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The 4 Stages of Development: What Leaders Need to Know

The 4 Stages of Development: What Leaders Need to Know

by Florian Biedermann | Nov 5, 2026 | Leadership Impact, Leadership Tips | 0 comments

The 4 Stages of Development: What Leaders Need to Know

A blog post inspired by Ivan Petric’s essay “Model of Models: Three Models. One Process. (Model modela: Tri modela. Jedan proces.)“

Whether we are learning a new skill, stepping into a leadership role, or building a high-performing team, development rarely follows a straight line. Initial enthusiasm often gives way to frustration, uncertainty, and challenges before real progress becomes visible.

But what if these experiences are not setbacks, but natural stages of the same development process?

In this first part of our two-part blog series, we explore how three well-known development models reveal a shared pattern of growth, and what leaders, coaches, and trainers can learn from it. Drawing on insights from coach and NLP trainer Ivan Petric, we take a closer look at why frustration is often a necessary part of learning and how the right support can make all the difference.

Model of Models

What do a young boy from Makarska, Lionel Messi, and a first-time manager have in common? Yes, it’s the letter M – but actually we did not mean that. According to coach and NLP trainer Ivan Petric, they all go through the same journey. It just looks different each time.

In his essay “Model of Models“, Petric takes three frameworks that most of us in learning and development keep in separate drawers and puts them side by side. What he finds is useful for anyone who coaches, leads, trains, or raises people.

Three drawers, one pattern

For years Petric kept three models apart. One came from NLP training, one from leadership work, and one from team development. Each had its own literature, its own experts, and of course, its own PowerPoint slides.

  • The Four Stages of Competence describe how an individual learns a skill. We move from unconscious incompetence to conscious incompetence, then to conscious competence and finally to unconscious competence.
  • Situational Leadership describes how a leader should support someone who is developing in a specific task. The development levels run from D1, the enthusiastic beginner, through D2, the disillusioned learner, and D3, the capable but cautious contributor, to D4, the self-reliant achiever.
  • Tuckman’s model of team development describes how a group learns to work together: Forming, Storming, Norming, and Performing.

The authors, disciplines, and content are different. But when Petric stopped paying attention to the labels, the same structure showed through:

Competence

Situational Leadership

Team Development

Emotional dynamic

Unconscious Incompetence

D1

Forming

Excitement, anticipation, novelty

Conscious Incompetence

D2

Storming

Reality check, frustration, uncertainty

Conscious Competence

D3

Norming

Practice, growing skill and confidence

Unconscious Competence

D4

Performing

Stability, autonomy, ease

Petric finds the last column the most interesting, and so do I. It reflects what he actually observes when working with people. What changes is not only what a person can do. It is also how they experience themselves and the process they are in.

Strip away the labels and what is left? A new beginning. A collision with reality and its complexity. Gradual adjustment and the building of new skills. And finally, more stable, independent and effective performance. These are not three models that happen to match. They are three perspectives on the same developmental process, at three different levels: the individual, the individual within an organisation, and the group.

Three drawers, one pattern

Why this matters: knowledge becomes transferable

Petric credits NLP not so much as one more model but as a way of looking at things. Its central idea is modelling: when someone succeeds at something, you look past the content of their story and ask how the process behind the result is structured.

What happens first? What next? What changes when feedback comes in? And could the same structure appear somewhere else under a completely different name?

He puts it simply: a beginner sees a technique, a more experienced person starts to see a pattern, and once you understand the process, you are no longer limited to a single technique. Seeing the shared pattern does not mean ignoring the differences. But it does something valuable. You can see what you understand about leadership in a child learning something new. What you observe in a sports team, you can recognize in a project team.

This search for patterns across levels later became the foundation of what Petric calls Fractal Coaching: moving from the whole to the structure to the detail and back to the whole, while checking what repeats and at which level an intervention makes sense.

The critical phase is not the beginning

Back to Marko. He is twelve or thirteen, his room is covered in posters, his hair is getting longer, and he sees himself on stage playing riffs while the front row screams. The rest is technical detail. His parents, who still have a kimono in the wardrobe from the time he was going to be the next Bruce Lee, are more cautious. Eventually his father gives in and buys a guitar.

