On a quiet morning (not sure it was quiet, but it sounds poetic enough to keep you reading 😊), I sat down with a cup of coffee and a simple objective: to satisfy my curiosity with learning, testing and simply “playing” with the latest generation of different AI tools. I wanted to understand their strengths, their limitations and whether they offered anything beyond the growing hype surrounding AI. I know they can help me prepare for strategic discussions. Or challenge my thinking. Or look at a problem from a different angle. But can they really save my time without sacrificing judgement?

What I discovered was not the technology itself that caught my attention. It made or even forced me thinking about it from different angle. In this case, less about the tools themselves and more about the people using them. As I closed my laptop few hours later, one thought stayed with me: perhaps the most important consequence of AI will not be the gap it creates between companies. I honestly believe it will be the gap it creates between people, between leaders.

Over the last year, hardly a week has gone by without conversations about AI. Boardrooms, conferences and leadership meetings are full of discussions about transformation, governance, regulation and competitive advantage. Yet I increasingly feel that many organisations are looking at the wrong question. Most leaders still ask how AI will change their company, while more relevant question might be: how is AI already changing individual leaders?

For decades, organisations are worried about access to technology. Companies with better systems, larger budgets or stronger technical capabilities often enjoyed a significant advantage. Today, however, access is becoming less important. The most powerful AI tools are available to organisations of almost every size. Whether it is Microsoft Copilot, ChatGPT, Gemini, Claude or another platform, the fundamental technology is no longer reserved for a select few.

The playing field has become remarkably same for all. What is becoming increasingly unequal is how people choose to use these tools.

In conversations with either colleagues (I call them “genuine AI explorers”) or executives across industries, I often notice a pattern. The difference is rarely age. It is rarely industry experience and it is certainly not determined by technical expertise. The biggest difference is curiosity.

Some leaders actively experiment. They test ideas, they challenge AI responses, they incorporate the technology into their daily workflows, they use it to prepare for meetings, analyse market trends, review contracts, structure strategic discussions or explore alternative perspectives before making decisions.  

Others continue to view AI as an interesting topic, but not yet a practical one. The consequences of that choice may seem insignificant today. Initially, the gap appears small. Over time, I guess it widens.

In “pre-AI” era, we all had the same 24 hours in a day. We all relied on teams, advisors, experience and judgement to make decisions.

Today, well we still have 24 hours a day, but some use these 24 hours to effectively work alongside a collection of virtual assistants that help them gather information, challenge assumptions and accelerate routine cognitive work. Others still do everything manually.

This does not make one group more intelligent than the other. It does, however, make them operate at different speeds. And speed matters tremendously.

Not because faster decisions are always better, but because modern business environments rarely reward delayed learning. Leaders are increasingly expected to absorb large volumes of information, understand regulatory developments, monitor competitors, evaluate new business opportunities and make decisions with incomplete data.

The challenge here is not a lack of information, the challenge is processing it. This is where AI can quietly reshape leadership effectiveness. Not by replacing judgement, but by expanding capacity.

The executives who seem to benefit most from AI are not using it to find answers. They use it to ask better questions. They treat it as a conversation partner, a sounding board or a research assistant. They remain responsible for the final decision, but they arrive at that decision with broader context and often in considerably less time.

We spend considerable energy debating whether AI will replace jobs, departments or entire professions. While those discussions undoubtedly have value, they sometimes distract us from a more immediate reality. 

One of the most significant risk for many organisations (at least in my opinion) is not that AI initiatives will fail, it is that companies will adopt it unevenly. Imagine two managers with similar responsibilities, comparable experience and equally talented teams. One actively incorporates AI into daily work. The other does not. After a week, the difference is hardly noticeable. After a month, there are small variations in preparation quality and responsiveness. After a year, the gap can become substantial. Both may hold the same job title. But they are no longer operating in the same way.

One book I often recommend in this context is Ethan Mollick's Co-Intelligence. What makes it particularly relevant is its central idea that the future belongs neither to AI alone nor to humans alone, but to those who learn how to work effectively with both. From what I observe in executive teams today, the same increasingly applies to leadership itself.

The solution is not another technology project. Nor is it a complex AI strategy document. In my experience, it starts with leadership behaviour. When executives openly experiment, share lessons learned and demonstrate practical uses of AI in their own work, adoption tends to spread naturally. When leaders treat AI as somebody else's responsibility, the rest of the organisation usually follows that example as well.

The most successful organisations are the organisations whose leaders remain curious. They encourage experimentation while maintaining appropriate governance. They recognise that understanding AI does not require becoming a data scientist. It requires developing enough familiarity to understand where value can realistically be created and where limitations still exist. Most importantly, they accept that waiting for complete certainty is unrealistic. 

After all said, what I consider matters the most is whether leaders develop the habit of learning, experimenting and continuously rethinking how work gets done. And leaders and executives who learn how to work alongside AI may eventually outperform those who do not.

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