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Use It or Lose It: How AI and Digital Tools May Be Changing Our Brains

Use It or Lose It: How AI and Digital Tools May Be Changing Our Brains

by Florian Biedermann | May 26, 2026 | Leadership and AI, Leadership Tips, Learning Transfer | 0 comments

Use It or Lose It: How AI and Digital Tools May Be Changing Our Brains

You travel to Madrid and want to chat with the locals, but you realize that after five years without practice, your rudimentary Spanish skills are now practically nonexistent and you even struggle to ask for directions. Then you try to find your way using a paper city map and notice that without GPS navigation, you are completely lost when it comes to finding the nearest tapas bar. This phenomenon can be extended in many directions: Your physical condition deteriorates rapidly without exercise, and your mental sharpness declines if your daily life consists solely of TikTok videos. Simply put, “use it or lose it” – both your muscles and your brain lose their abilities if you stop using them.

The Hidden Cost of Convenience and Digital Dependence

This natural selection of our abilities has, of course, existed since the dawn of humanity and affects everyone equally. In recent years, however, our lives have changed significantly in terms of convenience and the outsourcing of skills and knowledge. Especially due to apps like Google Maps, as well as functions such as autocorrect, we no longer have to make much effort and thus gradually lose both cognitive and physical abilities – our handwriting says it all.

We have all likely made this observation, both in ourselves and in others, but I have often wondered whether this is merely a subjective impression or a real phenomenon. In other words, are there reliable studies showing that the excessive use of tools gradually causes us to lose our cognitive abilities?

“There is a hotly debated but widely accepted consensus that the increasing use of navigation aids is accompanied by a decline in our cognitive navigation abilities,” explained PD Dr. Kai Hamburger from the Department of General Psychology and Cognitive Research at Justus Liebig University Giessen (JLU) as early as 2023. The same applies to handwriting, which activates the brain more than typing; teachers observe that less handwriting correlates with poor spelling. And regular GPS use leads to measurable declines in spatial memory and an accelerated loss of navigation-related skills.

How AI Is Reshaping Critical Thinking and Human Interaction

So far, so bad – but since 2022, we have had a new sparring partner in our lives that makes many things easier and takes a lot off our hands: Artificial Intelligence (AI).

Compared to autocorrect, text prediction, or GPS, AI tools offer a vast array of functions that can significantly impact our lives. This also affects critical thinking and conscious decision-making, which we are increasingly happy to “ask the AI” to handle for us. Instead of doing our own research, we use AI for ideas, texts, and problem-solving. And when we systematically delegate decisions and evaluations, we train our own judgment and creativity less and less, placing ourselves in ever greater dependence on AI.

Furthermore, depending on how it is used, AI can also have significant effects on our personal development and social skills. More and more people are using chatbots, avatars, and social AI tools as conversation partners, advisors, and sometimes even as friends. And because AI generally agrees with you and does what you tell it to, it is likely only a matter of time before we gradually lose our ability to engage in critical discourse, resolve conflicts, clear up misunderstandings, and build relationships and empathy.

MIT Study: What Happens to the Brain When We Use ChatGPT?

Media scientists at the Massachusetts Institute of Technology (MIT) conducted a study on this topic in 2025 and published it under the title “Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Tasks.”

More than 50 American adults between the ages of 18 and 39 participated in this small study. The participants were asked to write four essays over a four-month period, using:

  • ChatGPT
  • A search engine such as Google or Yahoo!
  • Their own brains (without search or AI tools)

Electroencephalography (EEG) was used to record the participants’ brain activity in order to assess their cognitive engagement and mental effort, and to gain a deeper understanding of neural activation during the essay task.

For the first three essays, electrical connectivity in the ChatGPT group’s brains was lower than in the other two groups. It was also lower in the search engine group than in the group that used only their own brains.

For the final essay, the groups were swapped: The “brain-only” group was now allowed to use ChatGPT, and the ChatGPT group was required to rely only on their brains. The group that had switched from using ChatGPT to relying solely on their own thinking showed significantly lower electrical connectivity in the brain than the “brain-only” group had in their third session, reported a reduced sense of personal responsibility for what they wrote, and showed a poorer ability to recall quotes from the essay they had written.

The Cognitive Risks of Overusing AI Tools

According to a 2024 research review, an increasing reliance on AI assistants and digital tools when performing tasks that require deeper thinking can entail the following risks:

  • Reduced mental engagement
  • Neglect of cognitive abilities such as arithmetic or information retrieval
  • Declining memory
  • Shorter attention spans and concentration problems
  • Inability to apply knowledge to new situations
  • Ethical and social concerns, such as reduced interpersonal interaction and social isolation
  • Mental health challenges, such as reduced self-confidence

The Cognitive Risks of Overusing AI Tools

Does AI Make Us Less Intelligent?

So does AI make us less independent or even dumber?

The answer is yes and no: excessive use of and reliance on AI technology can profoundly impair our understanding and critical thinking skills, but it does not have to be that way – it always depends on how and how often these tools are used.

On the other hand, AI is not inherently bad. When used correctly, it can certainly stimulate our creativity and promote learning. When applied appropriately – such as in cancer screening – it can work wonders.

It is therefore not simply a matter of “using AI less”; what is most important is that, for tasks requiring deeper thinking, we primarily use our own brains and employ AI at most as a supporting aid. When used correctly, it can even help foster deeper thinking, stimulate creativity, and increase efficiency.

How to Use AI Without Losing Your Cognitive Abilities

1. Think for Yourself First, Then Use AI

  • First formulate your own ideas or answers, then use AI to supplement them, find counterarguments, or uncover blind spots.
  • Use AI as a “sparring partner”: it can provide alternative perspectives, pros and cons, or additional hypotheses that you consciously examine and evaluate.
  • Practice conscious reflection: always view AI’s responses as suggestions and actively question them (“What is accurate here, what is missing, and what do I see differently?”).

