AI-Native Companies Aren't Higher Ed. But They Have Something to Teach Us.

Jul 31

By Claire L. Brady, EdD

Higher education is not a startup and nor should it be.

Our institutions exist to educate students, advance research, serve communities, and steward the public good. We operate within shared governance, accreditation standards, regulatory requirements, and missions that extend far beyond quarterly growth metrics.

That is exactly as it should be.

But every so often, another sector offers lessons that are too important to ignore.

A recent McKinsey article examined 15 AI-native organizations—from four-person startups to global technology companies—to understand how they are fundamentally redesigning work around artificial intelligence. Although these organizations differ dramatically in size and industry, they consistently described the same seven operating truths that are reshaping how they work.

No, higher education should not try to become these organizations. But we should absolutely study them, because they are asking questions that many colleges and universities have not yet begun asking. Not, "How do we use AI?" But, "How does AI change the way work gets done?" That distinction matters.

Too often, higher ed conversations focus on selecting tools, writing policies, or launching pilot projects. Those are important first steps, but they aren't transformation. The organizations McKinsey profiled are redesigning roles, rethinking workflows, capturing institutional knowledge, and changing leadership behaviors. They're treating AI as an organizational capability—not simply another technology implementation.

There are four lessons I believe higher education leaders should take from their example.

1. Stop thinking about AI as a tool. Start redesigning work.

The organizations in the study don't ask employees to simply "use AI." They rethink who—or what—does each part of the work. For higher education, that means moving beyond asking where faculty and staff can use AI and instead asking how advising, enrollment, communications, budgeting, HR, student support, and administrative work should evolve.

The goal isn't replacing people. It's allowing people to spend more of their time on the work that only humans can do: building relationships, exercising judgment, solving complex problems, mentoring students, and leading meaningful change.

Action Step: Choose one high-volume administrative process this summer and redesign it from scratch assuming AI is available—not simply layered on top of the existing process.

2. Your biggest AI challenge probably isn't AI.

It's institutional knowledge. McKinsey's research makes a compelling observation: AI performs only as well as the information it can access. When knowledge is trapped in email, meetings, shared drives, or the minds of long-time employees, AI exposes those organizational weaknesses.

Higher ed has lived with this challenge for decades. Our expertise is distributed by design—across colleges, divisions, departments, committees, and shared governance structures. Some of our greatest institutional knowledge lives in conversations, relationships, and the experience of long-time employees rather than in systems that can be easily searched or shared. As AI becomes more embedded in our work, knowledge management is no longer just an operational issue. It's becoming a strategic capability.

Action Steps:

Record and transcribe recurring leadership meetings.
Build searchable AI knowledge repositories instead of static file folders.
Capture institutional knowledge before retirements and turnover create permanent gaps.
Think less about where documents are stored and more about whether your institution's knowledge is actually discoverable.

3. Leadership behaviors matter more than technology choices.

One theme appears repeatedly throughout the article: leaders model the behaviors they hope others adopt. They experiment openly, share what they're learning, and create space for others to do the same. AI adoption becomes part of the culture—not simply another initiative.

I've seen exactly the same pattern across higher ed. The institutions making the most meaningful progress aren't necessarily those with the largest AI budgets or the most sophisticated technology. They're the ones where presidents, cabinet members, deans, and directors are willing to learn alongside everyone else.

Leadership curiosity is contagious. When leaders openly experiment, acknowledge what they're still learning, and celebrate thoughtful innovation, they create permission for the rest of the organization to do the same.

Action Steps:

Demonstrate how you personally use AI in your own work- and where you won’t use it.
Create regular opportunities for faculty and staff to share successful AI use cases.
Celebrate thoughtful experimentation—not just polished success stories.
Measure meaningful adoption and impact, not simply licenses or logins.

4. Capacity—not technology—is the real opportunity.

This may be the most important lesson from the article. The organizations McKinsey profiled aren't using AI simply to make today's work faster. They're asking what entirely new work becomes possible when people are freed from repetitive tasks and supported by intelligent systems.

That question feels especially relevant for higher ed. For years, we've asked our people to do more with less. AI won't solve structural challenges like funding, enrollment, or staffing. But it can help us rethink where we invest our human capacity. What if advisors spent less time documenting interactions and more time coaching students? What if department chairs spent less time wrestling with administrative tasks and more time developing faculty? What if cabinet members had more space for strategic thinking instead of being buried in operational work? That's the opportunity.

The goal isn't simply to become more efficient. It's to create the organizational capacity to invest more deeply in the work that only people can do.

Action Step: The next time your team identifies an AI use case, don't ask, "How much time will this save?" Ask, "What will we do with the capacity we gain?" The answer to that question is far more important than the efficiency itself.

As I finished reading the McKinsey article, I wasn't struck by how different higher education is from these AI-native organizations. I was struck by how much we can learn from them. We don't need to become startups. We don't need to adopt Silicon Valley's culture or abandon the values that make higher education unique. But we do need to be students of organizations that are learning how to thrive in the age of AI.

Higher ed has always excelled at creating knowledge. This is our opportunity to become just as intentional about applying it. That's a lesson worth learning—no matter where it comes from.

Read the full article here:https://www.mckinsey.com/capabilities/business-building/our-insights/the-seven-operating-truths-of-ai-native-companies