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Human First: Leadership in the Age of AI

Human First: Leadership in the Age of AI

by Johanna Glechner | Sep 14, 2026 | Digital Transformation, Leadership and AI, Leadership in the digital transformation | 0 comments

Human First: Leadership in the Age of AI

My professional journey has always taken place at the intersection of two worlds that, for a long time, were treated as separate: technology and leadership.

With a background in business informatics, I started my career in organizational development, analyzing and improving business processes and translating them into opportunities for automation. It quickly became clear to me that while technology and processes are fascinating, the decisive factor in any transformation is people.

This realization eventually led me into HR and, from there, into leadership development. My technological background never disappeared. Whenever HR software, automation, or digital solutions came into play, these topics almost automatically landed on my desk. Artificial intelligence was therefore a logical next step.

Since then, one central question has guided my work: How can we use AI to make leadership not only more efficient, but more effective?

Human First. AI-enabled.

When I talk about AI and leadership, one principle is at the heart of everything I do: Human first. AI-enabled.

I am not particularly convinced by either extreme in the current debate around artificial intelligence. We should neither romanticize AI nor assume that technology will replace people across the board anytime soon. What I do believe is that people who are willing and able to work competently with AI will have a significant advantage over those who choose to ignore it.

AI makes knowledge available faster. It simplifies analysis and automates operational tasks. And precisely because of this, distinctly human capabilities such as judgment, responsibility, empathy, trust, and genuine connection become even more valuable.

The more powerful our technology becomes, the more important our ability to lead as humans becomes.

This is also why leadership development needs to evolve alongside AI. The question is no longer simply which AI tools organizations should introduce. The more important question is: What capabilities will leaders need in an AI-shaped world of work?

The World Economic Forum’s Future of Jobs Report 2025 shows that leadership and social influence have seen the strongest increase in importance, rising by 22 percentage points. This is happening at exactly the moment when machines are taking over more tasks than ever before.

Bringing AI Into Leadership Development

Today, my work focuses on developing and integrating AI solutions for leadership development.

This ranges from AI Roleplays and AI Learning Coaches to learning formats that help leaders understand how to meaningfully integrate AI into their everyday leadership practice. My role combines several perspectives. I work as a consultant on strategic concepts, as a trainer facilitating learning, and hands-on with concrete AI applications.

That hands-on aspect is particularly important to me. I do not want to simply talk about AI. I want to experiment with it, develop solutions myself, and understand what works and what does not. When I speak about AI, I want to understand how it works, where its limitations are, and under which conditions it can actually create value.

This combination of technological understanding, leadership expertise, and practical implementation is at the core of my work.

Human First. AI-enabled.

AI Roleplays: A Safe Space to Practice Real Leadership Conversations

One way AI can support leadership development is through AI Roleplays.

At its core, an AI Roleplay is a digital training partner for real-life conversations.

Imagine a leader preparing for a difficult feedback conversation. In an AI Roleplay, the AI can take on the role of an employee while the leader conducts the conversation. The leader can ask questions, respond to resistance, and try different communication strategies.

The key advantage is the safe training environment. Leaders can practice situations repeatedly, test different approaches, and learn from their experience without the consequences of getting it wrong in a real conversation.

This becomes particularly effective when the scenario and language are adapted to the organization’s specific context. Participants should feel: “Yes, this is exactly how a conversation like this could happen in our company.” Afterwards, the AI can provide both qualitative and quantitative feedback, creating another opportunity for reflection and learning.

From AI Tool to Integrated Learning Solution

When we develop customized AI Roleplays with clients, we start with a structured kick-off.

Together, we define the use case, target group, organizational context, and desired learning objectives. From there, we build an initial prototype and move into testing as quickly as possible.

Through iterative feedback loops, we work with the client on the scenario, role logic, AI responses, and language. Again, authenticity is essential. The experience needs to reflect situations participants genuinely encounter in their working environment. After quality assurance, the solution can be rolled out, often in combination with live leadership training.