2. Use AI as a Starting Point for Research

  • Use AI for initial structuring, clarification of terms, or exploring a topic – then move on to primary sources, studies, and specialist texts.
  • Practice source criticism: consciously compare AI answers with other sources to assess validity, timeliness, and quality – this strengthens critical thinking.
  • Promote metacognitive learning: obtain an answer from AI first and then analyze it critically (“What did it leave out? What is unclear? What sources would we need for this?”).

3. Use AI for Analysis, Not as a Shortcut

  • Identify patterns that are hard to spot on your own: AI can quickly analyze large amounts of data or complex patterns – you then consciously use the results to make decisions.
  • Run through scenarios: ask AI “what if?” questions in strategy, change management, or product development and use the variations as a basis for team discussion.
  • Delegate operational tasks, retain the thinking: outsource repetitive tasks such as sorting, transcribing, or formatting to AI in order to reserve your cognitive resources for conceptualization, evaluation, and creative decisions.

4. AI as an Idea Generator, Not an Idea Replacement

  • First create your own drafts, then use AI to generate variations, stylistic ideas, or examples.
  • Simulate a change of perspective: ask AI to argue from the perspective of other stakeholders – this fosters empathy and systems thinking when you actively evaluate its input.
  • Use AI as a writing coach instead of a ghostwriter: ask for feedback on clarity, structure, or tone instead of having it write entire texts.

5. AI as an Assistant, Not an Autopilot

  • Use AI as an assistant that provides inspiration but does not take over your entire thought process.
  • Brain first, then prompt: spend 2–3 minutes thinking or sketching out ideas yourself before asking AI.
  • Use AI judiciously: accelerate complex or time-sensitive tasks with AI, but consciously handle simple everyday tasks without AI to maintain basic skills.

The Future of AI: Benefit or Dependency?

Will we adhere to such rules? Some of us, for whom it is important to keep training as many of our faculties as possible and to avoid dependence on technical tools, will certainly use AI wisely. But for humanity as a whole, I honestly see a rather bleak future. Too many inventions that were originally intended for a positive purpose have unfortunately been turned into the exact opposite in reality.

One example of this is Alfred Nobel’s invention of dynamite. It was originally developed as a safer alternative to nitroglycerin in order to facilitate tunneling, road construction, and mining, and to protect human lives. Yet in reality, dynamite is used less often for meaningful civilian purposes than for destroying things and killing people.

What was once intended for bridge-building is more frequently used to destroy bridges.

Not least for this reason, Alfred Nobel established a foundation to counter his negative image as a “merchant of death” and to do some good for the world by honoring people who have rendered outstanding service to humanity.

May AI also bring more benefit than destruction in the future – it is still in our hands.

Florian Biedermann

Florian Biedermann

Learning & Development Consultant at MDI

Florian Biedermann is a Learning & Development Consultant at MDI (Management Development Institute) – a global consulting company that offers solutions for leadership development. His focus is on making complex issues understandable and inspiring people to think – and act. Florian previously worked for many years as an author and manager in the e-learning sector, after spending over a decade as a freelance journalist.

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How Artificial Intelligence Shapes Who We Become

How Artificial Intelligence Shapes Who We Become

by Meike Hinnenberg | May 19, 2026 | Impuls series, Leadership and AI, Leadership in the digital transformation | 0 comments

How Artificial Intelligence Shapes Who We Become

Meike’s Reflections on Artificial Intelligence

Do you prefer to listen to this article? Click below to access our AI-generated audio version!

How Artificial Intelligence Shapes Who We Become

Meike’s Reflections on Artificial Intelligence

This is the forth part of MDI’s leadership architect Meike Hinnenberg’s new blog reflection series on AI. You can find the previous parts on our blog site! Stay tuned for more 🙂

How Artificial Intelligence Shapes Who We Become | Lines of Subjectivation

Maybe the most certain of all philosophical problems is the problem of the present time and of what we are in this very moment. (Michel Foucault: The Subject and Power) The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are undistinguishable from it. (Marc Weiser: The computer for the 21st Century)

The Hidden Labor Behind AI – A Berlin Exhibition

May 2026 in Berlin; spring has arrived. Light enters the room, and a quiet warmth settles in the apartment. The window is slightly open. I sit at the same table. Again, coffee – dark, dense, almost earthy in its intensity – fills the room.

While I follow its taste, while I continue working on this text, fragments of the exhibition The Language of Soil, which I visited earlier today, return. In this installation, the artist Anna Ehrenstein directs attention to platform workers in Nairobi, Congo, and Egypt – workers who sustain what is called Artificial Intelligence, and for whom Jeff Bezos once used the phrase “artificial artificial intelligence.”

Employed by outsourcing partners of Big Tech companies, their work remains largely unseen. The exhibition brings together interviews, workshops, and collective narrative formats in a 220° video installation, rendering perceptible the “interplay of (post-)colonial continuities, global economies, and the labor that underpins algorithmic systems“.

Voices from the Invisible Infrastructure

I am watching. I am listening. A father, closely connected to his family, now estranged from his daughter; her presence recalls the CSAM he is required to review and label each day. A woman working as a content moderator, checking and filtering visual material from an armed conflict that has also affected her own family, from whom she has had no sign of life. Syrian refugees, shaped by war, displacement, and flight, now labeling sequences of images – war, torture, suicide, rape, child abuse – images that do not remain external, but return.

Micro-Tasks, Micro-Pay

Payment is calculated per micro-task. Ten, twenty, twenty-five cents. Sometimes less. It accumulates slowly, often to less than two dollars per hour. Contracts remain short. They are extended or they are not. Refusal is possible, but not without consequence. Continuity depends on compliance. The work moves; the workers remain replaceable.

