AI is not the solution to old problems – but the starting point for new questions

The number of conferences focused on artificial intelligence continues to grow.
Representatives from business, government, and technology are discussing digital futures, the strategic use of AI, and the deep transformation processes underway in organizations. The common message: the digital shift must move faster and with greater determination—across both the public and private sectors.

The pressure is rising.
Administrative tasks are becoming more complex, efficiency demands are increasing, cyber threats are growing, and future-proof infrastructure is urgently needed. And yet, the technological solutions are already available: chatbots for citizen services, automated administrative workflows, smart assistant systems like “co-pilots,” and data-driven decision-making tools in business contexts.

In the private sector, the stakes are just as high: maintaining competitiveness—especially in high-wage countries like Germany.
From digital onboarding in HR departments to secure cloud solutions for managing complex data flows, AI applications are now widely deployable and advancing rapidly.

The numbers speak for themselves.
According to the ifo Institute (2024), 27% of German companies are now using artificial intelligence—a doubling from the previous year (13.3%). AI adoption is particularly strong in the manufacturing sector, with 31% of companies using it. In specific industries such as automotive, electronics, pharmaceuticals, and textiles, usage even exceeds 33%.

The figures are even more striking in the service sector:
72% of advertising and market research companies use AI, as do approximately 60% of IT service providers.

Company size also significantly influences AI adoption:

• Large enterprises (250+ employees): 48%
• Medium-sized businesses (50–249 employees): 28%
• Small businesses (10–49 employees): 17%
  (Statista, 2024)

At the same time, training and upskilling opportunities are rapidly expanding.
Platforms such as KI-Campus, Mittelstand-Digital Zentrum, and the Hasso Plattner Institute offer programs that combine technical expertise with the right mindset for responsible and effective AI use.

Numerous whitepapers and guides offer support on how to integrate AI into organizations.
One example is “Leadership and Artificial Intelligence – A Guide” (Möslein et al., 2020). It analyzes the challenges and opportunities at the intersection of AI and leadership and provides concrete recommendations for executives.

But this is where the real debate begins.
Many of these guides remain focused on operational or purely technological aspects. They offer tools—but no vision. In my view, we need something more: a fundamental discourse on the strategic, ethical, and cultural integration of AI.

Because AI doesn’t just change our tools—it changes how we think, how we decide, and how we lead.

In the age of artificial intelligence, we must rethink leadership.
Leadership will no longer mean leading people alone—it will also involve orchestrating data flows, automated decision-making processes, and algorithmic systems. It’s no longer enough to introduce agile methods or integrate AI selectively. What’s needed is a new understanding of organizational architecture—one that connects technology, responsibility, participation, and foresight.

One particularly critical issue:
What happens when AI systems no longer just offer suggestions but begin to structure decisions—and humans merely approve them?
The boundary between active and passive decision-making begins to blur. When our judgments are confirmed by AI-generated data, it alters both the legitimacy and the quality of decisions.

That’s why we need new spaces for decision-making—
spaces that offer clear reasoning, traceability, and a conscious awareness of when a decision was truly made by a human—and when by a system.

Another compelling question: What will we actually still need to decide ourselves in the future?
Which tasks will be automated—and which new ones will emerge? Traditional leadership roles—whether team lead, project manager, or C-level executive—are based on the idea that organizations are steerable. Leadership meant: having knowledge, making decisions, coordinating, motivating, creating meaning, and setting priorities.

But many of these tasks can now be handled by AI.
Data-driven systems often detect patterns faster, more comprehensively, and more objectively than individuals. They make decisions more consistently and efficiently.

This raises a fundamental new question: Who takes responsibility for AI-supported decisions?
Could a decentralized system—such as one based on blockchain technology—enable responsibility to be distributed collectively, ethically, and transparently?
Maybe that still sounds like science fiction. But maybe it’s a realistic model for the coming years.

In the here and now, responsibility remains with leaders.
Their role is evolving: AI systems must not only be implemented but actively shaped, evaluated, and—when necessary—replaced. Leadership now means more than ever: thinking critically, stepping in when needed, and reflecting continuously.

Hybrid leadership is becoming a key concept.
Even in data-driven organizations, people need orientation—and a counterbalance to algorithmic rationality. Emotional intelligence, empathy, and communication skills are becoming core leadership qualities.

At the same time, new standards are needed:

• governance structures
• ethical principles
• and transparency in decision-making processes

Only when leadership is grounded in these foundations can AI truly realize its potential—not as a solution to old problems, but as a catalyst for asking new and better questions.