This is how an AI tool becomes a scalable and integrated leadership learning solution.

AI Integration Should Start With the Problem, Not the Tool

For me, integrating AI into existing leadership programs never starts with the technology itself. It starts with the problem we want to solve.

Depending on the learning need, AI Roleplays can support the transfer of learning into everyday leadership situations. AI Learning Coaches can accompany participants throughout a learning journey, while Knowledge Bots can make training knowledge accessible when people actually need it.

At the same time, leaders need to build AI literacy. In our Leading with AI formats, for example, we work with tools and frameworks such as the AI Change Cycle, AI Transformation Maps, and an AI Use Case Canvas. The goal is not for leaders to know as many AI tools as possible. It is to strengthen their ability to make decisions and take action in a working world increasingly shaped by artificial intelligence.

AI Transformation Is Also Change Management

Many organizations are still struggling to use AI productively in their everyday work. The biggest challenge is rarely technology alone. Poor data quality, unclear processes, unrealistic expectations, and a lack of acceptance can all prevent organizations from turning AI investments into real value.

AI is neither magic nor simply an IT project. It changes the way people work, the roles they take on, and how decisions are made. AI transformation is therefore also change management. People need the right capabilities, concrete experience with AI, and above all the opportunity to try it themselves.

In my experience, attitudes towards AI often begin to shift precisely when an abstract topic becomes a concrete personal experience. Reading about AI is one thing. Actually using it to solve a problem, prepare for a conversation, or improve a workflow is something entirely different.

Leadership plays a central role here. Leaders need to model how AI can be used while simultaneously ensuring that it is applied responsibly.

From AI Tool to Integrated Learning Solution

Why HR and L&D Need to Help Shape AI Transformation

My advice to HR and L&D professionals is simple: Stay curious and don’t wait for someone else to define the transformation for you. I have worked in and around HR for approximately 15 years, and curiosity has always been one of the most valuable capabilities for me.

HR and L&D have a key role to play in AI transformation. Even the best technological strategy will achieve little if employees do not understand it or are unable to apply it. That is why HR should not enter the conversation only when a new AI tool is ready to be rolled out.

HR and L&D should help shape the question much earlier: How do we want to work, lead, and learn in the future? My approach is very pragmatic: experiment with new things, build capabilities, start small – but start.

The Future of AI-Enabled Leadership Development

In two or three years, I believe we may talk much less about “AI-enabled leadership development” because AI will simply have become a natural part of leadership. We will work in much more agentic ways, and leaders will not only lead people but also orchestrate AI agents.

This raises an important new question: How should we distribute tasks and responsibilities between humans and technology? At the same time, leadership development will move closer to the actual flow of work. Before a difficult conversation, a leader might reflect on the situation with an AI Coach or practice it immediately beforehand in an AI Roleplay. Learning will become more accessible exactly when it is needed.

As AI becomes a more integral part of leadership, AI literacy and responsible data use will become increasingly important. A reliable regulatory framework will be essential. And one of the central leadership responsibilities of the future will be to consciously decide what we delegate to technology and what should remain deeply human.

Because ultimately, the future of leadership is not about choosing between humans and AI. It is about understanding how technology can enhance what we do while preserving the human capabilities that leadership depends on most.

Johanna Glechner

Johanna Glechner

Artificial Intelligence Solutions

Johanna Glechner combines a background in business informatics with around 15 years of experience in HR, organisational development and leadership development. At MDI Management Development International, she focuses on developing and integrating AI solutions for leadership development – from AI roleplays and learning coaches to AI-enabled leadership journeys. Her approach: Human first. AI enabled.

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Zeca Ruiz on Cross-Generational Leadership and Navigating Change

by Jana Wölfl | 13. May 2026 | Leadership Impact, MDI Spotlight Series, Training Insights | 0 Comments

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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

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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

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Generation Z isn’t the Problem but Our System is

by Zeca Ruiz | 3. December 2025 | Impuls series, International leadership development, Leadership in the digital transformation | 0 Comments

Generation Z Isn’t the Problem, but Our System is. Read this article on crossgenerational management by Zeca Ruiz to find out more!

