10 Takeaways from McKinsey’s 2025 AI Report

by Claire Brady, EdD

Every November, McKinsey drops their annual AI report that becomes required reading for anyone leading digital transformation—and this year’s edition is especially relevant for higher ed. The headline is clear: AI is everywhere, but meaningful impact is still rare.

That tension feels familiar to anyone working on a college campus right now. We’ve spent the past two years surrounded by excitement, pilots, tool demos, and “what if” conversations. And yet… most institutions haven’t crossed the bridge from experimentation to enterprise-level change.

The 2025 McKinsey State of AI Report puts data behind what many of us have been sensing: the next phase of AI won’t be defined by tools—it will be defined by workflow redesign, leadership ambition, and the courage to rethink how work gets done. The organizations seeing real value aren’t the ones tinkering at the edges. They’re the ones moving beyond efficiency and leaning into growth, innovation, and system-level transformation.

For higher ed, these insights matter. They confirm the friction we’re feeling, the slow progress we’re navigating, and the opportunity ahead for leaders who are ready to act with clarity and purpose. They also offer slide-ready guidance for cabinet conversations, board updates, retreats, and conference presentations.

Below, I’ve distilled the most relevant findings for campus leaders—what the report says, why it matters for higher ed, and how to translate each insight into a message that resonates with your teams and stakeholders.

Because if there’s one thing the McKinsey report makes unmistakably clear, it’s this:

The institutions that will thrive in the next five years aren’t the ones with the most AI tools. They’re the ones willing to pair ambition with redesign—and lead boldly into what’s next.

1. “AI Everywhere, Impact Not Yet”

AI adoption is high. AI impact is still low. 88% of organizations are using AI, but two-thirds haven’t scaled it across the enterprise. This mirrors where many campuses are: lots of tools, lots of pilots, very little system-level transformation.

2. AI Agents Are Coming Fast

62% are experimenting with AI agents—automated systems that perform multi-step tasks. Early adoption is strongest in IT and knowledge management, which align directly with advising, student services, and campus operations. The shift from chatbots to agents will reshape service delivery in higher ed.

3. High Performers Think Bigger Than Efficiency

80% aim for efficiency, but the organizations seeing the most value also target growth and innovation. Efficiency is the starting line, not the strategy. Efficiency saves time; innovation reinvests it.

4. Workflow Redesign Is the #1 Predictor of AI Success

High performers are 3x more likely to redesign workflows rather than bolt AI onto what already exists. This is where higher ed struggles the most—too much retrofitting, not enough reimagining. AI isn’t a plug-in—it’s a process transformation.

5. Senior Leaders Must Model AI Use

High performers have leaders who personally champion and use AI in their own work. AI fluency is now a leadership competency, not an IT project. AI leadership cannot be delegated.

6. AI Value Today Is Mostly Qualitative, Not Financial

Only 39% report enterprise-level financial impact from AI. But most report improved innovation, customer satisfaction, and competitive differentiation—early wins that matter. The ROI today is innovation. The ROI tomorrow is transformation.

7. Scaling Requires Investment

One-third of high performers spend 20%+ of digital budgets on AI. They invest in talent, infrastructure, governance, and workflow redesign—not just tools. You can’t scale AI on pilot budgets.

8. Workforce Impact Is Real—but Uneven

32% expect workforce reductions; 13% expect increases. Most see role shifts, not job elimination. AI-related hiring—especially engineers, data roles, and operational analysts—is rising rapidly. AI transforms roles before it transforms headcount.

9. Risk Management Is Catching Up

Organizations are now mitigating twice as many risks as in 2022. Top risks: inaccuracy, privacy, explainability, IP protection, reputational risk. A responsible AI strategy is no longer optional—it’s expected.

10. The Biggest Differentiator: Ambition

The most successful organizations treat AI as a transformation, not a task.

High performers are:

• More likely to pursue transformative change

• Scaling 3–5x faster

• Using AI in more business functions

• Deploying more AI agents

• Backed by strong executive ownership

The institutions that thrive in the next five years won’t be the ones with the most tools—they’ll be the ones with the clearest ambition, the strongest leadership, and the courage to redesign how work gets done.