Where Do I Stand in This Formation?

As I follow this movement of memories, questions begin to insist: Where am I located in the formation I am trying to describe? How am I affected by it? How do I relate to it?

Has something like an exterior position been gained through thinking the dispositif of Artificial Intelligence, through its lines of visibility and enunciation? Is this now a stable place from which I can speak with a certain autonomy, perhaps even judge it? Or is this, too, only a movement within the same singular and historically situated configuration?

The Illusion of the Exterior Subject

It would be tempting to assume that what has come into view simply persists as knowledge at my disposal, while I myself remain unaffected. Such a perspective preserves the familiar figure of an exterior, self-assured subject and a stable reality upon which it acts by means of technology. And yet this assumption falters. If Artificial Intelligence is approached not merely as a set of tools but as a condition of world-disclosure, the situation becomes more complex.

If the preceding analysis marks a shift in the conditions of seeing and saying, if what appeared self-evident is shown to depend on structured exclusions, then this shift cannot be limited to the object. It implicates the position of the one who sees and speaks, and with it the conditions under which others remain unseen and unheard. What comes into view does not simply add itself to knowledge; it alters the field in which both subject and world take shape.

Foucault and the Making of Subjects

The man described […], whom we are invited to free, is already in himself the effect of a subjection much more profound than himself.

Michel Foucault’s understood his own work as an attempt “to create a history of the different modes by which, in our culture, human beings are made subjects.” (Michel Foucault: Subject and Power) We are not subjects prior to these processes. We are born into historically specific arrangements, dispositifs, within which we speak and are spoken about, see and are seen, act and are acted upon. Even if this idea is an affront to one’s ego, subjectivity does not precede these relations; it takes shape within them. Or, in a Deleuzian inflection: we are continually in processes of becoming-subject.

Lines of Visibility: Who Gets to Appear

If lines of visibility are conditions of perception – if they determine what can appear, in what form, and from which position – then they do not merely organize objects. They also distribute subjects: they situate them, relate them to one another, and define the positions from which something like a “self” can emerge within a given regime of visibility.

Lines of Enunciation: Who Gets to Speak

If lines of enunciation are conditions of sayability – if they determine who or what can speak, where agency is grammatically and conceptually placed, what can be said and in what form it becomes meaningful – then they also affect the subject. For those who speak are not exterior to these conditions.

They take shape within them. What can be articulated, and from which position it can appear as intelligible, does not simply structure discourse; it structures the one who speaks. Subjectivity emerges here not as origin, but as effect: as something formed within a field of available statements, distinctions, and attributions of agency.

To speak is therefore not only to express oneself, but to enter a space already organized in advance, to adopt positions, to repeat or displace existing formulations, to inhabit or refuse a grammar that distributes agency and responsibility. What appears as a self speaking is inseparable from the conditions of enunciation through which it becomes legible, both to others and to itself.

Lines of Enunciation: Who Gets to Speak

Lines of Subjectivation in the Dispositif of Artificial Intelligence

In a Deleuzian sense, lines of subjectivation do not designate identities or inner states. They are trajectories through which subjects are produced: ways in which beings are called into relation with themselves, assigned positions of responsibility, and made capable or incapable of acting, speaking or refusing. They are neither purely imposed nor freely chosen, but emerge in the interplay of practices, norms, and material arrangements.

Within the dispositif of Artificial Intelligence, such lines are not peripheral; they are constitutive of its operation. They do not merely run alongside technical systems but traverse them, linking infrastructures of computation with everyday forms of self-relation.

We are simultaneously involved in their production and their effects: by generating data, labeling and rating outputs, prompting and correcting systems, but also by adopting Artificial Intelligence as interface, infrastructure and environment. At the same time, we are produced through these same relations and practices – as users, data subjects, workers, experts, and objects of prediction.

The Figure of the User

One dominant line of subjectivation produces the figure of the user. Here, the subject is addressed as an interacting point within a system, defined through traces of behavior and patterns of response. Agency is not denied but redirected: it appears as choice within pre-structured environments, as optimization within given parameters. The subject becomes legible insofar as it is continuously translated into data, and governable insofar as it can be rendered comparable, measurable and adjustable.

The Subject as Data

A further line produces the subject as data itself. In this configuration, life is not primarily addressed as expression but as extractable material. Actions, preferences, and linguistic traces are transformed into features, categories, and probabilities. Subjectivity no longer precedes this process; it is retroactively assembled through classification. What one is becomes inseparable from what one can be made to count as.

The Invisible Worker

Another line concerns labor. Here, subjects appear as infrastructural operators of AI systems: annotators, moderators, raters, validators. Their work is essential yet structurally displaced from visibility. It appears only in functional form, as “human-in-the-loop,” as quality control, as correction, while the conditions of its production remain largely unacknowledged. Subjectivation takes the form of simultaneous centrality and erasure.

The Subject of Expertise

A further line produces subjects of expertise. Engineers, researchers, and ethicists are positioned as rational stewards of complex systems. Responsibility is localized at the level of technical decision-making, while broader political and economic structures recede into the background. In this way, agency is reorganized as competence, and critique is often translated into questions of design, optimization, or governance.

The Predictive Subject

Finally, a predictive line of subjectivation renders individuals as anticipatable entities. In domains such as policing, border regimes, or welfare systems, subjects appear as risks, scores, or probabilities. They are addressed not primarily in relation to what they do, but in relation to what they are expected to do. In this configuration, subjectivation operates in advance of action: it produces subjects through the pre-structuring of possible futures.