Then the real problem starts.

In the first lesson, the teacher’s fingers land exactly where they should. Marko’s fingers hit the wrong string, miss the fret, and produce sounds that have nothing to do with the records. By the time he has moved from one chord to the next, the song would already be over. On day three, the guitar is lying next to his bed. By day five, he is wondering whether drums might suit him better. The strings are too stiff anyway, and an electric guitar would surely be easier.

Marko analyses the problem in great detail. The only thing his analysis misses is that all of this is completely normal.

This is why Petric considers the second stage, conscious incompetence, D2 or Storming, perhaps the most decisive in the whole process. Not because it is the most important in theory, but because this is where we so often decide whether to continue or quit. In this phase, we do not only learn chords, a new business process, or a coaching technique. We also learn how we react when results do not come, when feedback is negative, and when our original self-image no longer holds.

Can I tell a mistake apart from personal failure? Can I try again? Can I ask for help without feeling incompetent?

The same thing happens at work. Someone starts a new job full of energy. A few months later, the job no longer looks like the interview: deadlines, difficult relationships, decisions without enough information, tasks that were not in the job description. If the manager only sees falling motivation, the conclusion is “wrong person“. If the employee only sees their own frustration, the conclusion is “wrong company“. Sometimes both are right. But sometimes they are looking at a perfectly normal developmental step and mistaking it for a final diagnosis.

guitar lesson

Constructive frustration versus overload

Marko’s father had two easy options. He could fix the problem right away: the guitar is not working, so buy drums, then a piano, then maybe a moped. Or he could go to the other extreme: “You wanted this, now deal with it.” Neither is smart development management.

Instead, he held to their agreement: stick with it until the end of the term, then decide. And the teacher did something important. He did not call Marko lazy or give him a lecture on discipline. He took two chords and slowed everything down until Marko managed, for the first time, to switch between them without chaos. Once it sounded clean. Once it didn’t. But Marko had heard that he could do it.

A person in the second phase rarely needs us to solve the whole problem. They need enough support to see that there is a way through, enough structure to take the next step, and enough responsibility to take that step themselves.

That is why Petric rejects the idea that a good teacher, parent, coach, or manager is someone who removes discomfort. Development is hardly possible without meeting the limits of what we can do. The real question is what kind of environment we build around that point. If a task is so hard there is no chance of success, frustration leads to giving up. If it is so easy there is no challenge, nothing new develops. In between lies the zone where we try, fail, correct, and see little progress.

Safety is not sterility. Safety means I can make mistakes without being exposed, receive feedback without being attacked, and try again.

We cannot skip developmental stages. But we can help people go through them better: with clearer expectations, better feedback, and fewer unnecessary mistakes, without the illusion that we can teleport someone from beginner to expert.

Development Is a Process, Not a Shortcut

Whether someone is learning to play the guitar, taking on a new leadership role, or finding their place in a team, the underlying lesson remains the same: growth takes time, practice, and the willingness to work through uncertainty.

For leaders, this means recognizing that frustration is not always a sign that something is going wrong. Sometimes, it is a sign that development is happening. The challenge is knowing when to provide guidance, when to create space for learning, and when to let people find their own way forward.

But what happens when we apply these development patterns beyond the individual?

In Part 2 of this blog series, we will continue exploring Ivan Petric’s insights and what they mean for leadership, coaching, and team development.

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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Beyond the Training Room – Turning Insights into Impact

Beyond the Training Room – Turning Insights into Impact

by Florian Biedermann | Oct 22, 2026 | Leadership Tips, Learning Transfer, Training Insights | 0 comments

Beyond the Training Room – Turning Insights into Impact

Traditionally, a training session ends with a recap and a round of feedback. In these face-to-face conversations, participants’ responses are often overwhelmingly positive. Usually, those who enjoyed the training are the most likely to speak up, while few participants feel comfortable telling the facilitator directly that they did not find it particularly valuable.

