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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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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.

  • LinkedIn

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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 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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