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Generation Z isn’t the Problem but Our System is

Generation Z isn’t the Problem but Our System is

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

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

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

 

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

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

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

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

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

GenZ – The Useless Generation?

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

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

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

Reflect, don’t judge!

Reflect, don’t judge!

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

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

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

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

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

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

Putting Gen Z Into Context

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

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

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

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

And let me be clear about something.

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

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

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

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

Putting Gen Z Into Context

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

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

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

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

Seeing the Bigger Picture

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

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

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

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

Conclusion

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

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

Zeca Ruiz

Zeca Ruiz

Leadership Trainer and Consultant

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

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by Rafael Ungvari | Sep 3, 2025 | Digital Transformation, Leadership and AI, Short Knowledge Bits | 0 comments

From Lab to Practice: What We Learned With AI

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

From Lab to Practice: What We Learned With AI

AI in organizations isn’t a one-size-fits-all solution.

And in people development, this becomes even more obvious: AI only creates value when it’s tailored to how people actually learn and practice skills.

At MDI, we’ve been working on this question for more than 1.5 years. What started as an internal experiment with ChatGPT quickly grew into our AI Leadership Lab – a sandbox where we could prototype, test, and refine how AI could support leadership development.

Our Journey With AI

Along the way, we moved from simple chatbots to immersive roleplays with voice and avatars. We discovered that immersion is not an add-on, but the goal. Our first demos now feel almost nostalgic – clicking a button, waiting for a response – compared to today’s fluid dialogues with emotional, human-like voices.

We also learned that systemic design matters more than model hype. GPT-3.5 to 4 was a leap, but not a breakthrough. The real difference came from how we designed scenarios: choosing the right challenge, calibrating resistance, and iterating with our trainers until the practice felt authentic.

And finally, we realized that feedback cannot be generic. AI’s true learning value comes when feedback is contextual, practical, and directly connected to the learner’s performance. That’s why we co-created feedback models with our trainers, based on real workshop experience.

Those internal learnings became the foundation of our Lab. But what happens when you take this approach outside – into client organizations?

From Internal Lab to Client Projects

In our first client projects implementing the AI Leadership Lab, one thing became crystal clear:

Success doesn’t depend on AI itself – it depends on how well the application is tailored to the organization.

Here’s what we learned in practice:

Our Journey With AI

1. Industry- & Company-Specific Adaptation

Generic simulations don’t work. For AI learning to have impact, scenarios must reflect the company’s reality:

  • the industry’s challenges,
  • the roles participants actually face,
  • and the objectives that matter most.

That’s why we don’t deliver “out of the box” roleplays. We co-develop scenarios with clients, allowing participants to rehearse the exact conversations and situations they encounter in their day-to-day work. AI enables the scaling of this realism across multiple contexts.

2. Co-Creation as a Success Factor

An AI Lab isn’t something you roll out. It has to emerge in co-creation:

  • our 1.5 years of Lab learning,
  • combined with our leadership development expertise,
  • and the client’s own learning and development (L&D) goals, models, and training structures.

This triangulation is what makes the Lab not only innovative but also credible, relevant, and sustainable within the organization.

3. Integration over Isolation

AI roleplays only create value when they are integrated into existing learning journeys, not used as isolated demonstrations.

That means embedding them into training modules, aligning them with objectives, and positioning them as part of the transfer process.

This way, AI strengthens the overall program instead of standing apart. It becomes a sustainable elementof leadership development – not just an add-on.

From experiment to system

Looking back, there’s a clear arc:

  • In our internal Lab, we learned the principles of immersion, design, feedback, and stakeholder involvement.
  • In client projects, we learned how to apply these principles to various industries, cultures, and learning and development (L&D) structures.

Together, these experiences show how AI can move from experiment → tailored system → scalable practice.

Final reflection

AI will not transform leadership development on its own. But when it is:

  • adapted to the industry and company context,
  • co-created with trainers, participants, and L&D teams,
  • and integrated into existing programs,

…then it can turn training into truly immersive, relevant, and scalable development.