Alternative Practices: What the Dispositif Cannot Fully Capture

However, not all lines of subjectivation find equal conditions of existence within the dispositif of Artificial Intelligence. Alongside those described above that are actively produced and stabilized, there are others that remain structurally disfavored, forms of becoming-subject that do not easily enter regimes of datafication, optimization, or classification. These are not external to the field, but they appear as weak intensities within it, continually at risk of being neutralized or translated into more legible forms.

If lines of subjectivation traverse the dispositif in this way – if they produce us even as we reproduce them – then the question cannot be limited to which subjects are made possible, but must also address which remain difficult to sustain, and how this difference is lived. If they emerge in the interplay of practices, norms, and material arrangements, a further question arises: what other forms of becoming-subject might be opened through different practices? And which forms of self-relation do we, in turn, sustain or reinforce?

Alternative Practices: What the Dispositif Cannot Fully Capture<br />

Writing: From Struggle with Meaning to Selection Among Outputs

What is the difference between writing a text in the slow proximity of one’s own words – hesitating, revising, following a thought that resists formulation – and producing a text through a system that calculates probable continuations? What shifts in the relation to language, if expression no longer emerges from a struggle with meaning, but from selection among pre-structured possibilities? What kind of subject takes shape when writing becomes prompting, when articulation becomes navigation within a space of outputs already statistically composed?

What becomes of thought when it is no longer allowed to remain without immediate result? What changes if attention is not held in suspension – wandering, returning, lingering – but is continuously operationalized as input, as signal, as resource? What kind of self is formed when thinking is oriented towards an immediate answer, rather than toward the possibility of not yet knowing what it is that one thinks?

What happens to relation when conversation is displaced by mediation? When the effort to encounter another – through hesitation, misunderstanding, goodwill, care, kindness – is replaced by a system that purportedly anticipates, summarizes, or simulates response? What is lost when affect appears as something that can be retrieved on demand, rather than something that emerges in the unpredictability of presence?

What becomes perceptible when an artwork interrupts the smooth passage from image to category? When what is seen does not immediately resolve into recognition, but remains suspended – irreducible to function, resistant to immediate use? What kind of subject emerges in such a moment, in which perception is not yet captured by classification, and meaning does not stabilize into a single trajectory?

And what shifts when the figure of the “annotator” ceases to appear as function and becomes encounter? When the one who labels, filters, and corrects is no longer integrated as an invisible component of the system, but appears as a situated other, whose experience cannot be exhausted by the categories that depend on it and who makes a claim on us? What becomes unstable when this presence can no longer be fully translated into data, role, or task?

Points of Non-Coincidence: Where Other Trajectories Begin

These questions do not lead outside the dispositif. They do not restore an untouched subject prior to its formation. But they begin to indicate points at which its operations do not fully close, and thus sites at which what Waldenfels calls Antwortlichkeit becomes possible. For in each case, something remains that does not coincide with its capture: a hesitation in language, a surplus in perception, a resistance in relation, a remainder in the other that exceeds the roles through which they are made intelligible.

It is perhaps here – not beyond, but within these moments of non-coincidence – that other trajectories of subjectivation become thinkable. Not as stable alternatives, but as fragile deviations: ways of speaking, seeing, and relating that do not entirely align with the imperatives of calculation, prediction, and optimization, and that, precisely in this misalignment, keep the field from becoming fully closed and protect us from totalization.

Which Forms of Life Do We Sustain?

What these movements begin to make visible is a relation that resists simplification. We are not external to the dispositif we describe. We do not stand before it as sovereign subjects, capable of steering it from a position of independence. We are formed within it – through its lines of visibility, its regimes of enunciation, its processes of subjectivation. What we can see, what we can say, what we can become is never simply our own.

And yet, this does not exhaust the relation. For if we are shaped within these configurations, we are not only their effect. We participate in their continuation. We stabilize them through our practices, our repetitions, our forms of use. But precisely in this, a different possibility emerges: that what is reproduced can also be shifted. That even within the field that forms us, there are movements – hesitations, deviations, reconfigurations – through which other trajectories of subjectivation can be fostered.

Neither Determined Nor Free: A More Demanding Question

The question, then, is not whether we are determined or free. It is more demanding: which forms of life do we sustain through the ways we see, speak, and relate? Which subjects do we become when we align ourselves seamlessly with these systems – when we allow their operations to pass through us without resistance, when we accept their abstractions as sufficient descriptions of ourselves and others? And what becomes unavailable in this alignment: which forms of attention, of relation, of language, of responsibility begin to recede when they are no longer practiced?

Conversely, what might it mean to remain within these formations without fully coinciding with them? To inhabit their structures, but not to let them settle entirely into what we take ourselves to be? If there is no outside from which to act, then intervention must take place within the very relations that bind us – within the practices through which subjectivity is continuously produced and reproduced.

Toward the Distribution of Forces

It is here that another dimension comes into view. For the dispositif does not only organize what can be seen, said, and become; it also distributes forces. It channels, intensifies, and stabilizes them. It produces asymmetries, accumulations, and thresholds. To understand how these movements hold, how they persist, and how they might be altered, it becomes necessary to follow not only lines of visibility, enunciation, and subjectivation, but also the lines along which forces are arranged, transmitted, and transformed.

Meike Hinnenberg

Meike Hinnenberg

Learning & Development Architect

Meike Hinnenberg is a trainer and Learning and Development Architect at MDI Management Development GmbH and specializes in communication, conflict management, diversity & inclusion, and lateral leadership.

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AI-Empowered Leadership: 6 Guiding Principles

AI-Empowered Leadership: 6 Guiding Principles

by Gunther Fürstberger | Apr 14, 2026 | Leadership and AI, Leadership Tips, MDI Whitepaper | 0 comments

Download the full whitepaper here

AI-Empowered Leadership: 6 Guiding Principles

This blog post is a detailed summary of the whitepaper “Guiding Principles of AI-Empowered Leadership” by MDI’s CEO, Gunther Fürstberger. You can find the full whitepaper here!

