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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.

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

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

by Zeca Ruiz | Sep 24, 2026 | Digital Transformation, Leadership and AI, Leadership Tips | 0 comments

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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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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How You Deal With Neurodiversity as a Leader

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

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

Success Through Change: How to Stay Oriented During Transitions Do you prefer to listen to this article? Click below to access our AI-generated audio version:Success Through Change: How to Stay Oriented During Transitions Change processes often involve challenges....
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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

4 Tips on How to Shape Change Processes as a Leader Do you prefer to listen to this article? Click below to access our AI-generated audio version:4 Tips on How to Shape Change Processes as a Leader Imagine a dynamic network of paths that is constantly changing. While...
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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

Organize Your Team in a Hybrid Workplace Prefer to listen to the article? Click below to access our AI speech-generated audio. However, if you want to read it as usual, keep scrolling.Organize Your Team in a Hybrid Workplace In his new Rise Course, our MDI trainer and...
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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

Exploring the Influence of AI on Leadership Roles: Learn more about the ongoing ChatGPT experiment by MDI CEO Gunther Fürstberger Do you prefer to listen to this article? Click below to access our AI-generated version. Exploring the Influence of AI on Leadership...
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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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    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

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

    The Lasting Impact of Leadership Horizon - Meike's Perspectives Do you prefer to listen to this article? Click below to access our AI-generated audio version!The Lasting Impact of Leadership Horizon - Meike's Perspectives MDI’s Meike Hinnenberg shares her reflections...
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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

    Success Through Change: How to Stay Oriented During Transitions Do you prefer to listen to this article? Click below to access our AI-generated audio version:Success Through Change: How to Stay Oriented During Transitions Change processes often involve challenges....
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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

    4 Tips on How to Shape Change Processes as a Leader Do you prefer to listen to this article? Click below to access our AI-generated audio version:4 Tips on How to Shape Change Processes as a Leader Imagine a dynamic network of paths that is constantly changing. While...
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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

    Successful Team Building for Boehringer Ingelheim RCV Do you prefer to listen to this article? Click below to access our AI-generated audio version:Successful Team Building for Boehringer Ingelheim RCV In the interview with our MDI trainer and partner, Anita Berger On...
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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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    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.

    • LinkedIn

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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?

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    How You Deal With Neurodiversity as a Leader

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    Generation Z isn’t the Problem but Our System is

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    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

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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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