That’s the future we’re building with the AI Leadership Lab – step by step, from lab to practice.

Rafael Ungvari

Rafael Ungvari

Artificial Intelligence Expert

Rafael is an AI specialist 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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AI Hears; Humans Listen: Become a Master of Attunement

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by Hamza Khan | Apr 18, 2025 | Digital Transformation, Leadership and AI, Leadership in the digital transformation | 0 comments

AI Hears; Humans Listen: Become a Master of Attunement

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.

AI Hears; Humans Listen: Become a Master of Attunement

Not Black Mirror. Not Severance. The reality we’re in is starting to feel a lot more like Her.

According to eye-opening research published in Harvard Business Review, the most common use of generative AI isn’t writing code, synthesizing data, or even creating content.

It’s companionship.

Let that sink in. At the frontier of one of the most advanced technological revolutions in history, people aren’t just looking for answers or performance enhancements—they’re looking to feel heard.

The Burnout I Didn’t Catch

At this year’s Leadership Horizon, my partner Bailey Parnell and I are set to announce the groundbreaking AI product we’re building at SkillsCamp. It’s the fastest-moving venture I’ve ever been a part of. 

In my previous companies, we’d maybe pivot once or twice a year. Now we’re pivoting multiple times a day. Strategies shift at breakfast. Features change by lunch. Priorities rearrange by dinner.

It’s exhilarating. It’s also exhausting.

In the middle of this whirlwind, we missed something important—one of our teammates was struggling to keep up. The constant change had become disorienting. They were slipping into the early stages of burnout. And here’s the part that really hit me:

I wrote the book on beating burnout.

The Burnout Gamble is explicitly about how leaders can prevent precisely this kind of thing. On top of that, during my keynote speech at Leadership Horizon a few years back, I preached the gospel of human-centered leadership—of slowing down to tune in. Of attunement.

Even though I had been hearing my colleague, the truth is, I hadn’t been listening.

I had only been reacting. Optimizing. Building the future of leadership. But not asking, in the way that only a human can:

“Kaif al hal?” (كيف الحال؟)

It’s Arabic for “How are you?”—but it literally translates to: How is your heart doing? AI can’t ask that. At least not yet. And even when it can, it won’t mean it.

Everything’s Amazing. Nobody’s Happy.

Over the past year, we’ve seen an explosion in AI capabilities. From Claude and DeepSeek to custom GPTs, agents, copilots, and beyond—we’ve unlocked tools that can write like us, talk like us, and think faster than us. And yet amid all this brilliance, morale is shaky. Anxiety is rising. Relationships at work feel more fragile. Loneliness is still trending. 

Somehow, despite everything being amazing…nobody seems to be fully happy. And that’s because the problem isn’t just about what’s being built. It’s about what’s being missed.

Become a Master of Attunement

Stephen Covey once said:

“The biggest communication problem is that we do not listen to understand. We listen to reply.”

These days, we don’t even reply—we prompt. We’ve become so good at asking AI the right questions, we’ve forgotten how to ask each other the real ones.

So here’s an idea: Let AI be your productivity engine. Your logic brain. Your pattern-detecting genius. But let you be the soul. The resonator. The attuner. The etymology of attunement is “to bring into harmony.” It’s not about fixing people—it’s about feeling with them.

Become a Master of Attunement

In leadership, this means mastering what I call the Listening Ladder:

Emotion

Response Style

Example

Pity

Recognize

“That’s awful. At least it’s almost Friday.”

Sympathy

Care

“I’m sorry to hear that. That sounds tough.”

Empathy

Feel

“I hear you—it sounds like this workload is really taking a toll.”

Compassion

Act

“Let’s find a way to ease your load together.”

Attunement isn’t passive. It’s an active presence. It’s emotionally intelligent alignment. It’s not just knowing what someone is going through—it’s standing with them in it, and saying: I’m here.

But Isn’t AI Getting Good at This?

Sure, AI can detect emotional cues through text or tone. It can simulate concern. It can even give decent advice. But there’s a line it can’t cross: It doesn’t feel.

