10 Practical Moves for an AI Strategy in Higher Education

As higher education leaders navigate rapid change, the real challenge isn’t whether to use AI, but how to build a thoughtful AI strategy for higher education that serves mission, people, and students well.

The 2025 State of AI report from McKinsey outlines where organizations stand in AI adoption, what’s working, and what’s not.

While the data points are insightful, they raise an important question for those of us in higher education.

What does all of this mean for leaders navigating enrollment challenges, digital transformation, and the shifting expectations of students?

After reviewing the full report, I’ve pulled together 10 reflections that every higher ed leader — especially marketers, admissions teams, and digital strategists — should consider as they work toward a clearer AI strategy for higher education.

1. AI Scaling Is the New Digital Divide

Eighty-eight percent of organizations say they’re using AI in at least one business function, but only a third have scaled it enterprise-wide.

In other words, most institutions are still stuck in the pilot phase.

This creates a new digital divide: those who are learning by doing and those who are just dabbling.

If we’re still treating AI like a sandbox experiment in 2026, we risk falling behind institutions that are moving toward a more intentional AI strategy for higher education.

It’s time to get teams trained and give them permission to use AI in daily work.

2. Efficiency Alone Won’t Cut It Anymore

According to the report, high-performing organizations are not just aiming to cut costs.

They’re leveraging AI for growth, innovation, and enhancement.

That’s a critical mindset shift.

In higher education, AI can’t be viewed as just another automation tool.

The institutions making real progress are using AI to rethink outreach, personalize recruitment, and improve yield — not just streamline back-end operations.

This mirrors the same shift many institutions face when moving from tactical marketing to mission-fit marketing that prioritizes right-fit students over raw volume.

This shift is central to building a sustainable AI strategy for higher education.

3. Don’t Just Add AI. Redesign the Workflow.

One of the strongest signals from the report is that the organizations getting real results are redesigning workflows around AI.

They are not simply plugging AI into old systems.

It’s a bold ask, but it’s one higher ed leaders need to embrace.

Whether it’s financial aid communication, student advising, or content creation, the question isn’t where AI fits into existing workflows.

The better question is how those workflows would be rebuilt if AI were native from the beginning.

4. Agentic AI Has Potential, but We’re Not There Yet

Agentic AI — systems that can plan and act autonomously — has generated significant buzz.

Yet only 23 percent of surveyed organizations are scaling these systems, often in just one or two functions.

That perspective matters for higher education leaders shaping an AI strategy for higher education.

AI agents may eventually support enrollment or financial aid operations.

For now, leaders should focus on proven, well-governed use cases that deliver immediate value.

5. Executive Buy-In Is a Make-or-Break Factor

Across high-performing organizations, leadership involvement is consistent.

Senior leaders don’t just approve budgets.

They model usage, champion integration, and take ownership of outcomes.

If an institution’s AI strategy lacks visible executive support, progress will stall.

When AI ownership lives only in IT, it’s difficult to achieve enterprise-wide momentum.

6. Bigger Budgets Help, but Strategy Matters More

High performers often invest a significant portion of their digital budgets in AI.

What matters more than the amount spent is how those resources are allocated.

Successful organizations invest in teams, infrastructure, and training.

For resource-constrained institutions, this reinforces the need for prioritization.

An effective AI strategy for higher education focuses spending where alignment and impact meet.

7. Risk Governance Needs to Catch Up

More than half of organizations report negative consequences from AI, often tied to inaccurate outputs.

Few are actively mitigating risks like bias, explainability, or data privacy.

For higher education institutions handling sensitive data, governance must be intentional.

At the same time, governance cannot become a barrier that prevents progress.

The goal is oversight that enables innovation, not bureaucracy that buries it.

8. AI Will Reshape Roles, but Not Always with Job Loss

Most organizations did not see widespread job loss from AI adoption.

Instead, roles evolved.

In higher education, AI literacy is quickly becoming essential for marketers, communicators, and admissions teams.

Those who understand how to work alongside AI will create greater impact than those who don’t.

This reality should shape how institutions think about training and workforce development.

9. Ambition Is the Differentiator

The most successful organizations aren’t just more advanced technically.

They are more ambitious.

They are far more likely to pursue enterprise-wide transformation.

For higher education leaders, ambition is often the missing ingredient in AI strategy.

We see a similar pattern when institutions hesitate to fully commit to long-term enrollment and marketing strategies that require cross-campus alignment and leadership ownership.

Vision and intent matter as much as tools.

10. The Future Is Still Wide Open

Despite rapid progress, most organizations have not yet scaled AI effectively.

That means opportunity still exists.

Higher education institutions willing to act thoughtfully can help define what responsible, mission-aligned AI looks like.

The question is whether leaders will wait or help write the playbook.

This conversation isn’t about chasing trends or shiny tools.

It’s about preparing institutions for a future where personalization, data, and automation are expected.

In the AI age, strategy still beats speed.

But only if leaders are willing to start moving.

Custom AI Masterclass: From Insight to Action in Higher Education

If this post raised important questions, you’re not alone.

Most institutions aren’t struggling with whether AI matters—but with how to move from experimentation to a clear, responsible AI strategy for higher education.

Our Custom AI Masterclass is a 6-hour, hands-on experience, typically delivered over two days, designed to help higher ed teams move forward with confidence and intention.

• Day 1 focuses on AI best practices, prompt design, and practical tools, with time for real questions and discussion.
• Day 2 is dedicated to workshopping real-life use cases from your institution and identifying next steps that align with your mission and workflows.

Rather than adding AI to broken processes, this masterclass helps teams redesign how work gets done—strategically, responsibly, and with clarity.

If your institution is ready to move beyond pilots and start learning by doing, the Custom AI Masterclass is a strong next step. Contact us to learn more.

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Images via Midjourney