In a webinar last week, Saravanan Subbarayan and David Gagnon defined four ways higher education institutions are approaching AI integration: as trailblazers, synergists, mavericks or stragglers.
"It’s a mosaic of factors, ranging from technological advancements to necessity, that is driving the shift in higher education institutions moving beyond their initial fear of AI,” Subbarayan later wrote in an email to Government Technology. “Whereas a few years ago institutions were questioning the need, now several of them are asking themselves ‘what can we can do to ensure we utilize this capability to the benefit of our students?’”
FOUR INSTITUTIONAL APPROACHES
Gagnon and Subbarayan said they developed the four categories based on interactions with higher ed clients.
Trailblazers are leading from the front, investing heavily in technology, and bringing their peers along, they said.
Synergists collaborate with other schools, pooling resources for shared investments and strategies. They said most schools fall within this category.
Lastly, stragglers proceed cautiously, often hindered by limited resources or outdated systems.
“Even as the initial fear of AI diminishes, 'survivors' — the institutions that are either smaller in size, have financial strain or are lagging in performance — often become 'stragglers' in GenAI adoption due to existing technology infrastructure, limited funding, or challenges in allocating resources for new technology experiments,” Subbarayan said. “Additionally, reliance on legacy systems and an aging workforce makes the transition to cloud technologies particularly daunting for stragglers.”
For all four categories, resources, funding and performance have great influence. Few schools have the resources and support necessary to be a maverick, for example.
Abby Sourwine
INDIVIDUAL APPROACHES
Within institutions, those leading the charge of AI integration have different priorities.
“Our observations indicate that most institutions are synergist and collaborate with peers to ideate and implement GenAI use cases; however, different roles within the same institution may fall into various categories,” Subbarayan said.
For these collaborators, Subbarayan and Gagnon also developed a framework for understanding different approaches. The three kinds of leaders in their framework are technologists, academicians and administrators.
Technologists focus on infrastructure and vendor selection. Traditionally, this might be a chief information officer or IT manager. Academicians, as Subbarayan and Gagnon defined them, prioritize ethical applications and academic research. Administrators seek operational efficiencies and workforce transformation.
Gagnon said the recipe for successful generative AI adoption is a mixture of collaboration and coordination among a team that includes all three kinds of leaders. The two frameworks — the institutional and the individual — go hand in hand, they said.
“For instance, a chief information officer (CIO) might be a trailblazer leading from the front and bringing their peers along, while an administrator could be a synergist collaborating with their peers,” Subbarayan said.
LOOKING AHEAD
In light of this shift away from fear, AI will be even more of a priority in 2025 and will require careful governance, according to Gagnon. While higher education mirrors the technological advancements of commercial sectors, its consensus-driven culture can slow decision-making. Integrating AI into existing decision-driving systems will have a big impact, he said.
“The extent to which existing ERP and SaaS products incorporate generative AI will be a critical factor, with colleges and universities likely adopting a blend of these innovative use cases,” Gagnon said.
He said addressing issues like biased data and algorithmic errors through comprehensive policies and internal controls will likely drive governance, as they can pose risks to an institution’s data analysis, revenue or reputation.
“However, with the right controls in place, the benefits of AI can outweigh the risks,” he said.