AI won’t sit in silence with a teammate who just got a life-changing diagnosis. It doesn’t notice how someone’s voice slightly trembles when they mumble “I’m fine.” Machines can’t experience shame, grief, awe, or love.

And it can’t ask, from the heart: How is your heart doing?

So yes, AI may one day outpace us in logic, language, and even innovation. But the sacred skill of soul-to-soul listening—that remains deeply, beautifully human.

The Future of Leadership

Ray Kurzweil prophesied that the 21st century won’t bring 100 years of progress—it will bring the equivalent of 20,000.

But no matter how far we go, one truth stays constant: People don’t quit companies. They quit leaders who don’t listen.

As the future of work accelerates, the leaders who thrive won’t be the ones who outpace AI. They’ll be the ones who partner with AI—and lead like humans.

Consider this your invitation to become irreplaceable. Learn to attune. Ask real questions. Listen with your whole body. Respond with presence. And the next time someone on your team seems off, don’t just check their output.

Check their heart.

Hamza Khan

Hamza Khan

Keynote Speaker

Hamza Khan is a best-selling author, award-winning entrepreneur, and globally-renowned keynote speaker whose TEDx talk “Stop Managing, Start Leading” has been viewed over two million times.

The world’s leading organizations trust him to enhance modern leadership, inspire purposeful productivity, nurture lasting resilience, and navigate constant change.

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AI Transformation: How Companies Need to Adapt

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by Gunther Fürstberger | Feb 12, 2025 | Digital Transformation, Leadership and AI, Leadership Tips | 0 comments

AI transformation: how companies and leaders need to adapt

Economic history repeatedly shows how technological leaps revolutionize entire industries. 150 years ago, sailing ships dominated world trade, while steamships were only used on lakes and rivers. But within 30 years, the ratio was reversed: steamships took over the market, while sailing ships lost their importance.

Interestingly, it was not the established companies with capital that drove this change, but new companies that took advantage of the new technology. This is a pattern that keeps repeating itself – just as Blockbuster was replaced by Netflix or the traditional automotive industry is facing the challenge of electric mobility.

What does this mean for our time?

Digitization and, in particular, artificial intelligence (AI) are today’s disruptive technologies. Companies are faced with a choice: do they want to be among the pioneers or do they want to wait and see? The question of what our job description will look like in five to ten years is not a hypothetical one.

Stability or change? The influence on digital transformation

Some people tend to seek stability, to preserve security and proven values. Others, on the other hand, are change-seekers, curious and willing to explore new technologies. However, digital transformation requires companies to actively engage with change.

Europe tends to regulate rather than innovate. However, AI applications are becoming increasingly important, and it is crucial not only to consider the risks but also to seize the opportunities. Responsible participation is essential to avoid being controlled from the outside.

AI and its impact on companies

AI and its impact on companies

AI is changing many aspects of the economy:

  • Business opportunities and risks: Those who do not adapt could soon become irrelevant. Established processes can become ineffective while new technologies are faster, more efficient and more cost-effective.
  • Efficiency and productivity: Automated processes improve workflows, reduce errors and increase the speed of decision-making.
  • The world of work: Some jobs will disappear, while new ones will be created. The challenge lies in preparing employees for the new world of work.
  • Learning and development: AI makes it easier to create and personalize learning content, enabling better education and development processes in the long term.
  • Customer focus: AI can help create personalized customer experiences and better understand needs.

The role of leaders

Leaders face the challenge of creating transparency. They must educate about the potential, limitations, and deployment scenarios of AI, but they must also promote acceptance and reduce fears. A successful transformation does not proceed linearly, but rather in so-called transformation loops:

 

  1. Why? Why are we doing this?
  2. Planning: What steps are necessary?
  3. Implementation: How do we implement it?
  4. Evaluation: What works well, what doesn’t?

