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From Individual Growth to Leadership and Team Development

From Individual Growth to Leadership and Team Development

by Florian Biedermann | Nov 12, 2026 | Impuls series, Leadership and AI, Learning Transfer | 0 comments

From Individual Growth to Leadership and Team Development

In the first part of our blog series, inspired by coach and NLP trainer Ivan Petric’s essay Model of Models, we explored how three well-known development models reveal a shared pattern of growth. From initial enthusiasm to frustration and, eventually, competence, we discovered why challenges aren’t necessarily obstacles to development, but often an essential part of it.

But development doesn’t happen in isolation. People grow within teams, organizations, and systems that influence how they learn, collaborate, and perform. And sometimes, even well-intentioned support can stand in the way of real progress.

Let’s explore why effective leadership isn’t just about solving today’s problems, but about developing people who can solve tomorrow’s challenges independently.

When the whole system develops

Development doesn’t happen inside one person. When a child starts school, it is officially the child who goes to school. In reality, the whole family has to reorganize getting up earlier, homework, who drops off and who picks up. After a while, routines settle, and life feels normal again. Until something changes: another child, a new job, a move.

This adds something important to the four-stage story. The fourth stage is not a lifelong graduation. It means that a person or system works well under certain conditions. Change enough of those conditions, and you can be back at the start very quickly. A leader who ran a top team for ten years can take over a different organization and find that old methods no longer work. The skill has not disappeared. The context has changed.

This is where Messi comes in. You can have eleven excellent players and still not have a good team. The team photo at the start of the season looks great. Everyone smiles. Forming is a very photogenic phase. Storming is less so. Then it becomes clear who actually plays, who sits on the bench, whose idea wins, and who takes the penalty. Sometimes, the arrival of a better player weakens a team for a while, not because the player is a problem, but because the whole system has to adjust.

Looking only at individuals, we ask who is better or more motivated. Looking at the team, we ask different questions: How are roles distributed? How does information flow? What happens after a mistake? Where is trust? Petric describes teams where almost nobody was a “problem case,” and there were still problems. Marketing blamed sales, sales blamed production, production blamed management. Each person’s story made sense. The problem lived between them.

So instead of only asking “Who is doing it wrong?”, he asks: “Which part of this relationship structure produces this behavior?” Maybe an employee does not need more training, because the issue is unclear roles. Maybe a team does not need team building, because the issue is a reward system that pits departments against each other.

And one more point: nobody stands outside the process. A father learns to be a father. A leader learns to lead. A new leader can be D1 in their own role while leading someone who is D3 in theirs.

football

The parent and the leader in one person

Ask leaders and HR people about the new generation of employees, and you hear similar things: “They ask why.” “If they don’t like it, they leave.” “They want feedback but don’t accept authority the way we did.” Petric suggests a better question than “What is wrong with this generation?” In what system did these people grow up, and what did they learn from it?

Each generation of parents tends to fix what it missed. Those who grew up with little worked so their children would have more. Those whose parents had no time tried to be more present. This is understandable. But as Peter Senge writes in The Fifth Discipline: “Today’s problems come from yesterday’s solutions.”

If I remove every obstacle for my child, I may also remove the chance to learn how to overcome obstacles.

Petric tells the familiar story of a man who watched a butterfly struggling to leave its cocoon. He took scissors and widened the opening. The wings never unfolded, and the butterfly never flew.

Then comes the closed loop. At home, parents tell their children: “Don’t let them take advantage of you.” “If they don’t value you, find another job.” “Don’t stay silent for twenty years as I did.” The next morning, the same parent drives to work and becomes a leader. Across the desk sits someone else’s child, who heard almost the same thing. That evening the parent-leader comes home and says: “I just don’t understand this new generation.”

Middle managers are squeezed between targets from above and new expectations from below. “It has to be done” used to end a conversation. Today it often starts one. A leader may experience this as a loss of authority. Or perhaps their own leadership repertoire has simply entered a new developmental phase. The model applies to the D2 leader as much as to the D2 employee.

Good leaders, like Marko’s teacher, do not remove the demands. They make them meaningful and help the person grow through them. Formal authority can produce obedience, but not necessarily competence, responsibility, or independence. If I want someone to reach D4, I cannot keep treating them like someone who only follows instructions.