Let’s be honest: most conversations about AI in leadership quickly turn into either breathless hype or vague unease. What’s actually missing is a clear, grounded perspective on what it means to lead well in an age where AI is becoming part of everyday work.

That’s exactly what MDI’s CEO Gunther Fürstberger tackles in his latest whitepaper. If you’re a leader trying to figure out how to work with AI in a way that’s confident, effective, and responsible — this one is worth your time. Here’s a detailed look at the six guiding principles!

 

Principle 1: Leadership responsibility stays with the individual

AI already outperforms us in many cognitive areas — processing speed, pattern recognition, and handling vast amounts of data. That gap will only grow. And yet: AI is a tool, not an actor. It can analyze, simulate, suggest, and optimize. But meaning, purpose, judgment, and accountability remain human tasks.

Leadership doesn’t mean being the strongest or most intelligent entity in the room. It means taking responsibility for impact, people, and consequences. That responsibility can’t be delegated — not to algorithms, not to AI systems. Leaders who understand AI as a superior but supportive tool keep their ability to shape the future. Those who treat it as a threat lose room to maneuver. Those who treat it as a tool gain sovereignty.

Principle 2: AI collaboration is a superpower

The decisive skill in the AI age isn’t knowledge about AI — it’s the ability to collaborate effectively with AI systems. Research confirms that AI-enabled collaboration can significantly increase productivity and efficiency. The German Economic Institute reports that employees using AI applications tend to achieve better performance results, particularly where expertise and experience are already present.

What AI does well today: automated data analysis, contextual summaries of large volumes of information, and structured scenario planning. These functions reduce cognitive overload and create space for strategic thinking.

Leaders are increasingly evaluated on their ability to integrate AI potential into organizational culture, build AI competence within their teams, and uphold ethical and long-term goals at the same time. AI collaboration is no longer a nice-to-have — it’s a central lever for productivity, innovation, and lasting leadership impact.

 

Principle 3: Performance grows through the development of humans and AI in interaction

Even in AI-augmented teams, the team remains fundamentally human. AI agents are powerful tools — capable of learning, sometimes acting autonomously. But they lack consciousness, moral judgment, and genuine interpersonal skills. They operate within the goals and frameworks that humans define.

Three dimensions are crucial for human development in the AI age:

Self-leadership: Working with AI requires the ability to reflect on your own thinking and decision-making processes. When do you trust the AI? Where do you push back? Critical thinking and ethical clarity matter more than pure knowledge accumulation. A classic self-management principle also becomes more important: proactivity. AI systems tempt us toward reactivity — staying grounded requires deliberate distance, breaks, and AI-free time.

Collaboration: The more AI takes over operational tasks, the more central genuine human competencies become: building relationships, resolving conflict, building trust, conveying meaning. In AI-augmented teams, transparency about who uses which systems — and how — is essential, as is a strong learning culture as the foundation.

Working with AI: Professional AI use requires new skills: precise goal definition, clear prompting, iterative improvement, and quality control. AI should neither be mystified nor blindly trusted. It’s a powerful tool that must be consciously managed and reviewed.

When people keep developing, consciously shape their collaboration, and systematically build and improve AI agents, a dynamic learning architecture emerges at its best. Performance then doesn’t grow linearly — it grows cumulatively.

Download the full whitepaper here
Performance grows through the development of humans and AI in interaction

Principle 4: The division of labor with AI is dynamic

What is clearly a human task today may be supported or taken over by AI tomorrow. That makes leadership in the AI age an ongoing exercise in role reflection.

The central guiding question: What can human leaders do better — and what can AI do better?

Today, a leader’s strengths lie primarily in building relationships, creating meaning, making sound judgments, and taking responsibility. AI is already highly capable at routines, pattern recognition, and scaling.

But the shift is already underway. In a year, AI systems will be even better at personalizing and playing through complex scenarios. In three years, many analysis and planning tasks will be largely AI-supported. In five years, a large part of operational control processes could be automated — while the human leader becomes more of an architect of meaning, culture, and frameworks of responsibility.

There’s also an identity question here: what do we as leaders want to keep for ourselves — and what do we consciously hand over to AI? What matters is not a one-time decision, but the continuous development of collaboration. Trying out new forms of cooperation regularly — ideally daily — builds a dynamic balance: AI as amplifier, not replacement.

Principle 5: Securing the future requires a determined and responsible AI transformation

Economic history shows that technological disruptions rarely proceed linearly — they are abrupt, radical, and frequently underestimated. Around 155 years ago, sailing ships dominated world trade with roughly 90% market share. Thirty years later, steamships controlled 80%. The decisive advantage didn’t go to those who owned the technology — it went to those who consistently built new business models on top of it.

Today, AI isn’t multiplying our physical strength — it’s multiplying our intelligence. AI agents are changing not just individual processes but entire value chains, decision-making logics, and competency profiles.

Future-proofing doesn’t begin in a strategy paper. It begins in the calendar. Two concrete levers:

Regularly questioning your own tasks: Which of your tasks can AI already take over today? Which in a year? In three to five years? Administrative routines, data analysis, first drafts, market comparisons — all of this can be automated. Consciously delegating these tasks to AI frees up time for what only humans can do.

Regularly switching to the best new platform or tool: Technological progress is exponential. What leads today may be mediocre tomorrow. Transformation also means questioning technological loyalties. Not convenience, but performance should be the deciding factor.