Methods for successful AI integration

  • Target group: Clarifies for whom and for what purpose AI is used. This involves defining what results are to be achieved and how success will be measured.
  • AI roadmap & AI transformation map: Structured implementation planning helps to formulate corporate goals clearly and in a solution-oriented manner.
  • Commitment game board: Maximizes employee participation by actively involving various stakeholders in the process.
  • ADKAR model: Helps with individual change by raising awareness, motivation, knowledge transfer, application and reinforcement. The following questions could be asked: Why do we have to change? What’s in it for me? What new skills do I need? How can I apply what I have learned? How do I maintain the change?
Learning from practice: AI at MDI

Learning from practice: AI at MDI

At MDI, we have set up an AI task force that works according to the principle “do, reflect, learn, do”. Early involvement and continuous reflection ensure that insights can be implemented quickly. In addition, we are constantly developing our AI Leadership Lab, which revolutionizes learning processes through AI-supported role playing, knowledge bots and leadership coaching.

Sustainable integration into everyday business

Regular reviews and retrospectives are crucial to the long-term success of AI in everyday business. Companies should ask themselves:

  • What are the intermediate results?
  • What are the successes?
  • What went well?
  • Where is there room for improvement?

Implementing AI requires not only technical know-how but also communication skills. Building trust is essential because the longer internal communication is neglected, the greater the resistance to transformation.

Leaders of the future

To successfully deal with AI, leaders need five core competencies:

  1. Recognizing the potential of AI
  2. Enthusiasm for lifelong learning with AI
  3. Mastering agile transformation management
  4. Ensuring ethical communication
  5. Promoting responsible thinking

The AI revolution is in full swing – companies and executives must position themselves now to remain successful in the long term.

Want to learn more about this topic? Watch the new (German) webinar here “Mastering AI Integration: Your Roadmap for Leadership Success and Sustainable Transformation” by Gunther Fürstberger. Dive into the world of AI leadership!

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.

  • LinkedIn

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by Michael Kolleger | Sep 29, 2023 | Digital Transformation, Impuls series, Leadership and AI | 0 comments

In Dialogue with Michael Kolleger: Leadership 4.0 – AI in Transition

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.

In dialogue with Michael Kolleger: Leadership 4.0 and AI in transition

In the kick-off of our new expert series “Leadership 4.0: Leadership in the Changing AI Era“, Data Scientist Michael Kolleger took the time for an interview with us. As part of this interview series, we want to get a clearer picture of artificial intelligence and talk about leadership over the next few years.

Read this interview to join us in broadening your digital horizons and gaining more insight into the complexities of AI transformation.

Hello dear Michael, thank you for taking the time for this interview! First of all, please introduce yourself. How would you briefly describe yourself and your profession?

As a Data Scientist in a senior position, I am responsible for leading a highly skilled team group specialized in data-driven solutions. My career field encompasses several key functions, including presales activities, product development, and initiating innovative ideas.

In presales, I am instrumental in convincing potential customers of our data-driven solutions. I analyze their requirements and present customized solutions to meet their business needs. In product development, I work closely with our technical team to design and develop data-driven products and services. Here, the focus is on using data to improve our products.

In addition, it is my job to foster creative ideas and innovative approaches to constantly explore new opportunities for data-driven solutions and further develop our company.

What would you say are three exciting projects you’ve worked on recently related to artificial intelligence?

  • Energy Provider, Hong Kong: As a Data Scientist on this project, I had the challenging task of analyzing data from the energy sector in Hong Kong and developing intelligent solutions for the smart grid. Our work enabled us to use real-time data to predict power consumption and production and manage the network more efficiently.

 

  • Automotive, Germany, Investigating Generation Z’s Opinion on Mobility: In this project, my team analyzed extensive data from social media to deepen Generation Z’s understanding of mobility and language preferences. These insights helped the automotive industry to develop targeted marketing strategies and better tailor products to the needs of the young target group in the future.

 

  • Banking, Hong Kong, Developing New Financial Strategies with AI: In this exciting project, we supported a global bank in HK to break new ground by developing financial strategies using artificial intelligence. We analyzed market data, global economic trends, and business news to create advanced AI models. These models helped the bank make informed investment decisions, minimize risks, and optimize its portfolio management.

That sounds really exciting! But what exactly is artificial intelligence? Please describe this term as simply as possible.