Short-term efficiency and long-term development are not always the same thing.

“Just give me a summary”

Today it’s not only parents and leaders who can solve problems for us. AI can write a text, build a presentation, summarize a book, or write code in seconds. Petric hears one sentence again and again in training: “Just give me a summary.”

He is not against summaries. He uses them himself. When you already know a field, a summary saves time. The problem starts when a summary stops serving understanding and replaces the process of understanding.

If an app plays Marko a perfect guitar piece, we have music. If his goal is music, problem solved. If his goal is to become a guitarist, the app solved a different problem. The result is there, but the skill may be missing. The same applies to a junior employee who receives a brilliant AI-generated analysis but does not yet have the experience to judge what is good, what is wrong, and what to do when the situation no longer fits the pattern.

The interesting question is not “Can AI do this?” Very often it can. The question is: “What should a person be able to do after the AI has done it?”

What we actually do when we help someone develop

Two people can have the same problem on the surface and need completely different help. One needs knowledge, another practice. One needs support to get through uncertainty; another needs someone to stop rescuing them. A third may already have the skill but cannot access it in the current context.

Petric gives an example from coaching. A client says, “I have no patience.” Yet, at work, he can calmly listen to an angry customer for half an hour. At home, his partner says two sentences the wrong way, and after thirty seconds he stops listening. The ability clearly exists. The problem is transferring it to another context. That calls for a different intervention than building a skill from scratch. If Marko has never held a guitar, telling him to feel like Eric Clapton will not help. His fingers still have to learn. But if he plays well at home and freezes on stage, we are no longer working on chords.

Potential is free. Development is not.

The four-stage model then becomes a practical guide. Beginners usually need clarity and structure. Frustrated learners need clear feedback, support, and enough small wins to keep going. As competence grows, too much control gets in the way. And treating a truly competent person as if they need permission for every step is one of the worst leadership styles there is.

The problem is not any single style. It is using the same style regardless of where the person stands. The same is true for coaching: asking “What do you think you should do?” helps an experienced person reflect, but with a beginner it can be an elegant way of withholding information they really need.

Petric points to Croatian coach Ante Kostelić, who trained his children Janica and Ivica under far from ideal conditions. Years earlier, as a handball coach, he said: “My dream was to build, through training, a team that can play without me.” It is hard to find a better measure of development: Have I made people more dependent on me, or more independent?

What we actually do when we help someone develop

The questions that remain

Petric reduces the practical side of his “model of models” to a few questions that should always run in the background:

  • Where does this person stand in relation to this specific task?
  • What can they already do, and what not yet?
  • Is the issue a missing skill or a lack of access to an existing resource?
  • How much structure, support, challenge, and autonomy do they need right now?
  • And most importantly: what will they be able to do on their own after this intervention that they could not do before?

That last question protects coaches, trainers, leaders, and parents from a tempting mistake: the need to prove their own competence. A coach who solves the client’s problem may look like a genius. But in development, the result is not only what we solved today. It is also what we will not need to solve for that person tomorrow.

Not every moment is a development moment. If a plane has to land, you do not hand it to a trainee pilot for the sake of their growth. But if we always take the fastest route to today’s result, we may one day find we have built a system in which nobody has developed the ability to deliver tomorrow’s.

A model is a map

Petric is careful not to claim that the three models are “really the same”. Tuckman does not talk about skill development, and Situational Leadership is not a parenting manual. On one level, they differ. On another level, they describe the same structure.

A good model works like a map. It helps you see something in reality you would otherwise miss. But if you fall in love with the map more than the territory, you start pushing people into four neat boxes. Nobody is obliged to develop according to our slides. Teams get stuck, go backwards, speed up. And sometimes putting the guitar down after two months really is the best decision.

A model does not tell the truth about a person. It is a way to ask a better question about them.

Sometimes the model even tells us not to intervene. Once we have a tool, everything looks like a job for it. Coaches want to coach, leaders want to lead, parents want to help. Sometimes the best intervention is to let the person try again, let the team solve a conflict it can solve, or not answer a question the person can answer themselves.

Development needs support, but it also needs space. Marko did not need a guitar that played itself. He needed a teacher who made the task just manageable enough to progress, and just hard enough to grow.

