When electricity was introduced in the US as an alternative to steam in powering factories in the early 20th century, there were few early adopters. And those who were willing to make the leap simply replaced the central, steam-driven engines with electrical motors. It took fifty years for manufacturers to understand that they didn‘t just need to swap out the power supply: electricity enabled them to rethink entire working practices and processes to realize productivity gains.
As economist Tim Harford noted in his account of the steam-to-electricity transition, Why didn’t electricity immediately change manufacturing?: “The thing about a revolutionary technology is that it changes everything—that’s why we call it revolutionary.”
Artificial intelligence (AI) is our moment’s electricity. It’s transforming how people learn and work—and how educational institutions respond to this transformation matters. As with the early age of electricity, just swapping out the tools isn’t enough. The opportunity and the challenge for higher education is to reimagine its systems, practices, and cultures around AI.
To lead in this new era, educational institutions must advance on three fronts: capacity building, network activation, and systems change.
1. Capacity Building: Developing Institutional and Human Readiness
AI’s impact on the workforce will be profound—not because it can automate tasks, but because it can redefine how humans do their best work. For colleges and universities, that begins with capacity building: preparing learners, educators, and institutions to thrive in an AI-infused world.
For students, capacity means more than technical skill. It means data literacy, critical thinking, and ethical reasoning—the ability to use AI thoughtfully and responsibly. For educators, it means rethinking pedagogy, experimenting with generative tools, and embedding AI fluency across disciplines.
Institutional capacity matters just as much. Leaders need to invest in faculty development, foster experimentation, and signal that innovation is a welcome part of the academic mission. As LinkedIn Chief Operating Officer Dan Shapero has noted, “80% of C-suite executives globally believe AI will kickstart a culture shift where teams are more innovative… Getting employees to use AI is key to realizing these gains.”
The same holds true in education: empowering faculty, staff, and students to engage with AI—not as a threat, but as a catalyst for creativity—is essential to realizing its promise. We have to ask ourselves: if we were starting from a blank slate, how would we rethink structure, pedagogy and curriculum to encourage transformation, both structurally and from within? How would we utilize new tools and knowledge to achieve dramatically better learning outcomes at scale?
2. Network Activation: Aligning Education with a Changing World of Work
Workforce development depends on partnerships—and AI can amplify the development and complexity of these relationships. Labor markets are shifting faster than curriculum cycles, and the skills in demand today may evolve within months. Higher education must move from episodic collaboration to continuous network activation: agile partnerships that connect institutions, employers, and communities in real time.
Through shared data, co-designed credentials, and applied learning pathways, universities can not only ensure graduates are prepared for the evolving workplace. AI can be used to help identify emerging skills, assess program effectiveness, and reveal regional trends, delivering deeper value to human collaboration.
When education leaders convene cross-sector partners—industry leaders, employers, and policymakers—they help align innovation with opportunity and equity. More students find clearer paths to jobs, more employers gain confidence in talent pipelines, and more communities are presented with inclusive access to opportunities.
In this role, colleges and universities become network anchors — translating innovation into impact.
3. Systems Change: Redesigning the Learning/Working Ecosystem
Revolutionary technologies don’t just improve systems, they create new ones. For higher education, systems change means reimagining credentials, curricula, and pathways for a world of continuous transformation. It means modular programs that adapt to new industries, recognizing learning that happens outside classrooms, and designing ecosystems that support lifelong learners who must reskill continuously. It means, as noted above, reconfiguring our notions of capacity.
It also demands an ethical compass. Absent real leadership, AI could widen inequities. Institutions must model responsible AI use—from transparent data practices to inclusive design—and ensure the next generation can do the same.
Above all, systems change requires higher education to operate as a learning organization: nimble, data-informed, and oriented toward continuous improvement. The transformation ahead is as organizational as it is technological.
Leading the Human Side of the AI Revolution
AI represents both a technological revolution and a leadership test. It challenges higher education to connect its enduring mission—expanding knowledge and opportunity—with a rapidly changing future of work.
The institutions that succeed will do what the best early adopters of electricity did: not just replace old engines with new, but redesign themselves around what’s newly possible.
By building capacity, leaders prepare people and institutions to adapt and innovate. By activating networks, they align learning with economic and civic needs. And by driving systems change, they ensure education remains the foundation of shared progress in the age of AI.
As Harford reminds us, revolutionary technologies “change everything.” The question before higher education is whether it will let that change happen or lead it.
Want a practical way to reflect on these ideas?
Want a practical way to reflect on these ideas?
Download Reimagining Learning in the Age of AI, a leadership guide with prompts and frameworks to help decision-makers assess readiness, clarify alignment, and plan next steps.