Principle 6: The well-being of people and nature is the overarching benchmark for AI development

AI is one of the most powerful technologies of our time. It has the potential to cause immense suffering — and equally great benefit. Rarely before has a technological development been so rapid, so global, and so profound in its impact on the economy, society, politics, and individual lives.

The overarching benchmark cannot be efficiency, profitability, or geopolitical dominance alone. It must be the well-being of humanity and nature.

The ambivalence is real: emotion recognition can support psychotherapy — and be used for surveillance in authoritarian contexts. Generative AI can democratize creativity — and produce disinformation at an unprecedented scale. AI in medicine supports early cancer detection — and raises new questions about data sovereignty and equitable access.

There’s also an ecological dimension that is often underestimated. Training large AI models consumes enormous amounts of energy and water. If AI further accelerates consumption and resource use, it exacerbates ecological crises. Conversely, it can be a crucial tool in the fight against climate change.

For organizations, this means: ethics must not be a fig leaf — it needs to be integrated into innovation processes. AI projects should be systematically assessed for their social and environmental impacts. And transparency toward customers and employees builds trust while reducing long-term reputational risks.

Progress is measured not only by speed or scale but also by its contribution to a successful life and a livable environment.

Mindset is what matters

If there’s one thing these six principles make clear, it’s this: the real divide won’t be between organizations that use AI and those that don’t. It will be between leaders who shape this shift with intention — and those who just go along for the ride.

So ask yourself: Which of these principles is already part of how you lead? And where is there still room to grow? The whitepaper goes deep on all six — and if any of this resonated, it’s well worth reading in full. 

Because ultimately: AI serves humanity — not the other way around.

    Download the full whitepaper here
    Gunther Fürstberger

    Gunther Fürstberger

    CEO | MDI Management Development International

    Gunther Fürstberger is a management trainer, author and CEO of Metaforum and MDI – a global consulting company providing solutions for leadership development. His main interest is to make the world a better place through excellent leadership. He has worked for clients including ABB, Abbvie, Boehringer Ingelheim, DHL, Hornbach, PWC and Swarovski. His core competence is leadership in digital transformation. He gained his own leadership experience as HR Manager of McDonald’s Central Europe/Central Asia.  At the age of 20 he already started working as a trainer.

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    What AI Shows You — and What It Doesn’t

    Meike’s Reflections on Artificial Intelligence

    Do you prefer to listen to this article? Click below to access our AI-generated audio version!

    What AI Shows You — and What It Doesn’t

    Meike’s Reflections on Artificial Intelligence

    This is the third part of MDI’s Leadership Architect Meike Hinnenberg’s reflection series. You can find parts I and II on our blog page! Stay tuned for more parts to come 🙂

    What Leaders See — and What Stays Hidden

    As lines of enunciation organize the field of sayability, lines of visibility organize the field of perception. They are conditions of seeing that circulate within a dispositif, determining what can appear as an object, what form something must assume to become perceptible, from which vantage point it is illuminated, and what must recede into shadow for this illumination to hold. A line of visibility is thus a historically specific regime of seeing: a distribution of light and darkness that brings certain realities into presence while casting others into the shadow on which this presence depends.

    Michel Foucault traced a transformation of regimes of seeing when he showed how sovereign power, once staged in the blinding spectacle of public punishment, gave way to disciplinary power embedded in architectures of continuous observation. What changed was not only the exercise of power, but the arrangement of the visible itself: spectacle yielded to surveillance, and visibility ceased to be an event and became an environment.

    When we turn to the dispositif of Artificial Intelligence, how is the terrain of perception arranged, and which lines of visibility organize this regime of seeing?

    How AI Presents Itself: Four Lines of Visibility

    Line 1: The Interface — Intelligence as Performance

    One line runs along the interface. Here, Artificial Intelligence appears as responsiveness without delay: dashboards refresh in real time, prompts yield fluent replies, and systems demonstrate competence in carefully staged demonstrations. Intelligence presents itself as performance – immediate, seamless, self-contained. What this line establishes is the perceptible surface of operation: output as event, response as evidence. The system comes into view precisely where it answers.

    Line 2: Abstraction — Structure Without Weight

    A second line follows the path of abstraction. Models are described by architectures, parameters, and accuracy scores; performance is reported numerically, and improvement is recorded as optimization. Intelligence becomes legible as a formal property, detached from situation and substrate. What comes into view is structure without weight, reasoning without environment, cognition without bodies.

    Line 3: Scale — Expansion Beyond Intervention

    A third line unfolds at the scale level. Artificial Intelligence appears as planetary infrastructure: billions of parameters, global deployment, continuous operation across time zones and continents. Its magnitude exceeds ordinary perception. Scale produces its own regime of visibility: what emerges is inevitability, momentum – expansion beyond intervention.

    Line 4: Neutrality — When Calculation Replaces Judgment

    A fourth line organizes neutrality. Artificial Intelligence appears as objective and data-driven. Its operations present themselves as technical processes rather than situated decisions. Judgment appears as calculation; outcomes appear as results rather than interventions. What appears is a world cleansed of politics, in which a large part of responsibility is shifted to the system, and context is leveled out. Neutrality here is not simply descriptive; it is productive, structuring perception so that harm, choice, and embedded values recede into shadow, while the surface of computation shines as transparent and self-evident.

    The Illusion of Autonomy — and What It Conceals

    The Illusion of Autonomy — and What It Conceals

    Together, these lines compose a regime of seeing in which Artificial Intelligence presents itself as autonomous, immaterial, and inevitable. What appears is intelligence without remainder. Yet regimes of visibility do not simply reveal; they arrange revelation. They produce perceptibility by structuring what cannot be seen at the same time.