The Real Goal of Development: Building Independence

Across both parts of this series, one central idea stands out: Development is not about making challenges disappear. It is about helping people become capable of overcoming them.

Whether we are teaching a child, coaching an employee, leading a team, or introducing AI into the workplace, the question remains the same: Are we creating opportunities for people to learn, or are we simply making things easier for them?

Perhaps the most important takeaway is this: The success of leadership and coaching should not only be measured by the problems we solve, but by the capabilities we help others develop. Because ultimately, great leadership is not about becoming indispensable. It is about empowering others to succeed without us.

Based on “Model modela: Tri modela. Jedan proces.“ by Ivan Petric (September 2026). Models referenced: the Four Stages of Competence, Situational Leadership (Hersey and Blanchard), Tuckman’s stages of group development, and Peter Senge’s “The Fifth Discipline”.

 

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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What AI Shows You — and What It Doesn’t

What AI Shows You — and What It Doesn’t

by Meike Hinnenberg | Apr 1, 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

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.

  • LinkedIn

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

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

by Meike Hinnenberg | Mar 18, 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 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.

  • LinkedIn

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AI Ethics for Leaders: Why Context and Critical Thinking Matter More Than Ever

AI Ethics for Leaders: Why Context and Critical Thinking Matter More Than Ever

by Meike Hinnenberg | Mar 11, 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 Thinking Matter More Than Ever

Meike’s Reflections on Artificial Intelligence

This is the first part of a new AI blog series by our Senior Learning Architect, Meike Hinnenberg. Read her thoughts below and stay tuned for more parts to come!

The history of mining, like the devastation it leaves in its wake, is commonly overlooked in the strategic amnesia that accompanies stories of technological progress. […] As San Francisco drew enormous wealth from the mines, it was easy for the populace to forget where it all came from […] Just like the mines that served San Francisco in the nineteenth century, extraction for the technology sector is done by keeping real costs out of sight.

(Kate Crawford: Atlas of AI)

[…] – that is, as I said, the use of active forgetfulness, a porter at the door, so to speak, a custodian of psychic order, quiet, etiquette. From that we can see at once how, if forgetfulness were not present, there could be no happiness, no cheerfulness, no hoping, no pride, no present.

(Friedrich Nietzsche: The Genealogy of Morals)

It is immediately by deviating from equilibrium of animals, from tranquility – a departure engendered by the fault of Epimetheus – that mortals occur. Before the deviation, there is nothing. Then the accidental event happens, the fault of Epimetheus: to have forgotten humans. Humans are the forgotten ones. Humans only occur through their being forgotten; they only appear in disappearing.

(Bernard Stiegler: Technics and Time, 1)

Introduction

It is the beginning of February 2026 in Berlin; shortly after the big blackout in southern Berlin; shortly after several deaths and devastating losses in southern Europe in the wake of Storm Harry; shortly after a judge of the International Criminal Court was sanctioned by the United States; shortly after a cold wave driven by a destabilized jet stream claimed lives in the U.S.; shortly after deadly storms and floods across South and Southeast Asia left thousands displaced, hundreds dead and entire regions submerged; shortly after Russian strikes left thousands of households in Kyiv without heating while, almost in the same breath, the Ukrainian government entrusted the development of the namesake lithium deposit in the Kirovohrad region to TechMet – an Irish company partly backed by the U.S. government’s Development Finance Corporation and the U.S. financial firm Rock Holdings – drawing the country into the competitive circuitry of the global battery economy; shortly after …

It is the beginning of February 2026 in Berlin, and the air outside is sharp with cold. Inside my aging apartment, warmth gathers despite the leaky windows. The radiators whisper with gas that has traveled from Norway, the Netherlands, Belgium, the United States, or Azerbaijan, holding back the easterly winds for now. I sit in my kitchen. Milk warms slowly on the stove; coffee – probably carried from Africa, Asia, or South America – rises in the espresso pot and fills the room with its wonderful, familiar scent.

I open my computer, assembled through supply chains that fade into opacity, dependent on minerals, infrastructures, and forms of labor that rarely enter the frame. I open it to read the news and to begin research for a series on AI I am planning to write – aware, somewhere in the background, that even a question posed to a machine draws on energies and resources far beyond the gesture of typing it.

