I wasn’t at Davos this year, but I might as well have been for the richness of what I heard from those who were.

Every conversation relayed back to me carried the same unmistakable shift in tone. The old questions, the ones about what artificial intelligence (AI) is, or whether it would ever deliver real return on investment (ROI), were nowhere to be found.

Instead, CEOs, board chairs, and transformation leads seemed preoccupied with something far more human. They kept asking a variation of a single question ‘How do I bring my people along?’

What struck me most was how unanimously attendees described this pivot. They said the mood felt different, quieter but sharper, as if leaders were finally confronting the reality that technology is not the hard part of AI transformation, people are.

They spoke about a fireside chat with an amazing leader, where the conversation drifted from technical capabilities to leadership psychology. They described dinner tables filled with executives who, despite leading some of the world’s most capable organisations, admitted privately that they felt underprepared for the cultural implications of AI.

And in their stories, a pattern emerged: the real challenge today isn’t the adoption of AI tools, but the reshaping of identity, trust, and purpose inside workforces that are unsure of what the future means for them.

Participants told me that the “let a thousand flowers bloom” stage of AI, the era of bottom up experimentation, has reached its natural ceiling. Many organisations have had brilliant proofs of concept that never scaled, pockets of innovation that never connected to strategy.

Leaders now feel the weight of making top down choices about where AI can create a genuine competitive advantage and committing meaningful resources to those few bets. Yet even with those strategic choices becoming clearer, attendees said the more difficult work is managing the emotional and social dynamics of integrating AI alongside humans.

One founder shared a story illustrating this perfectly. His company introduced AI-driven forecasting tools into their operations team, and at first, the human analysts felt quietly threatened.

Rather than pushing the technology harder, he paused the rollout and created open Q&A forums where people could voice anxieties without judgment. He went further and created new roles, hybrid positions where analysts could pair their domain knowledge with AI oversight responsibilities.

Within months, analysts were championing the system. In his words, “They stopped seeing AI as a rival and started seeing it as an amplifier.” That transformation didn’t come from the tool; it came from how people were invited into the change.

A manufacturing executive described a similar experience. He had been tasked with deploying predictive-maintenance AI across a network of plants. Instead of centralising the work, he created cross functional groups on each site, including engineers, frontline operators, supervisors, and data scientists learning together.

One attendee told me that those groups felt like “AI guilds,” places where experimentation was allowed to be messy, where people laughed at the model’s wrong predictions, and where local teams shaped the final workflow.

By the time the system went live, it didn’t feel imposed; it felt co created. Productivity rose, yes, but the more interesting outcome was the cultural cohesion that took root.

I also heard a powerful story from healthcare. A CIO described integrating an AI triage assistant into clinical workflows, something that could easily provoke fear among clinicians. Instead of treating the tool as an established reality, she convened a clinician advisory council with representatives from nursing, emergency medicine, radiology, and operations.

She insisted on radical transparency, where the algorithm was strong, where it was limited, where uncertainty remained. She encouraged clinicians to challenge AI outputs in early pilots rather than defer to them. By the time the tool was deployed, the clinicians trusted the process because the process had trusted them.

What fascinated me in all these accounts was how much emphasis leaders placed on their own learning. One attendee said that a speaker remarked that executives learning to code, not to become developers, but to reshape their thinking, to understand systems, constraints, and possibilities from the inside out.

Several CEOs at Davos took this to heart. One even documented his journey through an introductory coding course, mistakes and all, as a way to signal that curiosity and vulnerability were now leadership expectations. His teams responded by adopting the same mindset; learning became something shared, not delegated.

The common thread running through every story I heard was the idea that bringing people along is not the soft side of AI transformation. It is the transformation. Leaders who succeed are the ones who choose visibility over mystique, inclusion over imposition, and learning over expertise.

They create languages of change that resonate with people, not just with systems. And they treat cultural fluency as a strategic asset.

I was also struck by how participants talked about the democratisation of AI knowledge.

MIT releasing its entire AI library for free came up repeatedly in conversations. People mentioned how accessible the materials were, dense but practical, better than many paid courses, and how this shift symbolised something important.

AI understanding is no longer confined to technical elites. Anyone with curiosity and a browser can meaningfully engage.

The leaders who embrace this democratisation and encourage teams to learn, not as a one off initiative but as a cultural norm, will see their people move from apprehension to agency.

The more I listened to these accounts, the clearer it became that the speed of technological change is no longer the problem. The problem is ensuring that people feel capable of moving with it.

The real work now is cultivating cultures where humans and intelligent systems elevate one another, where experimentation feels safe, where learning is visible, and where the future isn’t happening to people but with them.

Even from a distance, Davos made something unmistakably clear to me: this moment will be defined not by how quickly organisations adopt AI, but by how compassionately and courageously leaders help their people grow into an AI powered world.

The author is Adviser to the President – Aga Khan University & Executive CDIO – NHS West Yorkshire ICB