Under the Iceberg History Repeats itself in the AI Era

Higher education has always been exceptional at scaling impact through human capital. When a new initiative launches, when student needs grow, when fundraising goals increase, or when compliance requirements expand, the instinctive response is often the same: hire another coordinator, add another analyst, build another team. This human capital-based approach reflects the best of what universities are: mission-driven, relationship-based, and deeply committed to service. But it also reveals a long-standing pattern. Universities have historically scaled by adding labor, not by building the underlying technology and data infrastructure that make scale sustainable.

The challenge is that technology is often treated as the visible part of the solution, something you can purchase, implement, and move on from. A new customer relationship management (CRM) system, a refreshed website, a reporting platform, or an artificial intelligence (AI) tool can create the impression of modernization. But the real work of scale lives underneath those tools, and it is rarely visible to the casual observer. Like an iceberg, the part above the surface gets the attention: the user interface, the feature set, the vendor demo, the launch announcement. Under the waterline is the deeper and more consequential work: data architecture, integration, governance, privacy and consent management, data quality, and the ongoing operational discipline required to keep systems trustworthy over time.

This is where universities often underestimate the scope of what “technology” means. Technology is not just software; it is also the ecosystem that makes software usable. Without the infrastructure beneath it, even the best platforms struggle to deliver their promised value. Dashboards become unreliable because data definitions vary across units. Automation fails because processes are not standardized. AI produces inconsistent results because the underlying data are incomplete, outdated, or fragmented. Teams end up spending more time reconciling and repairing data than using the information to make decisions, and the organization quietly returns to what it knows best: solving problems with more people.

In that environment, foundational data work can feel like an endless negotiation rather than a clear path forward. Add to that the reality of budget cycles, leadership transitions, and the pressure to demonstrate immediate outcomes, and it becomes easier to fund visible improvements than invisible infrastructure. A new tool can be celebrated. A year spent building integration pipelines and governance models is harder to communicate, even when it is the very thing that makes long-term scale possible.

The result is a quiet mismatch between expectations and reality. Institutions expect technology to reduce workload, increase insight, and create efficiency, but without the part of the iceberg beneath the surface, technology often increases complexity. Staff become system translators, analysts become data detectives, and leaders lose confidence in reporting. Ironically, the more technology that gets layered on without a strong foundation, the more people it takes to keep it all running.

At first glance, this may feel like a technology problem, but it is also a business model problem. Higher education is being asked to deliver more value to more stakeholders with greater transparency, speed, and personalization than ever before. Students and families expect seamless digital experiences. Alumni and donors expect relevance and responsiveness. Regulators expect demonstrable compliance. Boards and presidents are under pressure to make decisions with clarity and confidence. Yet many institutions are still operating with a model built for a different era, where they could achieve scale primarily through staffing and institutional expertise.

This is not the first time higher education has wrestled with questions of relevance and value. In 1828, Yale University leaders published a well-known report defending a classical curriculum centered on Latin and Greek as essential for a well-rounded education. Their argument emphasized education as “furnishing the mind,” grounded in tradition and intellectual discipline. The Yale faculty viewed practical fields such as agriculture, mechanics, and applied sciences as secondary, even potentially dilutive, to academic rigor.

But the nation’s needs were changing. The United States was industrializing, communities required technical expertise, and the workforce demanded accessible pathways to new skills. In 1862, the Morrill Act helped establish land-grant institutions, often described as “democracy’s colleges,” expanding higher education into agriculture, engineering, and the applied sciences. Rather than weakening American higher education, that shift strengthened it. The modern U.S. university became globally admired precisely because it learned to integrate tradition with innovation.

That historical arc matters today because we are living through a similar inflection point. The question is not whether higher education should abandon its mission, values, or relational strengths, but whether institutions will build the modern infrastructure required to deliver that mission at scale.

True scale in the modern university requires a shift in mindset from technology as an add-on to an institutional capability. It is not just about selecting tools, but about building the infrastructure that allows those tools to perform. That includes investing in data strategy and infrastructure as core operational priorities. When that foundation is in place, technology becomes transformative. Workflows become repeatable, insights become trusted, and automation becomes realistic. Teams can focus less on managing complexity and more on advancing the mission.

Rachel Crosbie is vice president of strategy and operations at Fundmetric.