Depending on the questions asked, we hear that participants would recommend the training, that their expectations were met, and that the trainer was well prepared and explained the content clearly.

To capture these otherwise fleeting, subjective impressions, we at MDI have used our “1-Minute Feedback” for many years: a short questionnaire consisting of four questions, rated on a scale from 0 to 6, with 6 being the highest possible score. An open-text field also allows participants to share additional comments about the training.

Four questions – different perspectives

Our feedback questions explore different aspects of the learning experience:

  • Would I recommend the training or coaching?
  • Were my expectations met?
  • Was the trainer well prepared, and did they deliver the content effectively?
  • Am I confident that I can apply what I have learned?

A closer analysis of the responses reveals that, on average, the final statement — “I am confident that I can apply what I have learned” — consistently scores slightly lower than the others. In other words, confidence in applying the learning at work receives the lowest rating.

This does not make the positive feedback any less valuable. But it raises a crucial question: What is missing between “That was a good training session” and “I feel confident putting this into practice at work”?

Or, to use a sports analogy: Why are participants unable to reproduce their training performance when it matters — in the workplace?

The final question looks ahead. It shifts the focus from the training experience itself to what comes next: the next difficult feedback conversation, a conflict within the team, or a decision made under time pressure. In that sense, it is less an evaluation of the training and more an assessment of readiness for what follows.

One distinction is important: a lower score does not, in itself, demonstrate a lack of learning or a failure to transfer that learning into practice. It initially reflects participants’ subjective confidence in their ability to apply what they have learned. Whether they actually use it needs to be assessed separately.

Nevertheless, we should take this signal seriously. Believing we can successfully use a new skill is one relevant factor in transferring learning to the workplace.

feedback

What might explain the lower score?

A single number cannot tell us the reason. Instead, it should serve as a starting point for further questions.

“I understand it — but I haven’t practiced it enough yet.”

Understanding a conversation model, for example, is not the same as being able to use it in an emotionally charged situation.

Participants may not know how to apply it. What they may need is another opportunity to try it themselves, receive feedback, and try again. Realistic practice, a constructive approach to mistakes, and subsequent opportunities to apply the learning can all support transfer.

At MDI, we have developed a range of AI-powered role-play scenarios for precisely these situations. They allow participants to practice and refine what they have learned in realistic situations, both during the training and, importantly, afterwards. The AI provides feedback, with a choice of different feedback models.

“It works in training. But will it work in my situation?”

The example used in the training may have been clear to everyone. Yet participants’ own situations often turn out to be more complex than the practice scenario: an experienced team member responds dismissively, an unresolved conflict is affecting the team, or only ten minutes are available for an important conversation.

This raises the question of how closely the practice scenarios reflect participants’ actual challenges. It is therefore important not to oversimplify exercises, but to build them around realistic, specific situations from participants’ working lives.

“I don’t yet know whether my work environment will support it.”

Applying learning is not simply a matter of individual competence. It also requires opportunity and support. In most cases, workplace conditions differ considerably from those in a training session.

What happens when a leader wants to delegate differently after the training, but everyone around them continues to escalate every decision? Or when they want to provide regular feedback but find neither the time nor the support to do so?

Research on training transfer shows that support from both leaders and colleagues is relevant to the application of learning.

What we can change in practice

First, let us be clear: the goal is not to persuade participants to give higher ratings at the end of a training session. What matters is not the score, but the outcome.

More important than “What have I learned?” is “What can I apply from this training?” Our goal must therefore be to build a practical bridge between learning and everyday work.

1. Ask the crucial follow-up question

Alongside the rating, we could ask:

“What else do you need to feel confident applying what you have learned?”

Or, more specifically:

“In which situation do you already feel confident — and in which do you not?”

This helps us distinguish between a need for further practice, a clearer connection to real work, support in the workplace, or something else entirely.

2. Create practice loops rather than one-off exercises

One possible approach is to work through a real situation, receive targeted feedback, and then try the same situation again.