From this kitchen table, my orientation turns toward two recent points of departure: Kate Crawford’s Atlas of AI and her video essay  Mapping Empires.

Meike’s Reflections on Artificial Intelligence

Chapter I – The Dispositif of Artificial Intelligence

In Atlas of AI, Kate Crawford peels back the layers – conceptual and material alike – that have accrued around what we have come to call Artificial Intelligence, revealing a construct whose hidden assumptions we inhabit daily, largely unaware of the consequences they set in motion.

Her analysis unfolds as a kind of cartography, recalling Gilles Deleuze’s notion of the dispositif: a field traced by lines of visibility and invisibility, by grand narratives and what they render mute, by modes of subjectivation, fractures, continuities, and crossings – lines that intersect and mutate without ever solidifying into a universal structure.

Technologies of Domination

To draw nearer to this dispositif, to traverse its terrain, Crawford adopts the figure of the atlas. The atlas makes palpable the material and spatial dimensions condensed under the name Artificial Intelligence, even as these very conditions – its infrastructures, labors, and extractive foundations – are frequently displaced from view. 

At the same time, it affirms the situatedness of all knowledge: each map offers only a partial orientation, shaped by choices of scale, emphasis, and omission. In this convergence of aesthetic visual ordering and epistemic claim, Crawford shows that mapping is not a neutral description but a creative and political act.

Even as Crawford insists on the partiality of her own account – presenting her work as an invitation to follow emerging paths, to linger in zones of disparity, and to witness how particular perspectives come into being – she remains attentive to the darker history of the atlas itself. For atlases have never been innocent instruments of orientation alone; they have also served as technologies of domination.

The God’s Eye View

It is precisely this ambivalence that grounds her central hypothesis: that under the name Artificial Intelligence, such cartographic power is once again being mobilized. Along familiar routes of colonial exploitation and driven by an ambition no longer to draw an atlas of the world but to stand in for it, this impulse recentralizes power within the field of AI, advancing claims of universality and totality that rest on extractive regimes.

In doing so, it seeks to translate movement, communication, and labor into data, rendering the world legible from what Crawford describes as a supposedly objective, centralized “God’s eye view”.

Deterritorialization and Reterritorialization

The atlas itself operates through a double movement of what Deleuze would call deterritorialization and reterritorialization. While its cartographic abstractions unsettle fixed spatial relations and open new pathways of thought, they also carry a long-standing capacity for capture and domination.

Crawford’s intervention can be read as an attempt to amplify the deterritorializing potential of the atlas, even as she meticulously traces the reterritorializing operations to which colonialism and contemporary AIrepeatedly return.

The Ethical Task

Dispositifs, as singular and historically situated configurations, neither begin from nor arrive at the universal. And yet, in their operation, they repeatedly give rise to gestures of universalization and ambitions of totality, effects that emerge from within rather than from any transcendent ground. It is for this reason that such claims need to be traced genealogically, followed back along the paths through which they come to assert themselves.

Against this horizon, the ethical task is not to oppose totalization with a quasi-moralistic counter-universal, but to remain attentive to these movements as they unfold: to introduce shifts, frictions, and alternative pathways that keep the field open.

Ethical resistance, if it is to remain worthy of the name, must itself resist the temptation of ideology, universalization, and closure. For wherever resistance hardens into morally indignant certainty or ideological form, it risks reproducing – under the guise of critique – the very logics of universalization and reterritorialization it set out to unsettle.

Meike Hinnenberg

Meike Hinnenberg

Learning & Development Consultant

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

  • LinkedIn

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Generation Z isn’t the Problem but Our System is

Generation Z isn’t the Problem but Our System is

by Zeca Ruiz | Dec 3, 2025 | Impuls series, International leadership development, Leadership in the digital transformation | 0 comments

Generation Z isn’t the Problem, but Our System is

Do you want to listen to this article? Click here to access our AI generated audio version!

 

Generation Z isn’t the Problem, but Our System is

The discomfort we feel in relation to them reveals that something in us needs to change, because when an entire generation steps away from the same path, perhaps it is not the generation that is lost, but the path itself that is poorly designed.

Zeca is our MDI Trainer based in Latin America and an expert on generational cooperation. This blog article is the first part of a series – read below to find out what he thinks about the youngest generation in the workforce, Generation Z, and let’s start a discussion on LinkedIn!

