The 95% failure rate in corporate AI should terrify higher education leaders. It should also guide them.
Universities have been down this road before. Institutions have long struggled with the build-versus-buy dilemma, often convincing themselves that their needs are too unique for commercial solutions. Whether it was student information systems, learning management platforms, or CRMs, the pattern repeats: enormous development costs, years of technical debt, and eventual migrations to commercial platforms that deliver better results with less complexity.
Today, as generative AI promises to transform everything from admissions to academic support, higher education stands at an eerily similar crossroads. And if MIT’s new State of AI in Business 2025 report is any indication, universities are about to repeat history’s most expensive mistakes.
The Sobering Numbers Behind AI’s Enterprise Reality
The MIT report, based on 150 executive interviews and analysis of 300 AI deployments, delivers a stark verdict: 95% of generative AI pilots in enterprises are failing to deliver measurable business impact. Despite $30-40 billion in corporate AI investments, only 5% of organizations are seeing transformative returns.
For context, this failure rate exceeds even the most pessimistic predictions about digital transformation initiatives. The primary reason for failure is not the technology itself, but rather the implementation approach.
According to MIT researcher Aditya Challapally, successful organizations share three critical characteristics:
- They purchase specialized tools rather than building their own.
- They empower line managers rather than centralizing everything in IT.
- They focus on back-office automation rather than flashy customer-facing applications.
The Build-vs-Buy Trap Universities Keep Walking Into
The data from MIT is unambiguous. Companies that purchase AI tools from specialized vendors succeed 67% of the time. Internal builds succeed only 22% of the time. That’s a three-fold difference in success rates.
Yet across higher education, some institutions are doubling down on proprietary AI development, convinced their needs are too unique for commercial solutions. The same arguments used to justify homegrown systems in 2005 are being recycled for AI in 2025: “Our processes are different.” “We need to control the data.” “Commercial vendors don’t understand us.”
Meanwhile, specialized education technology companies are building AI platforms with domain expertise, regulatory compliance, and integration capabilities that internal teams can’t match. Companies like CollegeVine have already processed millions of student interactions, training their models on domain-specific use cases that no single institution could replicate.
When Forbes reported that 64% of data migration projects exceed their forecasted budgets, they weren’t just talking about corporate America. Higher education experiences the same failures, often worse, because of smaller IT teams and more complex stakeholder environments.
Where Universities Are Misallocating AI Resources
The MIT report reveals another critical insight: more than half of enterprise AI budgets go to sales and marketing tools. These are the areas with the lowest ROI. The highest returns come from back-office automation: eliminating outsourced processes, reducing external consultants, and streamlining operations.
Consider the financial reality. Universities facing potential cuts to federal research funding and Title IV aid are allocating scarce resources to compete with companies that are already partnering with hundreds of institutions to use AI to work with millions of students. It’s like asking your facilities team to design and build their own HVAC system from scratch. Could they eventually figure it out? Maybe. But at what cost, and why would you?
The real opportunity is in deploying AI for the time-consuming work that bogs down every enrollment office. Transcript evaluation that takes days to process. FAFSA verification that creates bottlenecks every spring. Incomplete application reminders that staff send manually, one by one. After-hours inquiry responses that pile up until Monday morning. When specialized AI vendors handle these workflows, IT can focus on what they do best: ensuring secure integration, maintaining data governance, and enabling departments to maximize these tools’ impact.
This mirrors exactly what I found in my analysis of Student Search performance. The biggest drops in enrollment yield weren’t from lack of innovation in student-facing tools. They were from operational inefficiencies in processing and managing leads. The same pattern holds for AI implementation.
The path forward for universities isn’t to build their own large language models or develop proprietary AI systems. It’s to partner with specialized vendors who understand education’s unique requirements: FERPA compliance, academic integrity, student privacy, and the complex workflows that define higher education.
Who Will Be Your AI Partner?
As MIT’s research makes clear, successful AI adoption isn’t about having the best technology. It’s about having the right implementation strategy. And that strategy, overwhelmingly, involves partnering with specialized providers rather than going it alone.
For universities, this means asking hard questions. Who understands both AI and higher education deeply enough to be a true partner? Which vendors have proven success in educational settings, not just impressive demos? Who can integrate with your existing systems while respecting your data governance requirements?
The institutions that answer these questions thoughtfully will be the ones that actually capture AI’s transformative potential. The rest will join the 95% failure club, with nothing to show for it but technical debt and missed opportunities.
Just as Search performance has degraded 67% since 2018, forcing institutions to rethink their entire enrollment strategy, AI implementation failures will force a similar reckoning. The difference is that with Search, we had decades to adapt. With AI, we have maybe three years before the competitive disadvantage becomes insurmountable.
The lesson from corporate America’s AI struggles is clear. You don’t need to own the AI to own the outcomes. What you need is a thoughtful partner who understands your domain, integrates with your workflows, and evolves with your needs.
Twenty years ago, universities learned this lesson with CRM systems. Let’s not take another twenty years to learn it again with AI.