Rather than adding more content, we should provide more opportunities to build confidence through focused practice. These practice and feedback opportunities can be integrated into the training itself and into follow-up learning activities.

Here, too, support after the training — such as the AI-powered role-plays described earlier — is key to helping participants change or adapt their behavior.

3. Make the first application step specific and visible

“I will give more feedback” is an intention. For a transfer plan, I would make it more specific:

  • Who will I have the next conversation with?
  • When will it take place?
  • What specific behaviour do I want to try?
  • What might get in the way?
  • Who can give me feedback afterwards?

Action plans, peer exchange, and conversations with leaders are established approaches to supporting application after training.

Exercises and scenarios must reflect participants’ actual working situations as closely as possible, rather than relying on hypothetical cases disconnected from their experience.

transfer

4. Do not leave the transfer entirely to the participants

Even before the training, participants and their leaders could clarify:

What should be different afterwards? Where will there be a suitable opportunity to try something out? What support will be needed?

This makes application a shared responsibility rather than an additional expectation that participants are left to manage on their own.

Research highlights the importance of a supportive work environment and genuine opportunities to apply learning. It therefore makes sense to build post-training activities into the programme design from the outset. Follow-up peer-learning groups, transfer labs, coaching, and the involvement of participants’ leaders can all be valuable elements.

5. Ask different questions after the training

The 1-Minute Feedback provides an important snapshot. Six to twelve weeks after the training, it is useful to ask follow-up questions that reflect the time elapsed and explore how participants have applied their learning:

  • What specifically have you tried?
  • What worked, and what did not work yet?
  • What changes have you observed?
  • What supported or prevented application?

This complements the immediate assessment with experience from everyday work.

Transfer is not a single moment. Initial attempts, feedback, and further adjustments can develop into changed behavior over time. A second or third feedback cycle, including one around six months after the training, can help capture longer-term changes.

The lowest score can prompt the most valuable question

Naturally, every trainer, consultant, and project manager appreciates excellent training evaluations. At the same time, this kind of affirmation carries the risk of interpreting the training and its design as “close to perfection”.

That does not mean we should rush to question every training programme. Rather, we need to understand what support is still missing between learning and action. In my view, this is one of the central responsibilities of L&D: not only to create a compelling learning experience, but also to design for transfer.

A noticeably lower rating should therefore not be dismissed as an unwelcome judgement or a sign of a negative attitude. Instead, it can provide both an incentive and a useful indication of where the training or its transfer support could be improved.

Of course, some negative feedback may reflect a participant’s mood more than the quality of the training. The open-text comments, where provided, can often help us understand the context. If they explain the negative rating or highlight unmet expectations or missing content, they should also inform improvements to the training where appropriate.

What have you experienced when it comes to the gap between high training satisfaction and lower confidence in applying the learning — and what helps you close it?

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.

  • LinkedIn

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Are Leaders Asking the Wrong Questions About Employee Retention?

Are Leaders Asking the Wrong Questions About Employee Retention?

by Zeca Ruiz | Oct 15, 2026 | Leadership Tips, Short Knowledge Bits | 0 comments

Are Leaders Asking the Wrong Questions About Employee Retention?

I was recently invited to give a talk at an association of business owners in Brazil, which was organizing a symposium on a timely topic: workforce challenges in a fast-moving, digital world.

Right in the briefing, the organizer raised one of the first points: the business owners were complaining about two things at once. The people already inside their company weren’t satisfied, and the people outside didn’t want to come in.

I asked what they thought was behind it. The answer, which she relayed on behalf of the owners, came quickly and seemed ready-made: “nobody wants to commit to anything anymore.”

I have a rule of thumb: a quick, surface-level answer always hides a deeper cause. So I went looking. I didn’t find a single cause, and I suspect there isn’t one. I found a hypothesis that seemed more useful than the first: What if part of the problem is time?

Why Flexibility Matters to Employees

While researching, one case caught my attention: Dropbox, which allows employees to design their own workdays, with a few fixed windows for collaboration and the rest left open.