    By citing Amazon’s crowd-working platform “Mechanical Turk” and recalling its historical namesake – the ostensibly chess-playing automaton constructed by Wolfgang von Kempelen in 1769 – Kate Crawford traces such a line of visibility and its fracture at once. The figure of the seemingly chess-playing automaton, dressed in Ottoman robes and seated before a wooden cabinet topped with a chessboard, appeared to deliberate and decide on its own. When its doors were opened, intricate gears and clockwork were revealed, offering the reassuring image of mechanical reason. Yet this visibility was carefully staged: concealed within the cabinet, a human operator followed the game in darkness, shifting position as panels were displayed to sustain the illusion. What appeared to be autonomous intelligence was, in fact, the surface effect of a hidden human presence.

    In recalling this machine, Crawford renders perceptible a continuity that the contemporary name Artificial Intelligence works to obscure: the appearance of autonomy sustained by distributed, hidden work. That Amazon names its global digital labor platform after this deceptive automaton – an illusion built not only on concealment but on the orientalist staging of a racialized figure – is at once cynical and involuntarily revealing. The name preserves, like a fossil in language, a longer history in which intelligence appears at the surface while the labor that sustains it is displaced elsewhere, often across colonial and postcolonial geographies, into bodies that remain structurally unrecognized.

    By shifting the vantage point, she intervenes in the regime of seeing itself. What appeared seamless reveals fracture lines; what appeared autonomous reveals dependence. The interface no longer appears as an origin but as a surface.

    Behind the Surface: Labor, Matter, and Geography

    Behind the abstraction of the model, material infrastructures come into view. Data centers operate at an industrial scale, consuming vast quantities of electricity and water to sustain continuous computation. Their servers depend on the conflict minerals tin, tantalum, tungsten, gold, and rare earth elements extracted from landscapes marked by toxic residues and ecological exhaustion. The expansion of machine learning contributes to growing streams of electronic waste, measured in millions of tons. What appears as immaterial intelligence is inseparable from extraction, depletion, and heat.

    Behind the neutrality of data, processes of selection and classification emerge. Machine learning systems depend on vast datasets assembled through human activity: images segmented, sentences evaluated, gestures annotated. Millions of crowd-workers across the world perform these tasks, often for minimal compensation, clicking through thousands of items in repetitive sequences that train systems to see. Content moderators encounter violence, pornography, and degradation so that others encounter sanitized outputs. Their perception becomes part of the system’s sensory apparatus, even as their presence disappears from its representation.

    Behind the scale of the system, a geography becomes perceptible: supply chains stretching across continents, data centers situated near sources of energy and water, labor distributed across time zones, extraction zones, and processing facilities linked in continuous operation. What appears as a unified technical object reveals itself as a convergence of environments, infrastructures, and bodies.

    Seeing Otherwise: From Output to System

    Artificial Intelligence does not simply appear differently once these conditions are seen. The regime of visibility itself is exposed as constructed. The lines that once produced the appearance of autonomy are revealed as arrangements that separate surface from substrate, output from labor, intelligence from matter.

    To follow these fracture lines is not merely to see more, but to see otherwise. Intelligence no longer appears as an isolated technical achievement, but as the visible surface of relations extending downward into the earth, outward across the planet, and inward into the perceptual and cognitive labor of others. What had appeared as a self-contained system becomes perceptible as a dispositif: an arrangement that produces both the object and the subjects who sustain it, while organizing the conditions under which this production can be seen or remain unseen.

     

    Meike Hinnenberg

    Meike Hinnenberg

    Senior Leadership Architect

    Meike Hinnenberg is a trainer and Senior Leadership Architect at MDI Management Development GmbH and specializes in communication, conflict management, diversity & inclusion, and lateral leadership.

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    Leading in the Age of AI: How AI Discourse Shapes Responsibility and Power

    Meike’s Reflections on Artificial Intelligence

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    Leading in the Age of AI: How AI Discourse Shapes Responsibility and Power

    Meike’s Reflections on Artificial Intelligence

    This is the second of seven parts of MDI’s leadership architect Meike Hinnenberg’s new blog reflection series on AI. You can find the first part here! Stay tuned for more 🙂

    Chapter II – Lines of Enunciation

    By distinguishing Artificial Intelligence as an industrial apparatus from machine learning as a set of practices, Crawford performs a gesture of ethical resistance. She interrupts the smooth circulation of the term, exposing Artificial Intelligence not as a settled object but as a line of enunciation – and in doing so opens a different path through the field.

    In Deleuze’s sense, lines of enunciation are neither utterances nor texts, neither speakers nor doctrines. They are conditions of sayability that circulate within a dispositif, delineating what can be named, thought, and acted upon.

    Most often, lines of enunciation remain invisible precisely because they work so well. They do not appear as commands, norms, or ideologies; they slip into language as description, into grammar as agency, into names that seem to pre-exist the things they gather. They do not ask to be believed: one does not need to agree with a line of enunciation to use it.

    How AI Discourse Shapes Reality and Responsibility

    These lines are not primarily repressive; they are productive. They bring objects into being (AI), generate problems (alignment, bias), propose solutions (ethical AI), and sketch futures (AI will transform everything). A critique that treats them merely as false representations, therefore, misses the point. Their force lies not (only) in what they conceal, but also in the realities they help bring into existence.

    Understanding this productivity – and, with it, understanding technology not simply as an instrument to be used wisely but as a mode of world-disclosure – is essential, especially with regard to the question of responsibility. We are not outside the dispositif. We are not independent of the social, technological, and linguistic structures through which the world becomes accessible to us. Our relation to ourselves and our access to reality are shaped within them.

    How AI Discourse Shapes Reality and Responsibility

    Response-ability

    What is therefore required is not the illusion of standing beyond these structures, but the effort to understand how the dispositif operates: what realities it brings into being, how we are positioned within it, and how we might relate to it, act within it, or even shift its lines. For now, being independent of these conditions does not mean we would not be responsible. Responsibility may instead take the form that Bernhard Waldenfels calls Antwortlichkeit (response-ability): a responsiveness to what addresses us before we fully understand it, a response that can never entirely catch up with what precedes it.

