Nuances of strategic and operational leadership in AI

The last few months of leadership, transformation and strategy conversations have pushed me to ponder deeper on the adaptation, resistance and the in-between states of AI transformation.

AI has really pushed the boundaries of change management across organizations. Traditional change management has allowed us to strategize, organize, bring people onboard and unveil a crafted, organized plan over a period of time. However, AI change management, whether within products, services or across the enterprise, is much more a mindset transfer problem than the handover of a concrete plan.

Since AI has not yet settled into a consensus benchmark, the change process requires us to remain in an experimental state. This is a state most individuals and companies are not naturally comfortable with. There isn’t a concrete “plan” yet as to how one should pivot, because the plan itself keeps moving. What feels like the right direction today can shift within months, sometimes even weeks, as capabilities, economics and customer expectations change. So leadership is no longer about creating certainty and then driving execution against it. Sometimes it is about creating enough alignment for people to move while uncertainty still exists.

Usually, strategic leadership directions take precedence and are aligned towards closely knitted business goals and market predictions. With AI still somewhat “up in the air”, it becomes difficult to set concrete strategic goals in the traditional sense. A key distinction of change management has always been clarity being handed over to operational leadership for execution. AI challenges that clarity and requires operational leaders to remain in an open state of mind, with a much broader level of alignment than traditional operating models usually require.

The interesting part is that the boundary between strategic and operational leadership starts becoming blurry.

Operational leaders are no longer simply executing the strategy. They are experimenting, producing evidence, learning what works, and sometimes shaping what the strategy becomes next.

For many individuals, this is also not a business transformation that is distant from personal uncertainties. AI requires people to confront deeply engraved beliefs about expertise, competence, value and even professional identity. It is one thing to ask someone to learn a new tool. It is another to ask them to rethink how much of what made them valuable for the last 10, 15 or 20 years will continue to matter in the same way.

It is a challenge to help people navigate this regardless of what kind of leader you are, because the battle is internal and societal as much as it is organizational.

The uncomfortable truth is that this is probably a transformation where we may not be able to carry the majority with us at the same speed. Many leaders already recognize the “cognitive resistance” coming from very smart and capable people. When mental models have been reaffirmed for decades, skilled individuals can find it surprisingly difficult to look at a problem from a completely different and practical point of view. Expertise itself can become part of the resistance, because our confidence is usually built on patterns that have worked before. This is not necessarily unwillingness, laziness or lack of intelligence. It is a much deeper adaptation problem and one that is being discussed widely across professional circles today.

But this is also not an industry that has NOT been through transformations. If anything, change has been our constant for many decades. We have moved through platforms, architectures, programming models, delivery models and entire shifts in how software is built and consumed. The difference this time is probably the speed, proximity and personal nature of that change.

So perhaps we should stop treating AI transformation as a one-time program that needs a dramatic launch and instead find pathways to integrate change into our day-to-day lives without making huge declarations of it. Experimentation has to become part of the operating rhythm.

Sometimes, allowing people to think and decide for themselves is more powerful than trying to convince them. Asking a difficult question such as, “What do you think is going to happen?” can be a better way of grounding an organization than repeatedly telling people what we think is going to happen. What happens to software engineering? What happens to consulting? What happens to knowledge work? What happens to the parts of our jobs we have spent years becoming good at? People may arrive at very different conclusions, but sometimes making them confront the question themselves creates more movement than another transformation presentation.

Continuous showcasing of what is possible with early adopters could also be a starting point. Leaders have to be strategic, but they also have to be able to operationalize the strategy on the go. At times, the strategy itself might almost become an afterthought of the operational experimentation we run. What happens if we do this? Then we jump into action, observe what happens, learn from it and form the next direction. Somewhere inside that cycle, we find the sweet spot where change starts to feel more like enthusiasm than forced labour.

This is hard work if you are used to linear management. If your natural way of working is to think things through until dawn, come up with the answer and then act on it step by step, this kind of environment can pose a challenge.

AI leadership sometimes asks you to think and act at the same time.

To move before every question is answered. To allow operational learning to feed back into strategy continuously. To accept that a decision made today may need to be revisited without treating that as failure.

And perhaps the most interesting part of all this is culture.

Decades ago, I read the phrase “Culture eats strategy for breakfast.” I understood it then, and I still understand why it became such a powerful idea. But with AI, I dare to challenge it slightly.

With AI, you may actually have an opportunity to strategize the emergence of a new culture.

Organizations rarely get the opportunity to reshape culture deliberately. Existing habits, structures and identities are too settled. But transformations of this magnitude create disruption strong enough to reopen questions that would otherwise remain untouched.

How do we make decisions? How quickly do we experiment? How much autonomy do we give people? How comfortable are we with admitting that we do not know? How do we learn? How do we share knowledge? What do we reward? Do we reward certainty, or curiosity? Do we reward protecting what already exists, or building what comes next?

How leaders decide, act and communicate during this period will create the breeding ground for an organization’s new culture. And that culture will probably shape much more than how AI gets adopted. It may shape the organization’s survival, identity and future operating model as well.

I see this as a pivotal opportunity to transform businesses, communities, cultures and individuals positively. Probably optimistic, but I see more possibilities when I am.