According to the company itself, nine out of ten employees identified this way of working, combining remote work and flexible hours, as one of the main reasons they stay. This comes from an internal survey at a tech company, so it doesn’t prove anything on its own. But it points in an interesting direction.

And you don’t have to look to Silicon Valley for a similar signal. Tens of millions of people in Europe now work through apps and digital platforms, and when asked why, the ability to choose their own working hours ranks among the top reasons.

For many, it’s also simply the job that was available, so it would be unfair to say they all chose it for the flexibility. But it does say something about what traditional employment may no longer be offering.

Flexible Working Hours

Can Flexible Working Hours Work Beyond the Office?

For anyone running a factory or a shop, the conversation is different. Not everyone can decide when a shift starts. But on the other hand, having control over your own time doesn’t have to mean complete freedom. Even at Dropbox, flexibility comes with fixed windows for working together.

Still, Dropbox is a company where people primarily work from laptops, while the business owners I was speaking to run factories and shops. So I looked for someone who had tested the same question in a setting closer to theirs, and I found a study conducted at the American retailer Gap.

Researchers ran a controlled experiment across 28 stores. In some of them, employees received their schedules two weeks in advance and were no longer required to be on call.

The results were striking: those stores increased sales by a median of 7%, while labor productivity rose by 5%. Employees also reported sleeping better, something you don’t usually find in a sales report.

Small Changes Can Make a Difference

I don’t have equivalent evidence for the industrial sector, and every operation has its own constraints, whether related to customers, shifts, or regulations. So I would avoid treating this as a universal recipe. What seems reasonable to me is testing the hypothesis before accepting the first explanation.

One possible first step is small and reversible: ask your team which scheduling rule has the greatest impact on their lives. Pick one, test a change for a few weeks, and decide in advance how you’ll measure whether it worked. You could look at absences, employee turnover, or lateness.

If it doesn’t work, you can go back to the previous arrangement. At least you’ve learned that this particular rule wasn’t the problem.

Are We Asking the Right Questions About Workforce Challenges?

Back to the briefing for that symposium.

The business owners weren’t wrong about the symptom: there are people who are unhappy inside their companies and people who are reluctant to join them.

Maybe they’re wrong about the cause. Or maybe they simply stopped at the first answer. Before repeating that nobody wants to commit to anything anymore, it’s worth asking:

When was the last time your team had a say in their own schedule?

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.

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10 Tips for Effective Communication With Dominant Partners

10 Tips for Effective Communication With Dominant Partners

by Dominik Etzl, Rafael Ungvari | Oct 4, 2026 | Best Practice, Leadership Tips, learning effectiveness | 0 comments

Homer Simpson has been delighting young and old for many years.

Short-tempered to the point of being slightly irascible, he speaks his mind directly. He’s constantly getting annoyed over trivial matters and doesn’t seem to place much value on the opinions and advice of others.

If you encounter someone like this in a professional setting, your enthusiasm is likely to be limited.

But we say: You can communicate effectively even with Homer Simpson. Why not give it a try right now with our tips?

The DiSG Communication Model

The DiSG communication model states that people communicate according to four different patterns—depending on their individual type: steady, initiative-driven, conscientious, or dominant.

Even though the person sitting across from you in any given conversation is always the biggest challenge—it’s the dominant conversation partners who tend to stick in our minds as particularly unpleasant. Why is that?

Dominant conversation partners

  • speak bluntly and want to get to the point quickly without beating around the bush—which is easily perceived as rude
  • are direct and very honest—which is sometimes perceived as being too honest!
  • They focus on solutions, not problems—and they usually don’t want to spend much time talking about the problems anyway

And yes, that tendency to get hot-headed… that comes up every now and then, too. 😉

Homer Simpson is the epitome of the dominant communication style.

But even with him, it’s possible to communicate effectively. So, over the next week, find your own personal Homer Simpson and try to have a productive conversation with him as an equal.

Or you could try out the conversation first in a safe environment. After all, knowing how to communicate with a dominant counterpart is one thing. Staying calm, clear, and confident in a real-life situation is often quite another.

