to experiment, or leaving them to navigate this shift alone?
These are not rhetorical questions. They are practical challenges forward-thinking practitioners are actively wrestling with, the ones that surface the most useful conversation when the right people are in the room together.
That conversation is happening. The institutions willing to ask harder questions than the technology itself demands are the ones most likely to lead.
