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Artificial intelligence is everywhere in higher education conversations right now. Yet a recent MIT study revealed a sobering truth: 95% of GenAI pilots fail to deliver measurable financial impact. Only 5% generate meaningful ROI. For campus leaders weighing investments, the data should raise tough questions: Why is building an AI workforce so difficult? And what can institutions do differently to succeed?
The Reality Check: Why Most Pilots Fail
AI is often treated as a project—something an IT team can spin up, test, and check off. The problem? AI isn’t a one-off project; it’s a product. Without sustained workflows, integration, and measurement, most pilots stall out.
The MIT study surfaced three key failure points:
- Lack of ROI Measurement – Many pilots deliver flashy demos but fail to track real business outcomes.
- Internal Build Struggles – Only 33% of internally developed AI tools succeed, compared to 67% of vendor-purchased solutions.
- Shadow AI – 90% of employees use personal AI tools like ChatGPT, but this bottom-up adoption rarely translates into enterprise-wide impact.
And while institutions wrestle with these challenges, staff burnout continues to climb. Higher ed teams report 15% higher burnout rates than other industries, underscoring why solutions that lighten workloads matter.
Pilots vs. Products: The Workflow Gap
The difference between success and failure often comes down to workflows. It’s easy to build a demo; it’s much harder to design end-to-end processes that tie directly to institutional goals.
As one AI leader put it: “Stop doing pilots. Do projects. Turn them into products.” That shift in mindset—from tinkering to deploying sustainable workflows staffed by AI teammates—is where ROI starts to emerge.
Why Vertical AI Outperforms DIY
The study also reinforced a critical insight: verticalization wins. AI built for higher education by education experts outperforms generic or internally built tools. Why?
- Domain expertise ensures solutions align with real enrollment, admissions, and student success workflows.
- Pre-built integrations accelerate time-to-value.
- Rich data models provide the context AI needs to perform at a higher level.
It’s also worth noting that 93% of features in traditional CRMs go unused. Vertical AI platforms designed for higher ed avoid that waste by acting as digital teammates, not idle tools.
Simply put: buying purpose-built solutions beats reinventing the wheel.
The Rise of Forward-Deployed Engineers
Even with the right tools, institutions face a skills gap. Translating institutional processes into AI-driven workflows requires both technical expertise and deep domain knowledge. Enter the forward-deployed engineer (FDE) model.
Borrowed from companies like Palantir and OpenAI, FDEs are embedded experts who:
- Understand institutional workflows
- Configure and deploy AI agents effectively
- Iterate quickly alongside campus teams
This hybrid role bridges the gap between AI capabilities and institutional needs—something traditional consultants or IT staff often struggle to do.
Where ROI Really Happens
Interestingly, most AI budgets are going to sales and marketing use cases—content creation, campaigns, and outreach. While these help, the highest ROI often comes from back-office automation, such as:
- Application fraud detection
- Transcript and application review
- Quarterly business review (QBR) preparation
- Student support and advising workflows
Unlike marketing tools that save minutes here and there, these back-office automations transform entire workflows, delivering measurable ROI quickly.
Recommendations for Higher Ed Leaders
If your institution is navigating the hype vs. reality of AI, here are three takeaways:
- Treat AI as a Product, Not a Project – Build for sustained workflows and measurable outcomes, not one-off pilots.
- Buy, Don’t Build (At Least for Now) – Vendor-purchased tools are twice as likely to succeed. Look for vertical solutions designed for higher ed.
- Invest in Hybrid Expertise – Whether through forward-deployed engineers, AI-specialized agencies, or partnerships, bridge the gap between tech and domain expertise.
The Bottom Line
The MIT study confirms what many higher ed leaders are experiencing: the vast majority of GenAI pilots underdeliver. But failure isn’t inevitable. By reframing AI as a product, prioritizing workflow coverage, and leaning into vertical solutions with embedded expertise, institutions can move from flashy demos to measurable impact.
The battle for AI in higher ed won’t be won with pilots. It will be won with products—and AI teammates—that transform how institutions operate, delivering real ROI for students, staff, and leadership alike.
Curious to hear the full discussion? We unpack this in detail on our Generation AI episode — listen now.
FAQs: AI Pilots and Higher Education
What percentage of GenAI pilots fail?
According to MIT research, 95% of GenAI pilots fail to deliver measurable financial impact, with only 5% producing meaningful ROI.
Why do most internally built AI tools underperform?
Only about one-third of internally built tools succeed. The rest struggle due to limited expertise, lack of integration, and absence of sustainable workflows.
What’s the difference between a pilot and a product?
A pilot is a short-term experiment; a product is an ongoing solution embedded in institutional workflows. Products are where long-term ROI is generated.
What is a forward-deployed engineer (FDE)?
An FDE is an embedded expert who combines technical skills with deep domain knowledge. They help configure, deploy, and iterate on AI solutions inside campus teams.
Where does AI deliver the highest ROI in higher ed?
While marketing use cases get the most attention, the biggest ROI often comes from back-office automation: fraud detection, transcript review, and advising workflows.
Should institutions build or buy AI solutions?
Research shows vendor-purchased AI solutions are about twice as likely to succeed compared to in-house builds—especially when they are designed specifically for higher education.
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