Technology is advancing quickly while the institutions meant to guide its use are still finding their footing. Aurel Domi draws on insightful discussions from Davos and the Forbes Chief Marketing Officers forum to reflect on how artificial intelligence is reshaping leadership, institutions, and the future of education.
I recently joined Davos and Forbes-led conferences to be part of the wider orbit of discussions with global leaders about the future of technology.
At these global gatherings, artificial intelligence (AI) is no longer introduced with a sense of novelty; it is assumed. What remains unsettled is something more basic: how institutions, and the people who run them, are meant to keep up.
One realm in which the future of AI is hotly debated is education. If knowledge becomes outdated more quickly, what should institutions prioritise? Technical skills, adaptability, judgment?
What do the world’s thought leaders think?
In panel rooms and side conversations, the technology itself often faded into the background. Instead, the focus drifted toward structure, who makes decisions when systems become more complex, where responsibility sits when outcomes are shaped by algorithms, and how much control leaders retain. The most revealing moments were rarely the formal presentations, but the pauses that followed them.
At Forbes events, conversations about innovation tended to frame AI as a strategic tool. Executives spoke about efficiency, scale, and competitive advantage. Yet beneath that language was a quieter admission: many organisations are still improvising. The tools may be powerful, but the rules governing their use are often incomplete, and sometimes entirely absent.
One statement from a top executive stayed with me closely:
“In a world defined by constant change, how are we to be led by pupils of institutions who haven’t seen change in so long?”
The atmosphere around Davos made this statement even harder to ignore. Discussions about productivity and growth repeatedly returned to education, though often indirectly. There was little agreement on whether existing systems are equipped to prepare people for work shaped by rapidly evolving technology.
How will educational institutions respond?
Education, in these conversations, appeared less as a solution and more as a question. The big employers asked: if knowledge does become outdated quickly, are technical skills, adaptability, or judgment the greatest priority? They weren’t sure themselves and the answers varied from different perspectives, the ones who were trying to defend their legislations and the ones who were truly concerned with what’s next, but the uncertainty itself was telling.
For institutions like the London School of Economics, this moment invites reflection rather than prescription. The challenge is not simply to follow technological trends, but to help make sense of them, to understand how AI reshapes power, redistributes opportunity, and alters the conditions under which decisions are made. These are not abstract concerns. They are already shaping markets, governments, and public trust.
Within my own MSc experience, I’ve seen this shift play out in subtle but meaningful ways. AI is not treated simply as a shortcut for efficiency, but as something that changes how we approach analysis itself. In seminars, the focus moved toward interpretation and critical framing, questioning sources, assumptions, and outputs, rather than simply gathering information. The presence of AI tools has made academic work less about access to knowledge and more about judgment: how to contextualise, challenge, and refine what technology produces. That shift has been one of the most noticeable changes in the contemporary classroom.
The path ahead remains uncertain
One recurring theme across these forums was a growing unease with certainty. AI is often discussed in terms of prediction and optimisation, yet its broader consequences remain difficult to map. In that context, education begins to look less like a means of providing answers and more like a way of learning how to ask better questions.
The conversations did not resolve these tensions. If anything, they left them exposed. And that may be the most honest reflection of the moment we are in, one where technology is advancing quickly, while the institutions meant to guide its use are still finding their footing.
From my own reflections, the path forward feels less about controlling AI and more about evolving alongside it. Rather than asking how to contain the technology, the more fruitful question may be how to elevate the human capabilities that technology cannot replicate. As systems become more capable of processing information, the premium shifts toward imagination, synthesis, ethical discernment, and long-term thinking.
In both business and education, I increasingly see AI not as a replacement for expertise, but as a catalyst that forces clarity. It compels organisations to rethink how decisions are made, how value is defined, and how talent is developed. The leaders and institutions that thrive may not be those that adopt AI fastest, but those that redesign themselves most thoughtfully.
If there is a direction emerging from these global discussions, it is this: the future will belong to environments that combine technological fluency with intellectual depth. Education, in that sense, becomes less about keeping pace with innovation and more about shaping the mindset with which innovation is approached.
The path ahead may remain uncertain, but it is not directionless. It invites institutions and individuals to be deliberate architects of the systems they build, rather than passive recipients of change.
- This blog post represents the views of its author(s), not the position of the London School of Economics and Political Science Department of Management.
- Check out our other blogs on AI and education.
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The post From Davos to the Classroom: AI and the Future of Education first appeared on LSE Management.
