From FOMO to Framework- What Higher Ed Leaders Can Learn about Scaling AI

By Claire Brady, EdD

In the race to embrace generative AI, many higher education institutions find themselves caught between excitement and uncertainty. The recent report from CGI, Scaling AI to Deliver Tangible Business Outcomes, offers a clear-eyed roadmap—one that higher ed leaders would be wise to study as we move from pilots and prototypes to enterprise-wide implementation.

“Organizations now face a critical challenge: how to scale AI initiatives while delivering tangible business outcomes.” Sound familiar? Whether you’re streamlining advising, enhancing student services, or modernizing operations, the pressure is on to move beyond the experimentation phase.

CGI’s research highlights a common tension: some institutions are “fearful—moving too slowly or tackling small, safe projects that don’t add value,” while others are “reckless, plunging ahead on too many initiatives” without a strategic foundation. For colleges and universities, both approaches carry risks—wasted resources, unmet student expectations, and missed opportunities to improve the learning and working experience.

So what does scaling AI responsibly look like?

CGI outlines five imperatives that resonate deeply with higher education’s current crossroads:

1) Future-fit operating model: We need agile, cross-functional teams that treat AI as part of the digital fabric—not a side project. This means setting clear KPIs for impact (think: enrollment, retention, advising hours saved) and balancing innovation with governance.

2) IT modernization: If your systems can’t support AI, no amount of ambition will move the needle. That means accelerating cloud adoption and retiring legacy tools that bottleneck progress.

3) Data strategy and interoperability: The old silos won’t serve us in an AI-enabled future. CGI emphasizes that “AI solutions must enhance data quality and break down silos” to ensure accurate, meaningful results. For higher ed, that’s about integrating SIS, CRM, LMS, and other platforms for a 360-degree student view.

4) Agentic AI and automation: The report notes the value of reducing manual tasks to free up human capital. For faculty and staff, this could mean automating form processing, appointment scheduling, or even generating first-draft communications.

5) Culture and talent strategy: Perhaps most critically, “build a culture of innovation that reduces the risk of attrition and retains and attracts top talent.” In higher ed, this means investing in AI literacy—not just for CIOs and data scientists, but for academic advisors, financial aid staff, and student leaders.

The report reminds us that AI isn’t just a tool; it’s a catalyst. When scaled strategically, it can “turn AI’s promise into tangible and trusted outcomes—not just in labs or workplaces but in homes and communities for greater societal impact.”

In a field built on purpose and transformation, we owe it to our students—and our colleagues—to lead with clarity, courage, and care. As CGI puts it, “The future of AI in business lies in aligning responsible AI initiatives with clear governance and business goals.” The same holds true for the future of AI in education.

Let’s move past the FOMO and toward frameworks that work.

Read the full CGI report to explore this topic further.