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The Lasting Impact of Leadership Horizon – Meike’s Perspectives

by Meike Hinnenberg | 2. July 2025 | Leadership Impact, MDI Inside, Short Knowledge Bits | 0 Comments

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Success Through Change: How to Stay Oriented During Transitions

by Anita Berger | 14. April 2025 | Impuls series, Leadership Impact, Leadership Tips | 0 Comments

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4 Tips on How to Shape Change Processes as a Leader

by Anita Berger | 3. April 2025 | Impuls series, Leadership Impact, Leadership Tips | 0 Comments

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Successful Team Building for Boehringer Ingelheim RCV

by Anita Berger | 29. March 2024 | Customer Story, International leadership development, MDI Inside | 0 Comments

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Organize Your Team in a Hybrid Workplace

by Peter Grabuschnig | 14. March 2024 | Impuls series, International leadership development, Leadership in the digital transformation | 0 Comments

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Exploring the Influence of AI on Leadership Roles – an experiment by a CEO

by Gunther Fürstberger | 14. November 2023 | International leadership development, Leadership and AI, Leadership in the digital transformation | 0 Comments

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AI Transformation Is Leadership Work

AI Transformation Is Leadership Work

by Gunther Fürstberger | Aug 28, 2026 | Leadership and AI, Leadership Tips, MDI Whitepaper | 0 comments

AI Transformation Is Leadership Work

The four-step loop that turns individual experiments into lasting change

Many companies are already using AI—and still not transforming. Tools, pilot projects, and guidelines only add up to transformation once they deliberately reshape value creation, processes, and collaboration. MDI is developing an AI transformation approach that helps leaders navigate the waves of AI disruption deliberately and well. We welcome critique and suggestions for improvement: 

AI Transformation Is Leadership Work<br />

1. Start with the Why — Direction before Action

The starting point isn’t a tooling question, it’s a strategic decision. A SWOT analysis and a solid business case clarify what AI means for the development of the business unit: where do productivity, quality, or growth levers emerge—and where does disruption loom? From this a vision emerges that considers not just efficiency but also the impact on customer value, roles, competencies, and collaboration.

The target circle translates this future vision into a first, manageable transformation cycle: from vision to concrete goals to KPIs—such as AI literacy, number of productive use cases, productivity gains, or revenue from AI-enabled innovations. Winning management buy-in requires a clear persuasion structure: strategic pressure to act, expected benefit, required investment, key risks, and a concrete decision. That’s how enthusiasm becomes commitment.

MDI PERSPECTIVE · AI & LEADERSHIP

AI Transformation<br />

2. Plan and Prioritize — Focused Use-Case Collection

The AI transformation navigator makes visible which areas and capabilities of the company need to move first. What matters here is looking across departmental boundaries: many of the relevant opportunities lie in end-to-end digital pathways—for example, from customer inquiry through offer and delivery to service—rather than in isolated point solutions.

Picture3

Use cases are systematically identified and prioritized in an evaluation matrix based on benefit and feasibility. Quick wins generate momentum and credibility; bigger levers are prepared as experiments. At the same time, the business unit needs AI talents as multipliers, a viable technology environment, and separate backlogs for use cases and features.

The IAO model sharpens prioritized use cases with three questions:  

3 Fragen

3. Implement and Experiment — Start Small, Learn Fast

A radical question opens up new thinking: what would the process look like if AI took it over completely? Then comes the pragmatic decision: what can AI already responsibly take on today—and where do human judgment, relationships, and accountability remain indispensable, at least for now?

Teams develop minimum viable solutions in short increments. Roles and decision rights are clarified, stakeholder commitment is secured, and risks—from data protection and quality to dependencies—are weighed. FOBO, the Fear of Becoming Obsolete, is countered through transparent communication, participation, and skill-building.