Artificial intelligence (AI) is like a smart computer that tries to mimic human thinking. It learns from information, draws conclusions, and makes decisions, much like we humans do. But it’s important to understand that AI is not a panacea. Rather, it is a tool that can be used to solve very specific problems.

Think of AI as a toolbox full of specialized tools. Each of these tools is designed to tackle a specific task or problem. For example, one AI tool may be used to recognize images, another to translate text, and yet another to make predictions based on data.

The key to using AI effectively is to choose the right tool for the right task. AI can help perform complex calculations, identify patterns in data, and automate repetitive tasks. Overall, AI can help us solve specific problems faster and more efficiently, but it requires wise application and expertise to have its full impact.

In the context of artificial intelligence, some often worry that their work will be replaced by artificial intelligence. How do you see this danger? Above all, what then is the role of a leader in the use of AI?

The concern that artificial intelligence (AI) could replace human labor is understandable. In fact, some repetitive and data-intensive tasks in many industries can be automated through AI. This can lead to efficiency gains, but it also presents challenges and opportunities for the workplace.

A leader’s role in the use of AI is to provide strategic direction and accountability. Here are some important aspects:

  • Strategic alignment: leaders need to understand how AI will impact their industry and company. They should define clear strategic objectives for how AI can help achieve business goals.
  • Resource allocation: executives are responsible for allocating the necessary resources, including talent and technology, to successfully implement AI initiatives.
  • Quality control: executives must ensure that AI applications are developed and deployed ethically and responsibly. This includes privacy, bias mitigation, and transparent decision-making by AI systems.
  • Collaboration with AI: AI can complement human work by performing routine tasks while humans focus on more creative, strategic, and interpersonal tasks. Leaders should encourage the integration of humans and machines to achieve the best possible results.
  • Crisis management: when job changes due to AI are imminent, leaders need to develop strategies for retraining and redeployment to mitigate the impact on the workforce.

Overall, the introduction of AI does not necessarily mean replacing workers, but rather shifting tasks and providing an opportunity to expand human capabilities. Leaders play a critical role in making these changes positive by finding the right balance between technology and human talent.

team meeting

What skills must a leader have in order to use AI successfully? What technical skills are necessary?

In order to successfully deploy artificial intelligence (AI) in their area of responsibility, a leader should have several skills and attributes:

  • Strategic vision: the ability to develop and communicate a clear vision and strategy for the use of AI in the organization.
  • Understanding of AI fundamentals: a basic knowledge of how AI works, its applications, and its potential is essential.
  • Technological understanding: knowledge of the technologies used for AI applications and their impact on the business.

How realistic is it that a portion of our executives will be made up of robots in the future?

Extremely unlikely.

So what would such a leadership style look like? One that consists solely of artificial intelligence?

A leadership style based solely on artificial intelligence (AI) would focus on data analysis, algorithm-based decision making, and automation. In this scenario, AI would continuously analyze large amounts of data to identify trends, patterns, and opportunities. It would generate recommendations for business decisions and make them based on objective data.

AI leadership would focus on efficiency, data optimization, and predictable operations. However, human leadership elements such as emotional intelligence, empathy and interpersonal relationships would be missing. This could lead to an emphatically rational and data-focused leadership culture that certainly neglects the creative and social aspects of leadership.

Where do you see advantages and disadvantages here? What can already be taken over by artificial intelligence, what should still remain human?

Challenges and limitations:

  • Complexity of human interaction: leaders often need to manage complex interpersonal relationships, act empathically, and communicate strategic visions. This is a capability that AI systems have so far been limited in their ability to mimic.
  • Creativity and innovation: developing new ideas, strategies, and creative solutions is an essential part of leadership that requires human intelligence and experience.
  • Acceptance and trust: Acceptance of robotic leaders in the workplace and employee trust in such systems could be barriers.