Today, in leadership conversations, it has almost become a mantra to say that “young people from Generation Z are lost and we cannot expect much from them.”

As a corporate trainer I repeatedly hear complaints or questions that express how difficult it has been for us to relate to this generation.

GenZ – The Useless Generation?

Many leaders describe this generation as immature, fragile, hard to manage, not very committed, rebellious or even disrespectful. And this narrative has become so common that it starts to sound like an absolute truth.

We have reached a point where some companies have simply stopped hiring apprentices from this generation. They have turned their attention to professionals over fifty who, even at the end of their careers, still fit the current ways of working.

As the good contrarian I like to be, I want to bring up a point that only a minority takes the time to investigate.

Reflect, don’t judge!

Reflect, don’t judge!

When we evaluate a behavior only through the lens of the discomfort it causes us, we completely lose the ability to understand its function and existential role.

In the paradigm of complexity, we understand that subjectivity is an inseparable part of the system, including the observer, and that every behavior carries an internal logic that only reveals itself when we examine the context that produces it, not just the effect it generates in us.

It is like judging a book only by its cover. What we feel when we look at the cover, without knowing the real content, says much more about us than about the book.

In the same way, when we look at Gen Z, we do not see only who they are and what they do, we also see the contrast between the Generation Z way of existing and the way we were formed, our beliefs, our world models and the scale we learned to use to measure behavior, ours and others.

And if both the environment has shaped Gen Z and has also shaped the way we judge them, then we need to change the lens of this observation and step away from unilateral judgment.

We need to start asking ourselves, why are they like this, what are these behaviors responding to, which structures taught this generation to act the way it does and what role these responses are playing in today’s society.

Putting Gen Z Into Context

If we look at this Gen Z phenomenon from a systemic perspective, and not a reactive one, it becomes clear that no generation collectively “wakes up” more fragile, more rebellious or more difficult simply by choice.

Every behavior is always a reflection of the environment, always a condition inherent to the context, an adaptive response to specific conditions that are present.

And for me, based on the topics I explore and study, the behavioral expression of Generation Z is not pointing to their weakness, as we like to assume. It is pointing to the deep obsolescence of the system around them.

Every generation is an adaptive response to the environment it inherits from the previous one and Gen Z is no exception. They are a response to a world that has changed faster than organizations have been capable of following.

And let me be clear about something.

I am not here to idealize Gen Z or place them on any pedestal, I also see they carry traits that challenge them deeply, regardless of the system they inhabit.

They can be anxious, impatient, prematurely exhausted, emotionally overwhelmed, and often unprepared to sustain prolonged discomfort. These are real characteristics that demand development, maturity and guidance.

But acknowledging their difficulties does not contradict what I am saying, it actually reinforces it. Because the turning point comes when we stop looking only at what they lack and start recognizing how much of our own worldview, our expectations and our outdated structures shape the very behaviors we criticize.

The moment we take responsibility for the lens through which we see them, we finally create the conditions for growth on both sides.

Putting Gen Z Into Context

Why is Gen Z like this? (before labeling, we must contextualize and understand)

Before making any judgment, it is worth looking at this generation with analytical sobriety and recognizing the quality of their most striking traits, such as their authenticity, their search for meaning, their intolerance for incoherence, their rejection of rigid hierarchies, their heightened emotional sensitivity, their digital fluency and their constant questioning.

Born into the digital age, members of Generation Z arrive in the workplace with very clear expectations for agility, transparency and innovation.

And because they often bring an entrepreneurial and autonomy driven mindset, they tend to challenge traditional paradigms and seek more horizontal structures, with authentic and coherent leadership.

Seeing the Bigger Picture

These elements are not isolated characteristics, they are expressions of a way of existing that was shaped by an environment radically different from the one that structured the ways of the previous generations.

And although these traits are sometimes interpreted as opposition to earlier generations, or as the result of having had too many comforts which would have weakened their capacity for effort and discipline, they are actually a very interesting starting point for deeper, contextual investigation.

After all, as always happens in the transition between generations, these ways of behaving are adaptive responses to conditions that simply did not exist before.

So the invitation here is to broaden our perspective and look beyond isolated behavior. It is to observe the scenario that shaped this generation, the environment and the conditions that gave rise to each trait and to each way that Gen Z responds to the world.