4 Evaluate and Adapt — Turning Results into a Learning Spiral S

At the end of each cycle, what counts isn’t the number of activities but the impact. Reviews compare results against KPIs and the business case: what gets scaled, changed, or ended? Retrospectives generate insights on collaboration, process, and technology. Vision, priorities, backlogs, and technology decisions are adjusted—the next loop starts at a higher level of learning.

The personal leadership rule: anyone who truly wants to achieve AI transformation has to live it. Leaders should devote around 20% of their working time to AI learning: testing applications, critically reviewing results, sharing experiences, and visibly trying out new ways of working.

The real competency is the ability to learn.

The loop connects strategy, portfolio work, execution, and organizational learning into one governable system. This is how AI transformation becomes a permanent design task—decisive, responsible, and measurable.

    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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    The Lasting Impact of Leadership Horizon – Meike’s Perspectives

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    What If AI Doesn’t Replace Us — But Drowns Us Out?

    What If AI Doesn’t Replace Us — But Drowns Us Out?

    by Martin Maglia | Jul 24, 2026 | Digital Transformation, Leadership and AI, Short Knowledge Bits | 0 comments

    What If AI Doesn’t Replace Us — But Drowns Us Out?

    Over the weekend, I watched a thought-provoking video from Rick Beato. (You can find the link at the end of this article.)

    For those who don’t know him: Rick Beato is a musician, producer, educator, guitarist, and one of the most respected music commentators on YouTube. With more than 5 million subscribers, he has spent decades analyzing what makes music meaningful and why some songs last for generations.

    As a hobby guitarist and someone who works professionally in leadership development, I expected another discussion about AI replacing human creativity. Instead, I heard something much more interesting.

    Rick speaks from experience.

    When he started his career, recording music was expensive. Studio time, professional equipment, record labels, producers, distributors, and radio stations acted as gatekeepers. Only a relatively small number of artists had access to a global audience.

    Then, technology changed everything.

    Digital recording dramatically lowered production costs. Streaming platforms made global distribution available to virtually everyone. Today, millions of songs are uploaded every year at almost no cost.

    The result? Music became more accessible than ever before. But something else happened as well.

    The challenge shifted from creating music to being discovered.

    For many artists, the biggest problem is no longer production. It is visibility.

    Rick’s argument is not that AI will eliminate musicians. His concern is that AI may create an overwhelming flood of content. Music platforms could become saturated with perfectly acceptable songs generated at almost zero cost. The challenge will no longer be creating content. The challenge will be finding a signal in the noise.

    That got me thinking about our own profession.

    That got me thinking about our own profession.

    In Learning & Development, leadership training, consulting, coaching, and sales enablement, AI is rapidly lowering the cost of producing content.

    Courses.

    Presentations.

    Articles.

    Videos.

    Assessments.

    Simulations.

    Soon, everyone will be able to generate them. The real differentiator may no longer be content creation. It may become:

    • Original thinking
    • Human judgment
    • Real-world experience
    • Trust
    • Authenticity
    • The ability to help people make sense of complexity

    In other words:

    The future value of humans may not be in producing more information. It may be in helping others decide what is worth paying attention to. Perhaps AI won’t create a world with too little knowledge. Perhaps it will create a world with far too much. And in such a world, the most valuable people will not be the best creators. They will be the best curators.

    What do you think?

    Will AI primarily replace expertise – or will it make trusted human expertise more valuable than ever?

    What I particularly like about Rick Beato’s perspective is that it moves the conversation away from the usual “AI versus humans” narrative. Instead, it raises a more strategic question: What happens when abundance becomes the problem? In leadership, sales, and learning, that may be the question worth discussing over the next decade.

    Martin Maglia

    Martin Maglia

    Leadership Trainer and MDI Partner

    Martin Maglia is an MDI Partner, leadership trainer, and executive coach with more than 25 years of international experience. Having worked with over 23,000 participants across 50 countries, he specializes in leadership development, personal effectiveness, and team performance. Martin combines extensive business experience, academic expertise, and a passion for helping individuals and teams unlock their full potential and achieve meaningful results.

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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 | Jun 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

    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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