Leadership in the Age of AI: What Does a Leader Still Need to Know Today?

This specialist article was first published in Organisator, issue 09-10/26, on 19 October 2026.

Until just a few years ago, it was taken for granted that managers would acquire knowledge, understand interrelationships, and make decisions based on this foundation. Today, generative AI can structure market information, formulate arguments, develop scenarios, or produce an initial strategy draft within seconds.

This provides relief. At the same time, a new challenge arises: those who have not developed the answer themselves must still be able to assess whether it is correct, relevant, and suitable for their own organisation. Knowledge therefore does not lose its importance; rather, its function changes.

For managers, this does not mean that they need to know less simply because AI knows more. On the contrary, their knowledge of culture, strategy, structures, and people within their own organisation becomes the crucial frame of reference for interpreting AI results and meaningfully applying them within the business context.

1. Asking the right questions

AI excels at generating answers. However, whether these answers are useful depends on how well the underlying problem has been understood. If a vaguely defined problem is submitted to an AI system, it will usually return only a quickly formulated ambiguity.

For managers, the ability to clearly define problems is therefore becoming increasingly important: What do we really want to achieve? What assumptions are we making? Which interests are in conflict? And what information is missing?

We usually notice quickly when a result does not convince us. It is more challenging, however, to clearly identify the underlying problem and to derive the right questions from it. This ability is becoming a key competence for managers, whether in dealing with AI or in conversations with employees.

2. Assessing plausibility

A convincingly worded answer is not necessarily a good answer. AI can condense information and present arguments coherently, even when assumptions are incorrect, data is incomplete, or conclusions are overly general.

Managers do not need to master every detail themselves. However, they do require sufficient specialist and contextual knowledge to be able to ask questions, challenge decisions, and assess quality.

A manager requires sufficient expertise to assess whether an AI-generated result is heading in the right direction. Equally important is their knowledge of strategy, structures, processes, and people within their own organisation. Only with this understanding can they judge whether a proposal will actually work in practice.

3. Identifying connections

Business decisions are rarely made in isolation. A cost reduction may affect service quality, motivation, or customer loyalty. The introduction of a new technology changes processes, roles, and expectations. In such cases, even the best individual solution is not sufficient.

Leadership involves placing information within a broader context: what does a decision mean for customers, employees, finances, and the long-term positioning of the organisation? Making these connections relies not only on knowledge, but also on experience and judgement.

Every organisation has its own unique DNA. Experience and knowledge of the company therefore play a crucial role in determining whether change will succeed. Effective leadership arises from the interplay between an individual’s skills and experience and the structures within which they operate.

4. Making Decisions Under Uncertainty

AI can compare options, structure risks, and formulate scenarios. However, it does not relieve a manager of the responsibility for making decisions. Especially in SMEs, there is often no perfect data available and no single correct solution.

Leadership is demonstrated by the ability to tolerate uncertainty, to make conflicting objectives transparent, and yet still to reach a decision. Technology can support this work, but it cannot replace it.

A sound decision combines a solid data foundation with the practical experience of a leader.

5. Understanding People and Organisations

Many changes do not fail because of a lack of technically correct solutions, but rather due to the way they are implemented within the organisation. Employees need to understand new roles, adapt their habits, and develop trust in new processes. Customers, too, do not always respond as a model might predict.

This is why social and organisational skills remain essential: listening, recognising conflicts, understanding interests, providing guidance, and shaping change in such a way that people are able to support it.

The more organisations undergo change, the more important it becomes to listen carefully and observe how these changes affect both individuals and the organisation as a whole. Those who are able to recognise such signals from their environment are better equipped to manage change effectively and embed it sustainably.

Not less knowledge, but different knowledge

The leader of the future does not need to have every answer. However, they must be able to recognise which answers can be trusted, which questions have yet to be asked, and when a decision requires more than a well-formulated recommendation.

The more powerful AI becomes, the less knowledge is an advantage simply by possessing it. What matters is how leaders interpret, connect, and translate it into responsible action.