Conclusion

Gen Z isn’t a sign of decline—they’re a sign that our systems haven’t kept pace. Their behavior points to the gaps in how we lead, organize and define work. When we stop labeling and start listening, we see that their traits aren’t flaws but responses to a world that changed faster than our structures did.

The real question isn’t what’s wrong with Gen Z, but what their reactions reveal about the environment we built. Once we shift the lens, it becomes clear: Generation Z isn’t the problem. They’re the diagnosis. The work ahead lies with us.

Zeca Ruiz

Zeca Ruiz

Leadership Trainer and Consultant

Zeca Ruiz is a Leadership Trainer, Facilitator and Consultant in Human and Organizational Development. He works in leadership development across Latin America and Europe, with experience in cultural transformation processes, team dynamics and the integration of systemic methodologies into corporate practice. He is a specialist in complex thinking, a generative coach and an integrative therapist, working at the intersection between human behavior, learning and the evolution of systems. He leads trainings, talks and development programs that combine depth, clarity and practical application to prepare people and organizations for high complexity environments.

  • LinkedIn

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How to Stay Connected: Leadership in Challenging Times

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How to Stay Connected: Leadership in Challenging Times

This blog is an excerpt from our new podcast! You can find the entire podcast episode here.

Staying connected: Leadership in challenging times

In our new video podcast format, “Voices of Leadership – an MDI Spotlight Series,” we regularly bring leadership experts in front of the camera to talk to them about their passions in leadership development.

Our first guest was Peter Grabuschnig – we spoke with him about the challenges in the current work climate, received his tips for new leaders, and discussed a topic close to his heart. Read this blog post to learn more about our first podcast episode!

What really matters to Peter

Right at the beginning, Peter introduces us to the topic close to his heart – connectedness in challenging times.

“I’ve noticed that we are more connected than ever before, but people feel little real connection.”

He explains that, especially with the changes brought about by the COVID-19 pandemic and new home office regulations, it has become more difficult to connect with one another, both among employees and between leaders and employees. At the same time, we are increasingly losing touch with ourselves and our values.

Peter emphasizes that it is an important challenge to restore this sense and be passionate about something again. He also says that he likes to use personality models in his training sessions. These can help participants to meet each other halfway and create a pleasant working environment despite personality differences.

Peter’s best feedback

When it comes to training, we ask Peter what feedback he remembers most. He tells us about participants who were skeptical at first but ended up giving him a high five with total enthusiasm.

He also says that participants often tell him that they’re using the tools they learned and that it’s working.

“I don’t think there’s anything better than that.”

What really matters to Peter

What should leaders change?

Remove judgment and listen consciously. Peter asserts that (negative) judgment, especially in conversations with employees, only causes lasting harm and demotivates employees.

At the same time, he says it is essential for leaders to listen consciously to their employees and thereby build a genuine relationship. The main thing here is to give the other person your full attention and show genuine interest.

“Genuine connection doesn’t take long. A few short sentences are enough.”

He also emphasizes that different people in a team are important assets and that sufficient communication is the most important tool here.

What will challenge us in the coming years?

For Peter, one thing is clear: we need human contact to actively combat increasing loneliness.

“Humans are social beings. We are community creatures; we need other people. Oxytocin is released when we hug someone, not when we have a nice MS Teams meeting with someone.”

Working from home means that many interactions with colleagues are lost. We are often more productive at home, but at what cost? Peter sees this as the biggest challenge – how can we balance productivity and connectedness in a hybrid, rapidly changing world?

Peter as a time traveler

Finally, we asked Peter what advice he would give his younger self if he could travel back in time. His answer: He doesn’t have to know everything. As a young trainer, he often felt that he had to be able to do everything and design the perfect training program. Now he knows:

“Being present is much more important than perfection.”

Authenticity also plays a major role: “If I am authentic, then hopefully my participants can be too!”

Conclusion

Our conversation with Peter showed us how important genuine connection is in today’s work environment. Leadership does not mean always knowing everything, but rather being present, listening, and consciously shaping relationships. Especially in times of remote work and hybrid structures, creating closeness and trust remains a central task.

