Part 1: The Question Behind the Questions

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This is Part 1 of a three-part series. Coming next — Part 2: The Questions That Make the Difference Visible | Part 3: What Leaders Who Answer Them Do Differently

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Most organizations are already adopting AI seriously: with awareness programs, training, investments, projects, and measured results. Early savings are visible, processes are faster, employees say AI tools help them, department heads are satisfied. Some departments operate with noticeably less effort. These are real first achievements and should not be underestimated.

But there is a question that rarely makes it onto the table in all this momentum: is this enough? Many CEOs and leadership teams I speak with sense that it is not — yet they are unsure how to move forward. Others believe they have done enough for now and will continue within the framework of next year’s strategic planning cycle. That is how they are used to working.

To both groups I offer the same nudge: find a meaningful answer to a pivotal question as soon as possible — how do you ensure that AI’s effects at the level of individual tasks, processes and departments synergistically translate into better performance of the organization as a whole? Not only for the organization itself, but above all for the customers and employees without whom the organization cannot exist — with or because of AI.

This is not a question that demands an immediate answer. It is, however, a question that reveals at which level an organization is truly engaging with AI.

And the direction in which leaders search for answers reveals more than you might expect.

Inside-Out or Outside-In?

It is natural for organizations to see the starting point of AI adoption inside the organization — in processes, departments, and workflows. This is where familiar pain points live, where opportunities are more visible, where there are always reserves in cost optimization, and where results are easier to measure and progress easier to demonstrate. This is therefore entirely clear, logical and reasonable.

What remains less visible is the view in the other direction: how AI can help understand what is happening outside the organization. How the rules of entire economies and individual markets are shifting, influencing the conditions under which organizations operate. How to continuously track what increasingly demanding customers — and now their AI agents — actually value. How an organization will retain its customers tomorrow. Where opportunities are emerging that its current business models do not yet capture.

Organizations that adopt AI predominantly inward become more efficient at doing existing work. That is not nothing. But it is only part of what is needed for organizations to remain the most attractive choice in the eyes of their customers tomorrow — and competitive against others who would take those customers away. These are the areas where AI can demonstrate its true business power, not merely its technological support to daily work.

Focused on Operations — or on Creating Value?

The answer might seem obvious. Leaders are oriented toward creating value. Yet in practice, organizations and their leaders too often follow the logic of the situation rather than their strategic intent. Operations always generate more immediate problems and therefore demand more immediate attention. They consume the focus and time that should go toward strengthening the levers for creating more and different forms of value. AI is precisely such a lever — and one of the most powerful ones available today.

At the operational level, AI optimizes how work flows. The focus is typically on reducing costs, shortening timeframes, relieving people of repetitive tasks — the classic elements of waste reduction. Results are measurable and direct. This is entirely legitimate and carries real value. But every organization is doing this, or soon will be. The question then becomes: where will the decisive advantage come from?

The answer is clear — from the ability to create higher forms of value, primarily for customers and for key employees. These two are more closely connected than they might appear at first glance. And here AI is no longer a sufficient condition on its own. This is the critical difference from using AI at the operational level alone. A harder task — but a far greater reward.

At the level of value creation, AI becomes a tool for exploring where the source of value lies in customers’ undiscovered needs, and how an organization can — with AI’s help — transform those needs and opportunities into value for customers and for itself. This demands that leadership think carefully about which forms of value the organization should create, and why. Not only how it serves customers, but what value it creates for them — and whether it could perhaps create it differently, better, with greater meaning for those who receive it.

Both levels are necessary. But the difference between them is worth understanding clearly. The operational model — the way an organization works today, internally — is historically conditioned. It gives the organization a real constraint, and organizations are often its prisoners, both physically and in terms of their thinking.

On the other side are business models. The plural is deliberate — organizations can have several, and they can build new ones in parallel. A new business model does not require dismantling what currently works. It is built alongside it. This is precisely what makes business models a form of escape from the constraints of operational and historical limitation: they open space for new forms of value without forcing an organization to abandon what is still successful today — even if there is no guarantee it will remain so tomorrow.

AI can help improve the operational model. But it is the rare organization that consciously uses it to rethink its business models — to explore which new forms of value are now within reach that were previously impossible.

This is where truly excellent collaboration between leaders and AI begins. And this is where the future of leaders lies — not in the operational model, where they are and will increasingly become replaceable.

In Part 2, we will look at the questions that open the door to this deeper reflection — and why the answers matter more than they first appear.