About the Episode
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About the Episode:
MIT Study: 95% of Generative AI Projects Fail
Chief AI Officers Emerge Across Industries
In this episode of Higher Ed Pulse, hosts Mallory and Gil Rogers break down the real reason 95% of AI projects fail—and it’s not the tech. From underground AI usage to rising trust issues and executive scrambling for structure, this episode explores how leadership—not capability—is the biggest hurdle for AI adoption in higher education and beyond. Whether you're an institutional leader or front-line staffer experimenting with AI, this conversation offers timely insights on how to approach AI integration with intention, empathy, and strategy.
Key Takeaways
- 95% of AI projects fail—not because of bad tech, but poor leadership. Lack of integration, governance, and training are the real culprits.
- Trust in AI grows with exposure—but leadership hasn’t caught up. Employees trust AI for assistance, not decision-making, especially when it comes to pay, hiring, and promotions.
- A “shadow workforce” is already using AI tools off-the-books. Nearly half of employees are using AI secretly, often paying for it themselves. That’s a governance failure, not innovation.
- Flashy pilot projects often miss the point. Without alignment to business goals and actual processes, AI fails to deliver ROI.
- Institutions need strategic leadership—not just tools. The rise of Chief AI Officers in other industries signals the growing need for formalized accountability and cross-functional AI oversight.
- Higher ed must act—but act smartly. Treat AI as you would any major technology shift: with process, policy, and the patience to iterate.
Episode Summary: The Leadership Failures Behind AI Flops — Answering the Big Questions
Why are so many AI projects in higher ed failing to deliver ROI?
According to MIT’s GenAI Divide study, 95% of AI projects are flopping—not because of the technology, but because of poor implementation. Mallory and Gil explain that most projects are rushed, under-resourced, and disconnected from strategic goals. Leadership often falls for the shiny-object syndrome, investing in flashy pilots without doing the hard work of integration, training, or measuring outcomes. As Gil puts it, “You wouldn’t hire a junior staffer and expect them to lead on day one without onboarding—why would you do that with AI?”
Why don’t employees trust AI to manage things like pay or hiring?
A recent Workday Hanover survey shows that while employees are open to using AI for productivity, 70% don’t trust it to manage critical HR decisions. The reason? AI can’t replace empathy or human nuance. People want to use AI, but they don’t want it used on them. This reinforces a core leadership lesson: Trust must be earned and designed into the system—especially when it comes to deeply personal aspects of work.
What’s the “shadow AI workforce” and why should leaders care?
Nearly half of U.S. employees are using AI tools at work without management’s knowledge. Many are even paying for tools like ChatGPT out of pocket. Mallory and Gil frame this as a governance gap, not a tech problem. When leadership lacks clear AI policies or suppresses experimentation, employees go underground—creating risk and inequity in who benefits from the technology. The result? Innovation happens outside the system, where it can’t be scaled or supported.
How should higher ed leaders structure AI governance?
The episode highlights a new trend in corporate America: the rise of the Chief AI Officer (CAIO). Brands like Lululemon and Ralph Lauren are appointing CAIOs to centralize AI strategy, risk, and integration. Gil argues that higher ed should take a page from this playbook—placing AI strategy at the cabinet level. Without centralized leadership, AI gets siloed, and opportunities for scale and safety are missed. But it’s not just about titles—it’s about accountability and cross-functional alignment.
What’s the danger of “pilot fatigue” and how can institutions avoid it?
Mallory and Gil agree that many institutions get caught in a loop of pilot projects that go nowhere. The issue is that most AI projects aren’t connected to real institutional problems or workflows. As Mallory notes, “It’s not that the tools don’t work—it’s that we aren’t ready for them.” Institutions must first revisit their fundamentals: goals, processes, and roles. Only then can AI add value. Otherwise, even the best tools will underperform.
How can we fix this and make AI work for higher ed?
Start with empathy, not efficiency. Create a culture of experimentation with clear guardrails. Align projects with real institutional pain points. Train your teams—not just on prompts, but on process. Most importantly, build trust through transparency. From admissions and marketing to financial aid and alumni relations, AI can amplify impact—but only if it’s deployed intentionally and ethically.
Connect With Our Host:
Mallory Willsea
https://www.linkedin.com/in/mallorywillsea/
https://twitter.com/mallorywillsea
About The Enrollify Podcast Network: The Higher Ed Pulse is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too!
Some of our favorites include Generation AI and Confessions of a Higher Education Social Media Manager.
Enrollify is produced by Element451 — the next-generation AI student engagement platform helping institutions create meaningful and personalized interactions with students. Learn more at element451.com.
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