Leadership & Generative AI: Hard-Earned Lessons That Matter

Actionable Advice for Higher Education Leaders in 2025

AFTER two years of working closely with leadership in multiple institutions, and delivering countless workshops, I’ve seen one thing repeatedly: the biggest challenge isn’t the technology itself, but how we lead through it. Here is some of my best advice to help you navigate generative AI with clarity and confidence:

1. Break your own AI policies before you implement them. Before implementing any AI policies, try breaking them yourself - or hire a student to do so. If you can easily circumvent them, so can your students. Hands-on testing tends to reveal the pointlessness of overly restrictive approaches.

2. Fund your failures. Set aside a budget for AI initiatives that might fail. Not "innovation funds" - explicitly call them "probably going to fail" funds. This signals that dead ends are valuable data points, not embarrassments.

3. Resist the pilot program. Pilots may work for you, but sometimes institutions use pilots as a way to delay real change. Consider going all-in on smaller but complete AI initiatives rather than endless "testing phases."
   
   

   
   

4. Host Anti-Tech Tech Talks. Create forums where professors can critique generative AI without feeling pressured to be "positive" or "solution-oriented." Professors who still use chalk and refuse to learn PowerPoint often have deep wisdom about what makes learning stick. Don't pressure them to "modernize" - instead, let them be your reality check on which AI implementations actually improve education versus just adding digital gloss.

5. Consider whether you really need to talk about "Best Practices". The term implies there's a known right way to handle AI. But in more ways than not, there isn't really. Replace it with "current experiments" or "working theories." This small language shift helps maintain institutional curiosity.

6. Create AI-Free Zones. Designate certain spaces or times as deliberately non-digital. The contrast often clarifies where AI actually adds value versus where it's digital clutter.

7. Celebrate Your Accidental AI Scholars. Watch for the literature professor who starts diving into machine learning papers, or the facilities manager exploring predictive maintenance algorithms. These boundary-crossers often understand the real institutional impact better than your official experts.

8. Forget about AI detection software. It doesn’t work and probably never will. False accusations frustrate students, and faculty often misunderstand the technology’s limitations. The evidence against detection tools is clear, as outlined in this excellent post by Stefan Bauschard. Instead of relying on unreliable tech, focus on fostering integrity through meaningful assessment design and dialogue.

9. Incentivize faculty AI innovation with AI. Not only does this approach show recognition and appreciation, but faculty innovators are key to making change happen. The best universities know this and have incentivized faculty innovation in teaching and research for decades. They back up this commitment with policies, funding, and processes that give faculty freedom and support to transform education at both the individual course and institutional levels.

10. Invest in people first, then technology. Whether you see generative AI productivity as a cost saver or as an abundance driver that can fuel your sustainable competitive advantage, AI should enhance human potential, not replace it. Be as transparent as possible about how AI will impact jobs (you probably don’t know yet - but it’s on everyone’s mind); AI success stems from people. People must be the priority.

11. Carpe diem. Capitalize. AI has captured the attention of all institutional stakeholders. It doesn’t get any better: this is the rare moment when you have everyone paying attention to your institution and its strategic goals for the future of education. Capitalize to reimagine teaching, research and innovation for the future.

12. Know your students. Don't make assumptions about what students need or think. I am frequently surprised by the student perspectives on AI, and you would probably be too. Continuously engage students via focus groups, design sessions, advisory boards - or more informally by chatting in the hallway. Make it your mission to find out how students interact with technology, and make sure these insights guide your institutional process.

13. Get out of the Ivory Tower with AI implementations. When launching AI chatbots, digital advising systems, or analytics dashboards, rigorously test with real users first. Expect hiccups and false starts. Be ready to ditch what fails and build on what shows promise. Make small bets before going all in. With emerging tech, you will inevitably waste money and time, but doing nothing might just put you out of business.

14. Don’t forget the water bottle. Think long and hard about the ethical and environmental consequences of AI. As a leader, your students (and staff) will hold you accountable for an equitable, transparent and fair use of the technology. This is your new reality.

15. Get your institutional AI strategy in place, right now. For all intents and purposes, this item is no longer optional. Enough said.

Finally, please remember that very little of this has to do with new technology.

While generative AI in higher education obviously involves new technology, it's much more about adopting a curious and human-centric approach in your institution and communities. It’s about empowering learners in new, human-oriented and innovative ways. It is, in a nutshell, about people adapting to new ways of doing things.

Happy leading, exploring and innovating with AI.

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