America’s colleges and universities find themselves under extreme pressure, from financial challenges exacerbated by declining enrollment to the Trump administration launching multi-pronged attacks against the sector.
Some college leaders view AI as just another point of consternation. However, rather than being dismissed as too disruptive, AI experimentation can be a central piece of a college’s response to current challenges.
Here are seven guidelines college and university leaders should follow to integrate AI into their institutions, in ways that can not just respond to their most urgent needs, but also set them up for future success:
Be focused and coordinated
Many college faculty, staff, and students are already exploring how AI can make their work better and more efficient. At the institutional level, though, practical AI experimentation takes more coordination. That means identifying priorities that align directly with an institution’s current challenges and supporting exploration that promises to advance progress in these areas. Outside crises like the pandemic, this level of focus isn’t easy. But when it works, it makes a difference—like the institutions and systems that have teamed up with ACUE to support faculty professional development on a broad scale, with promising results measured in improved student outcomes.
Identify a taxonomy of use cases
As part of focusing their efforts, institutions should create a structured process for categorizing potential uses for different AI applications. That includes exploring where colleges can deploy AI to achieve strategic goals, like boosting enrollment or persistence. Or they can borrow from others—such as IDC or Digital Education Council—that have begun building walls around this untended garden.
Adopt a portfolio approach
At a minimum, institutions should invest in areas that return quick results. But it’s worth balancing short-term wins with long-term opportunities that might not show impact as quickly. Positive results achieved quickly aren’t just good for morale; they can also create goodwill for AI applications that take longer to evaluate because they push the technology and organizational culture up to—and even beyond—their limits.
Resource the effort
Colleges making the most headway with AI are intentionally immersing their employees in this new technology rather than expecting their staff to address it during their time away from work. This approach will add expense. Resourcing the effort may require reallocating dollars from less productive or lower-priority activities. These kinds of trade-offs are hard in higher education, which, after years of enrollment growth, has gotten used to innovation through addition rather than substitution.
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Resourcing the effort may also mean engaging with employee professional development more deliberately and aligning it with institutional priorities. It will likely require institutions to make significant adjustments to how they envision professional development for staff and faculty — shifts that, particularly in the age of AI, are increasingly urgent. This, too, represents an essentially new direction for organizations where the focus of professional development, particularly on the faculty side, is a matter of largely unconstrained personal choice.
Keep people in the loop
Much of the anxiety around AI today comes from fears that it will take people’s jobs. But rather than asking how AI can replace people, forward-thinking colleges are exploring how AI can help their existing staff do things that wouldn’t have been possible alone. Those institutions are finding that the best use of this technology isn’t to take existing processes and make them more efficient. It’s to build better processes altogether—ones that leverage AI to respond and engage on the nuts and bolts while enabling professionals and subject matter experts to make better use of their expertise.
Have the courage to scale what works—and cut what doesn’t
Higher education is littered with bold initiatives that began with the best intentions but consumed scarce talent and resources, worked at cross-purposes with other efforts, and ultimately fell short of delivering results that justified the investment. While it takes a bold leader to jettison efforts that fail to bear fruit and focus only on the most promising approaches, it takes an even more courageous leader to choose among separate initiatives competing for scarce resources to deliver similar outcomes.
Of course, while some institutional efforts to innovate fail because they consume too many resources, others fall short of their potential because they don’t get the support they need. Institutions should have scaling strategies ready to go when an AI tool demonstrates evidence of impact. That’s the quickest and most effective way to drive widespread adoption of practical and impactful uses of AI across an entire institution.
As daily developments in Washington and beyond threaten to overwhelm the nation’s colleges and universities, technological change continues to accelerate. Institutions that cannot find a way to focus on both could be left behind. But those who embrace the potential of emerging technology will find that an intentional, thoughtful approach to innovation can help them both navigate the present challenges and prepare for a more sustainable future. That means starting small, identifying the right places to experiment, and being ready to scale what works.