    Let us follow this path a little further to see how it shapes the field. If we turn, for example, to the website of the OECD, we read:

    AI holds the potential to address complex challenges from enhancing education and improving health care, to driving scientific innovation and climate action. However, AI systems also pose risks to privacy, safety, security, and human autonomy. Effective governance is essential to ensure AI development and deployment are safe, secure and trustworthy, with policies and regulation that foster innovation and competition.

    How Discourse Limits What Can Be Questioned

    The OECD text speaks in a language in which Artificial Intelligence already acts: it drives, addresses, and enhances. Politics enters only later, as a moderating hand. In this grammar, Artificial Intelligence appears as an agent capable of benefit or harm, yet never itself fundamentally in question. Within this frame, one may debate safety, trust, and regulation, but more structural questions about extraction, power concentration, or the desirability of AI as such struggle to surface as relevant statements. The force of such enunciation lies not in persuading belief, but in pre-structuring the field of speech itself.

    By distinguishing Artificial Intelligence as an industrial apparatus from machine learning as a set of practices, Crawford renders such a line of enunciation visible and thereby intervenes in the field of sayability. By questioning whether Artificial Intelligence is even artificial or intelligent, she shows that what appeared as an autonomous historical actor is in fact a constructed convergence: an industrial apparatus, a planetary infrastructure grounded in colonial continuities and distributed human labor.

    What material and historical infrastructures make AI possible?

    By shifting the question from “Is AI fair?” to “What material and historical infrastructures make AI possible?”, the unity of the term Artificial Intelligence fractures like the ice layer of a winter-frozen lake.

    And another layer of the acoustic landscape begins to surface: the breathing of ventilation shafts, the murmur of moving earth, the metallic heartbeat of drills, the slow chewing of stone by machines, the deep-throated hum of engines, the churning of propellers folding the sea behind them, the wind threading through stacked containers, a quiet choreography of clicks and pauses labeling one image after another, bodies trying to keep time with logistics, repetition measured in beeps, the percussion of parcels in transit – a subdued sonority of work that must remain unnoticed, a human rhythm beneath the supposedly smooth surface of automation.

    Meike Hinnenberg

    Meike Hinnenberg

    Learning & Development Architect

    Meike Hinnenberg is a trainer and Learning and Development Architect at MDI Management Development GmbH and specializes in communication, conflict management, diversity & inclusion, and lateral leadership.

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    AI-Empowered Leadership: 6 Guiding Principles

    by Gunther Fürstberger | 14. April 2026 | Leadership and AI, Leadership Tips, MDI Whitepaper | 0 Comments

    AI-Empowered Leadership: 6 Guiding Principles This blog post is a detailed summary of the whitepaper "Guiding Principles of AI-Empowered Leadership" by MDI's CEO, Gunther Fürstberger. You can find the full whitepaper here! Let's be honest: most conversations about AI...
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    What AI Shows You — and What It Doesn’t

    by Meike Hinnenberg | 1. April 2026 | Digital Transformation, Impuls series, Leadership and AI | 0 Comments

    What AI Shows You — and What It Doesn't Meike’s Reflections on Artificial Intelligence Do you prefer to listen to this article? Click below to access our AI-generated audio version! What AI Shows You — and What It Doesn't Meike’s Reflections on Artificial Intelligence...
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    Leading in the Age of AI: How AI Discourse Shapes Responsibility and Power

    by Meike Hinnenberg | 18. March 2026 | Impuls series, Leadership and AI, Leadership Tips | 0 Comments

    Leading in the Age of AI: How AI Discourse Shapes Responsibility and Power Meike’s Reflections on Artificial Intelligence Do you prefer to listen to this article? Click below to access our AI-generated audio version!Leading in the Age of AI: How AI Discourse Shapes...
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    AI Ethics for Leaders: Why Context and Critical Thinking Matter More Than Ever

    by Meike Hinnenberg | 11. March 2026 | Impuls series, Leadership and AI, Learning Transfer | 0 Comments

    AI Ethics for Leaders: Why Context and Critical Thinking Matter More Than Ever. Meike’s Reflections on Artificial Intelligence Do you prefer to listen to this article? Click below to access our AI-generated audio version!AI Ethics for Leaders: Why Context and Critical...
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    A Success Story – When AI Sharpens Human Judgement

    by Claude MacDonald, Rafael Ungvari | 6. March 2026 | Customer Story, Digital Transformation, Leadership and AI | 0 Comments

    A Success Story – When AI Sharpens Human Judgement Do you prefer to listen to this article? Click here to access our AI-generated audio version! When AI Amplifies Human Judgment: A Customer Success Story About This Project At MDI, we believe that great leadership and...
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    How Do You Lead People Who Don’t Think the Way You Do?

    by Zeca Ruiz | 4. February 2026 | Leadership Impact, Leadership Tips, Learning Transfer | 0 Comments

    How Do You Lead People Who Don't Think the Way You Do? Do you want to listen to this article? Click here to access our AI-generated audio version!   How do you lead people who do not think the way you do? Leadership is a challenge, especially when you are not...
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    How You Deal With Neurodiversity as a Leader

    by Iris Kandlbauer | 3. February 2026 | Leadership Impact, Leadership Tips, Short Knowledge Bits | 0 Comments

    How You Deal With Neurodiversity as a Leader You prefer listening to this article? You can find our AI-generated audio version below! How You Deal With Neurodiversity as a Leader What might be behind “strange” behavior in a team—and how leaders can deal with it...
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