Between Countries, Between Sunsets, and the Future of AI Transformation
I started traveling during university.
At first, it was simple curiosity. I wanted to discover new places, experience different cultures, visit museums, understand people whose lives looked completely different from my own. Over time, travel became more than an occasional adventure. It became a way of living.
Over the last two decades, I have found myself moving repeatedly between countries, cultures, and ways of life. Some months were spent navigating the intensity, speed, and ambition of cities like London and New York. Others unfolded along the Mediterranean coast, where the rhythm of life seemed governed less by calendars and deadlines and more by the movement of the sun across the horizon.
For years, I believed these experiences were teaching me how to travel.
Looking back, I realize they were teaching me something far more valuable: how to adapt.
The interesting thing about arriving in a new country is that very little of what you know becomes wrong. Most of it simply becomes incomplete. The assumptions that once felt universal suddenly reveal themselves as local. The same words carry different meanings. Behaviors that are rewarded in one environment may be misunderstood in another. You begin to notice how much of your confidence was built not on certainty, but on familiarity.
Every move requires a small adjustment. A willingness to observe before acting. A willingness to question assumptions that previously went unchallenged. Over time, these adjustments accumulate into something deeper. You become more comfortable operating without having all the answers. You learn to trust your ability to navigate uncertainty rather than your ability to eliminate it.
Recently, during a conversation about Artificial Intelligence and organizational transformation, a simple question stopped me for a moment: “We can build almost anything from a technology perspective today. But what is the business actually ready to adopt?”
The question stayed with me because it exposed a tension I see repeatedly across organizations.
For the past two years, the conversation around AI has been dominated by technology. We talk about models, platforms, capabilities, use cases, and productivity gains. We discuss what AI can do today and speculate about what it may be able to do tomorrow.
Yet increasingly, the technology itself is no longer the primary constraint.
The real challenge begins after the technology arrives. The numbers point in the same direction. According to McKinsey’s latest research, 78% of organizations now use AI in at least one function. Yet only 1% describe their AI initiatives as fully mature. The challenge is whether they can adapt quickly enough to realize the value.
Organizations often approach AI transformation as a technology deployment challenge. Once the platform is selected, the roadmap defined, and the training delivered, success is expected to follow naturally.
In reality, the most difficult part of transformation starts precisely at that moment. AI does not simply introduce new tools. It changes the environment in which people operate. It challenges long-standing assumptions about expertise, decision-making, and value creation. It forces individuals and teams to reconsider habits that may have served them well for years. More importantly, it asks people to remain effective while the rules around them are changing.
This is why many transformation initiatives struggle despite strong technology foundations. The obstacle is rarely capability. The real obstacle is adaptation.
The scale of adaptation required is difficult to ignore. The World Economic Forum estimates that 59% of the global workforce will require reskilling or upskilling by 2030. The conversation is about helping people navigate change at an unprecedented scale.
For decades, professional success was largely associated with accumulation. More knowledge, more experience, more expertise. Organizations rewarded individuals who could provide answers based on what they had learned over time.
Artificial Intelligence is subtly changing that equation.
When information becomes instantly accessible and routine cognitive tasks become increasingly automated, the most valuable individuals are not necessarily those who possess the most knowledge. Increasingly, they are those who can learn, unlearn, and relearn quickly. They are comfortable questioning established assumptions and adjusting their thinking when circumstances change.
In other words, they possess the same qualities that frequent travellers develop over time.
The ability to remain curious when confronted with something unfamiliar.
The ability to operate effectively without complete certainty.
The ability to adapt before adaptation becomes unavoidable.
This is where I believe traditional approaches to change management must evolve.
Historically, change management focused on communication, stakeholder engagement, and training. These elements remain essential, but they address only part of the challenge.
AI introduces something deeper. Organizations are not simply asking people to learn a new process. They are asking them to redefine how they create value. They are asking experienced professionals to rethink assumptions that may have guided their careers for decades. They are asking teams to develop new relationships with expertise, decision-making, and collaboration.
These are not technical transitions. They are human transitions.
William Bridges famously argued that every transition begins with an ending. Before something new can emerge, something familiar must be left behind. This is precisely what many organizations underestimate.
Leadership often focuses on the beginning: the launch of a new platform, the implementation of a new capability, the announcement of a new strategy.
Employees experience the ending. The ending of familiar routines. The ending of established ways of working. Sometimes even the ending of identities built around expertise accumulated over many years.
Until organizations recognize this reality, adoption will continue to lag behind ambition.
The companies that thrive in the age of AI will not necessarily be those with access to the most sophisticated technology. Technology is becoming increasingly available to everyone.
The differentiator will be the capacity to adapt.
The capability of leaders, teams, and organizations to remain effective while continuously evolving.
Perhaps that is why some of the most important lessons about AI have very little to do with technology itself.
Sometimes they emerge in boardrooms and strategy discussions.
Sometimes they emerge through conversations about organizational change.
And sometimes they appear unexpectedly while standing in a different country, watching another sunset, realizing that adaptation is not a phase of transformation.
It is the foundation that makes transformation possible.