Peter reminds us that leadership does not primarily work through processes or tools, but through people. Authenticity and genuine interest are often the key to strengthening teams in the long term.

Want to listen to the whole podcast? Here you can access the YouTube video! Follow us to make sure you don’t miss any more conversations with leadership experts.

Jana Wölfl

Jana Wölfl

Marketing Assistant

Jana Wölfl is a marketing assistant at MDI and works on our blog. She has already been responsible for several areas of marketing, such as designing our new website and administering our personalist.at portal.

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Success Through Change: How to Stay Oriented During Transitions

Do you prefer to listen to this article?

Click below to access our AI-generated audio version:

Success Through Change: How to Stay Oriented During Transitions

Change processes often involve challenges. They not only require structural adjustments but also an emotional examination of the new. For leaders, in particular, this means accompanying employees through the change, constructively absorbing resistance, and creating new perspectives. In this blog post, you will learn how to successfully implement changes and support your team in reorienting themselves.

This blog post is part of a change management series. Click here to go to the first part.

5. Letting Go and Finding New Perspectives

Psychological ownership – a concept developed by Jon L. Pierce, Tatiana Kostova, and Kurt T. Dirks – describes the feeling of personal responsibility and identification with a task or way of working, regardless of actual ownership. Employees invest time, energy, and commitment in their work, which creates a strong bond.

However, in change processes, this identification can lead to resistance, as it means letting go of what is familiar. In this case, it is the leader’s job to consciously guide this process while also providing orientation.

Tips:

  • Acknowledge the past: Give credit for your team’s past successes and show appreciation for their commitment.
  • Clarify the change: Be open about the need for change and emphasize the benefits for the team and the organization.
  • Offer new ways of identification: Encourage employees to actively participate in shaping change by giving them responsibility and the opportunity to help develop new structures.

6. Take Responsibility and Use Resources Wisely

Successful change requires not only strategic decisions but also a conscious use of resources. As a leader, you are challenged to strengthen your resilience while effectively leading your team through the change.

Tips:

  • Reflect on your role: regularly question your position in the change process and get support through coaching or peer feedback.
  • Self-care plan: consciously pay attention to your resources to maintain energy and clarity.
  • Use technological tools: rely on digital support, for example, through AI tools such as ChatGPT or knowledge databases, to gain new perspectives and find more efficient solutions.

Take responsibility and use resources wisely

7. Communication as the Key to Success

Transparent and continuous communication is essential to build trust and reduce uncertainty. Change processes are more likely to succeed if goals are repeatedly explained, expectations are formulated, and questions are actively encouraged.

Tips:

  • Provide regular updates: Continuously communicate the progress and background of the change.
  • Facilitate open dialogue: Create spaces for questions, feedback, and constructive exchange.
  • Consider individual needs: Tailor your communication to different types of employees to achieve the highest possible level of acceptance and participation.

8. A Structured Approach With the ADKAR-Model

The ADKAR model provides a clear structure for guiding change processes and taking employees with you step by step:

  1. Awareness: Create an understanding of the need for change.
  2. Desire: Create motivation by highlighting the benefits for individuals and the team.
  3. Knowledge: Ensure that all employees receive the necessary information and training.
  4. Ability: Support implementation with targeted coaching and constructive feedback.
  5. Reinforcement: Promote sustainable change through continuous support, further development, and transparent performance measurement.

Conclusion: Actively Shaping Change

Change processes are challenging but manageable with the right strategy. Through appreciative communication, clear structures, and conscious use of resources, you can safely lead your team through the change. Encourage a growth-oriented mindset, enable co-creation, and pay attention to your stability – because only those who are stable themselves can accompany others through challenging times.

Anita Berger

Anita Berger

MDI partner and trainer

Anita Berger is an executive coach, consultant, and trainer with a strong focus on leadership development in the VUCA/BANI world, design and facilitation of transformation processes and organizational culture development, as well as international human resources management. She is a co-owner and partner of MDI, Management Development International. With over 25 years of experience in management and leadership positions (including in the management of Coca-Cola Hellenic Austria & Slovenia & Konica Minolta Business Solutions) in various industries and company sizes, from medium-sized businesses to international corporations. Numerous contributions focus on leading virtual and hybrid teams, agile change management, organizational and leadership culture, as well as strategic talent management.
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