This is exactly where AI role-plays can help you. In an AI-powered conversation simulation, you can realistically run through typical situations from everyday leadership scenarios and try out different reactions and phrasing. Your virtual counterpart reacts to how you lead the conversation, so that—just like in real life—the conversation can unfold differently depending on your behavior.

But first, here are our tips:

10 Tips for Effective Communication with Dominant Conversation Partners

DO’s

  1. Focus on results and solutions.
  2. Get to the point quickly and avoid long introductions and small talk.
  3. Show self-confidence.
  4. Be prepared for blunt and (brutally) honest statements.
  5. Don’t overreact.
  6. Show respect.
  7. Look the other person in the eye and speak in a firm, clear voice.

DON’Ts

8. Don’t stray from the topic!
9. Don’t challenge the other person—you’d lose!
10. Don’t make promises you can’t keep.

Now what? Practice the conversation with a dominant counterpart

These tips sound simple in theory—but how well do they work when the other person actually acts dominantly?

With our AI roleplay “Feedback with BID”, you can try out exactly this situation. You’ll take on the role of Michael, a team leader, and conduct a feedback conversation with Alex. Alex is very dominant and results-oriented and regularly takes over the conversation during team discussions.

Your task: Address his behavior clearly and confidently, highlight its impact, and try to agree together on a concrete change for future team discussions.

In doing so, you can apply some of the tips from this article right away: Do you get to the point quickly? Do you remain confident when Alex reacts dominantly? Do you communicate clearly and respectfully? And are you able to steer the conversation toward a concrete solution?

What Are the Benefits of an AI Role-Play?

The advantage: An AI role-play creates a safe space for experimentation. You can practice difficult conversation scenarios without putting a real employee discussion at risk. You can adjust phrasing that doesn’t work on the first try and try the situation again.

Especially when dealing with dominant conversation partners, this exercise can help you not only know what you want to say, but also practice how to react in that specific situation.

The role-play uses the BID model—Behavior, Impact, Development. You’ll practice addressing specific behaviors, highlighting their effects, and jointly agreeing on a desired course of action. This allows you to combine dealing with a dominant counterpart with a concrete feedback method.

Would you like to try out our AI Roleplay and immerse yourself in challenging simulations? Feel free to contact us on our website, and we’ll provide a customized solution for you and your company.

Dominik Etzl

Dominik Etzl

MDI Trainer

​Dominik Etzl is a Trainer at MDI Management Development GmbH. His focus is on topics of digital transformation, as well as OKR (Objectives & Key Results), agile leadership and lateral leadership. His goal is to support managers in leading a valuable contribution to their environment: on an individual, corporate and societal level.

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Rafael Ungvari

Rafael Ungvari

AI Product & Solution Lead

Rafael is AI Product & Solution Lead at MDI and is working to redefine leadership development through artificial intelligence. To implement this idea, he has worked with our team to establish the MDI AI Leadership Lab, which serves as a hub for experimenting with and applying AI solutions together with clients and trainers.

His work builds on his studies in business informatics at WU Vienna, where he combines business perspectives with technical expertise to develop practical and sustainable digital solutions.

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4 Heuristics to Stop Mistaking Symptoms for Cause in the AI Era

The AI era is intensifying an old problem: acting too fast on the wrong diagnosis. Every one of us has lived through, or heard about, some organizational mess born from exactly that. In every era, every organization has had someone fixing the wrong symptom in a hurry.

We’re neurologically wired with biases, and for plenty of reasons we’re not immune to bad conclusions: herd behavior, confirmation bias, sunk cost, and more. That’s just how we’re built. Maturity in leadership means recognizing your own blind spots and questioning them before you trip over them.

What AI changes is the cycle, taking the risk to another level: an answer generated in seconds, written with a fluency that sounds more confident than any uncertain human opinion, and that tends to validate more than it challenges. Pressure used to come from outside, from deadlines and demands. Now it’s also built into the very tool that’s supposed to help you decide faster.

To deal with that dilemma, what we need is a filter before we act, not more data.

That doesn’t make AI the enemy of good diagnosis.

