Higher ed growth shouldn’t feel like digging a brand-new well every time we want to expand, yet that’s exactly how many colleges and universities operate: launch another program, rebuild another process, stand up another mini-system, and hope the portfolio adds up. We challenge that model and ask a sharper question: Are we scaling what we do, or are we scaling what we make possible?

We pull lessons from Airbnb, Shopify, and OpenAI to explain platform strategy in plain language, then translate it into a university context without pretending a campus should become a marketplace. The turning point is the “highway vs trucks” idea: Real scale comes from shared infrastructure that lets work carry forward across offerings. Using research and reporting from McKinsey, Harvard Business Review, Fortune, and MIT Sloan, we show why duplicated work, restarts, and disconnected data are the silent killers of operational efficiency, staff capacity, and sustainable revenue.

Then we zoom out to the hub economy and the stakes for student pathways. If value now comes from connection, a large catalog of disconnected programs becomes a liability. We explore what it would look like for a college to act as connective tissue for learning across faculty, employers, alumni, communities, and peers and what happens if third-party platforms become the “front door” that organizes discovery, sequencing, and ongoing engagement. You’ll leave with three practical questions to take into your next leadership meeting: what gets reused, what connects, and what carries forward.

Subscribe for more higher education strategy, share this with someone leading change on your campus, and leave a review with the biggest “silo” you want to break next.

Universities aren’t slow because people inside them don’t care. They’re slow because they’re built to protect expertise, quality, and legitimate process, and that operating design makes fast change unusually hard. I walk through Henry Mintzberg’s idea of the professional bureaucracy and the trade-off it creates: higher education gets reliability and rigor, but it struggles when the world outside starts changing faster than our cycles of approval, coordination, and shared governance.

From there, I zoom out to what fast-learning organizations do differently. The point isn’t to copy startups or chase hype; it’s to understand how experimentation becomes part of daily operations and how results actually change decisions. We get specific about the failure mode universities know too well: pilot fatigue. When pilots aren’t tied to a decision to scale, fund, stop, or reallocate resources, the organization generates activity and data but doesn’t move. Over time, that drags down credibility, burns staff time, and spreads resources across initiatives that never fully land.

We also tackle a concept that often makes higher ed flinch: minimum viable products. I argue for an ethical, student-protective version of MVPs that helps institutions learn before committing at full scale. The real risk isn’t a small, scoped test; it’s making large irreversible bets without testing assumptions. If speed of organizational learning is becoming a competitive advantage in an AI-accelerated economy, the question is whether higher education builds the capacity to learn deliberately or keeps reacting after the fact. Subscribe, share this with a colleague, and leave a review with one place you think higher ed should run a small test next.