Artificial intelligence (AI) has already demonstrated the ability to automate and take over human tasks in many areas. Here are some examples of tasks and work areas where AI is being used successfully:

  • Image and speech recognition: AI systems can recognize and interpret images and speech, which is used in applications such as facial recognition, text translation and virtual assistants.
  • Chatbots and customer service: AI-based chatbots can handle customer queries, solve problems and provide information.
  • Financial and investment analysis: AI can be used in the financial industry for automated analysis of market and investment data.
  • Medical diagnosis and imaging: In medicine, AI helps diagnose and analyze medical images such as X-rays and MRI scans.
  • Manufacturing and robotics: robots with built-in AI are used in manufacturing to automate tasks such as assembly, quality control, and material handling.
  • Human resource management: AI can assist in applicant selection, monitoring employee performance, and identifying training needs.
  • Traffic control: in cities, AI systems are used for traffic monitoring and optimization to improve traffic flow.
  • Cybersecurity: AI can help monitor networks and systems for threats and take quick countermeasures.
  • Translation and speech processing: AI can translate texts in real-time and enable human-like speech in chatbots and virtual assistants.

generational management

Now that we’ve talked so much about artificial intelligence – what concrete first steps can companies/leaders take? How can we incorporate AI into our everyday work step by step?

  • Education and training: Start with training and education for leaders and employees to foster a basic understanding of AI and its applications.
  • Identify use cases: Identify specific areas or tasks in your organization where AI could add value. These could be tasks with high data content or routine tasks.
  • Data preparation: make sure your data infrastructure is solid. Clean, high-quality data is critical to successful AI applications.
  • Pilot projects: Launch small pilot projects to test the feasibility of AI applications in your organization. This allows you to gain initial experience.
  • Partnerships and outsourcing: Consider working with AI experts and vendors to leverage expertise and resources without building everything in-house.
  • Ethical guidelines: Develop ethical guidelines for the use of AI to ensure that the technology is used responsibly and in line with your values.
  • Communication and change management: Make sure you inform your employees about the introduction of AI and demonstrate the added value for them. Consider fears and concerns.
  • Measuring ROI: Establish clear KPIs to measure the return on investment (ROI) of your AI initiatives and ensure they are delivering business value.
  • Scale: if pilots are successful, consider scaling up and expanding the use of AI in other areas.
  • Feedback and adaptation: continuously gather feedback from employees and adapt your AI strategy accordingly for continuous improvement. 

One last question I’d like to ask all of our guests. What do you see as the biggest challenge for leaders in the next 5 years?

Overall, leaders in the next 5 years will face the challenge of balancing technological innovation, business growth, and social responsibility while facing a rapidly changing business environment and changes in the socio-cultural environment. The ability to adapt and use technology strategically will be critical.

 

  • Digital transformation: leaders must successfully guide their companies through digital transformation, which means adapting business models, processes and culture to remain competitive.
  • Change in the socio-cultural environment: Generation Z, born in the late 1990s and 2000s, is shaping the socio-cultural environment with new values and behaviors. Their influence has led companies to pay increased attention to social responsibility and adapt their marketing strategies to reach this target group.
  • Talent management: attracting and retaining technology and data science professionals will be a challenge. Leaders must be able to build and develop talented teams.
  • Rapid change: Technological developments and market changes occur rapidly. Leaders must be agile and adaptable to respond to unexpected events.
  • Global uncertainties: Economic uncertainties, geopolitical tensions, and global crises could affect business operations. Leaders need to be resilient and develop risk management strategies.
  • Sustainability: sustainability goals and environmental impacts are gaining importance. Leaders must develop strategies to be environmentally responsible.
  • Workplace culture and employee well-being: Creating a positive workplace culture and employee well-being is becoming increasingly important to attract and retain skilled workers.

Thank you for this exciting interview, Michael!

Michael Kolleger

Michael Kolleger

Data Scientist

Dr. Michael KOLLEGGER is a distinguished Senior Data Scientist with an illustrious career spanning over 30 years in the field of Data Analysis and Artificial Intelligence.

With more than two decades of experience specifically dedicated to Artificial Intelligence, Dr. KOLLEGGER has consistently demonstrated his expertise in gathering and analyzing business requirements, leading to the development of innovative AI solutions.

  • LinkedIn

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