It just makes AI one more place a ready-made answer can come from, right alongside the rushed colleague and your own bias. The heuristics below work on any of those sources: ask a colleague, ask yourself, or ask AI. The filter that catches the fast, wrong answer is the same one either way.

That’s what a heuristic is: a practical decision rule. Not a complete theory, not something that claims to explain everything. It’s a tested shortcut that helps you ask the right question before acting, so you avoid the most common mistakes.

I built up a set of these over years of reading about management and organizational behavior. I tested them against real decisions and kept only the ones that actually solved something. Four survived that I use constantly, especially now, with the decision cycle so much shorter because of AI.

1. Flip the fundamental attribution error

There’s a well-documented human tendency to blame someone’s failures or off behavior on their personal traits rather than on the context they’re operating in. That’s the fundamental attribution error. The heuristic is simple: if you’re going to be wrong, be wrong on the side of context, not the individual.

In the AI era, this shows up every time a new tool’s rollout fails, and the quickest explanation is “the team is resisting change.” Before accepting that, it’s worth asking whether the context was actually built to support the change:

Was there real time to learn it, was the surrounding process adjusted, does the person have the authority to apply what they learned?

When to use it: any time a problem’s explanation comes as a label on a person, “the team doesn’t care,” “so-and-so is resistant,” “this generation has no commitment.”

What changes in practice: instead of trying to fix the person, you fix the process around them. A process problem, once corrected, tends to stay fixed. A misdiagnosed “people problem” always comes back.

2. Don’t fall for the “now you know, and knowing is half the battle” fallacy

Identifying the behavior that needs to change isn’t enough. Chris Argyris described the gap between the theory we claim to follow (espoused theory) and the theory that actually drives our actions (theory-in-use). Knowledge alone rarely changes the second one. The environment around it has to change too, and that takes time, experimentation, feedback, and adjustment.

That’s why AI training, prompt engineering workshops, “AI literacy” sessions so often change nothing day to day. The person learned. The context around them stayed the same.

When to use it: any time the answer to a behavior problem is “let’s train everyone”

What changes in practice: before you approve the training, you ask what’s going to change in the actual work environment to sustain the new behavior once the course ends. If the answer is “nothing,” the training alone won’t work, no matter how good it is.

Don't fall for the "now you know, and knowing is half the battle" fallacy

3. Tend the relational field so the organizational field can function, and vice versa

Every organization runs two fields at once: the organizational field, where tasks, processes, and structure live, and the relational field, where trust, connection, and human needs live. Neglect one and you put strain on the other. Without a solid relational base, the organizational field can’t hold up.

AI is speeding up the organizational field faster than the relational field can keep pace. Processes change faster than trust can rebuild itself, and the result is people hitting their numbers while edging closer to burning out.

When to use it: when output is climbing, but the team’s mood is tenser, quieter, or more defensive than it was a few months back.

What changes in practice: that’s usually the first signal, showing up before it ever hits a performance number. Investing in trust now is what keeps the output from dropping later.

4. Put every “best practice” on trial

Popular isn’t the same as effective. Every management practice making the rounds has some context where it makes sense and solves more than it creates. Outside that specific context, it can do damage nobody traces back to the right cause, because the effect only shows up months or years after it was adopted. Sometimes the most powerful move isn’t adopting a different practice. It’s simply stopping the one you’re doing.

“Adopt AI” has become a mandate copied from one company to the next, with no one stopping to ask whether it solves an actual problem in their own operation, or whether it’s there just because everyone else is doing it.

When to use it: before copying any practice from another company, including adopting AI.

What changes in practice: you ask what specific problem of yours this practice actually solves. If the answer is generic, “everyone’s doing it” or “we’ll fall behind otherwise,” nobody has checked whether it fits your context. Checking upfront costs a lot less time than undoing the wrong call later.

None of these four heuristics require budget, new tooling, or anyone’s approval above you. They require one question before your next important decision: am I looking at the cause, or just the easiest explanation someone handed me?

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.

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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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    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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    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.

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