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Leadership and AI: Between Responsibility and Opportunity

Leadership and AI: Between Responsibility and Opportunity

by Marina Begic | May 6, 2025 | Digital Transformation, Leadership and AI, Leadership Tips | 0 comments

Leadership and AI: Between Responsibility and Opportunity

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

Leadership and AI: Between responsibility and opportunity

Artificial intelligence is no longer a pipe dream – it is changing our working world here and now. It is about much more than technology: it is about attitude. How do we want to lead when machines think for themselves? How do we provide orientation when uncertainty is becoming the new constant?

Trust instead of fear

Many leaders worry about being replaced by AI. But this fear is rarely justified. Julie Sweet, CEO of Accenture, says that not a single one of her client companies plans to replace employees with AI. On the contrary – the technology is intended to relieve, not displace.

An international study of over 4,000 executives shows that the majority see AI as an opportunity for efficiency, better decision-making, and higher quality of collaboration. At the same time, there is often a lack of know-how, courage, and a clear strategy to truly leverage this potential.

This is where leaders are called upon to take responsibility – not in the future, but now. After all, we cannot outsource technological developments. We are challenged to recognize the opportunities of AI – and to boldly break new ground.

Beyond Efficiency – How AI Can Make Leadership Better

AI is often reduced to speed, cost-cutting, and automation. But its true value lies in enhancing quality: helping us to act more strategically, communicate more clearly, lead more reflectively, and master complexity.

Artificial Intelligence does not replace leadership – it enhances and empowers it. When used wisely, it strengthens human connection, creates space for meaningful work and reflection – all the things that define modern leadership.

AI as a lever for better leadership – not only for efficiency

Three Levels Where AI Strengthens Leadership

1. Individual Level

Through AI, leaders gain time by automating tasks like text generation, research, or translation. This frees up space for strategic thinking, personal development, and effective leadership.

2. Team Level

AI-powered tools improve collaboration and communication through feedback systems, knowledge platforms, and digital simulations. Meetings become more structured, decisions more grounded.

3. Organizational Level
Data-driven decisions, automated processes, and innovation impulses – AI enables organizations to become adaptive and learning-driven. Leadership becomes a catalyst for true transformation. 

Technology Is Not Enough – Values Remain Central

The more algorithms become part of our everyday lives, the more crucial human qualities become: empathy, responsibility, and ethical orientation. Good leadership remains human where it matters – wherever decisions impact people.

The use of AI brings new questions: What should be automated – and what must remain human? Which values must be preserved? Leadership today means finding clear answers and demonstrating authentic values.

At the same time, even though empathy itself can’t be programmed, AI can sometimes appear more patient or neutral than humans. What matters is not what AI can do theoretically, but how we shape and use it.

Leading today means creating spaces for experimentation, encouraging reflection, and integrating technology responsibly, not out of tech enthusiasm, but because we want to shape the future.

Learning as a Leadership Mandate – Rethought with AI

Judith Marks, CEO of Otis, summed it up: Leadership means setting a strategic direction while continuously learning. This is precisely where AI can unlock enormous potential.

What many e-learning platforms have long promised, AI can finally deliver: individualized, flexible, and needs-based learning – anytime, anywhere. Learning paths adapt dynamically, and feedback is delivered in real-time.

Especially when it comes to building soft skills – like conversation techniques, feedback, or conflict management – AI is a powerful enabler. Intelligent simulations react live, reflect real-world challenges, and promote sustainable development.

At MDI, we actively use this technology in leadership training, especially for interactive roleplays designed to strengthen leadership skills. Participants receive direct, situation-based feedback, boosting their effectiveness through repetition and practice.

Why the big breakthrough is still a long time coming

Why the Big Breakthrough Is Still Pending

Despite positive attitudes, studies show that only 13% of companies report a tangible AI impact. Why is that?

One key factor: Trust. Julie Sweet distinguishes two dimensions:

  • Functional trust: Does the technology work reliably? Built through usage, experience, and good change management.

  • Emotional trust: Will AI take away my job? Will it diminish my role?

It’s often emotional trust that becomes the stumbling block. Yet history shows: change has always been a constant in the labor market. 80% of today’s jobs didn’t exist 100 years ago.

Thus, the real question is not: “What will AI take from me?” but rather: “What can it give me – and what will I make of it?” This is where modern leadership truly begins: by driving a shift in perspective.

First Steps – How Leaders Can Get Started

1. Experiment yourself: Try tools like ChatGPT for everyday tasks. Build realistic familiarity.

2. Communicate openly: Share your experiences and uncertainties. It builds trust.

3. Enable experimentation: Encourage your team to try new tools. Build a culture of learning.

4. Discuss ethics: What can and should be automated? What must remain human?

5. Identify potentials: Where along the value chain can AI create real added value?

6. Lead by example: Show authentic values, use AI thoughtfully, and actively shape the future.

Conclusion: Future-Proof Leadership Combines Humanity and Technology

AI is not a threat nor a miracle cure – it is a tool. How we use it will determine its value.

If we recognize Artificial Intelligence as an opportunity to make our work more meaningful, our communication clearer, and our decisions more sound, we create a new kind of leadership – one that unites technology and humanity. For greater impact, greater purpose, and a stronger future.

Marina Begic

Marina Begic

Head of Business Development – Digital Transformation Driver

Marina has been working on new, effective learning methods and the future of corporate learning for over 15 years. In her current role, she is responsible for Digital Business Development at MDI, where her focus is not driven by the current buzzwords, but primarily on the feasibility of digital transformation for clients such as Erste Group, Lenzing, Semperit, Deutsche Bahn, Andritz AG, Uniqa, Mayr-Melnhof, Frequentis, RHIM. Her greatest strength is bringing loose ends together, which she impressively demonstrates time and time again with her big picture view and multi-dimensional approach. Her greatest passion is to provide learners not only with an experience, but also with real, lasting value for their real challenges.

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

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

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AI as a Co-Trainer in Leadership Development

by Gunther Fürstberger | 14. November 2023 | Impuls series, International leadership development, Leadership and AI | 0 Comments

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by Peter Grabuschnig | 17. August 2023 | International leadership development, Leadership in the digital transformation, training new leader | 0 Comments

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What is Sustainable Individualization in Leadership?

by Iris Burner | 28. June 2023 | Digital Transformation, Leadership Impact, Leadership in the digital transformation | 0 Comments

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by Aline Depoorter, Jana Wölfl | 26. June 2023 | Leadership Impact, Leadership Tips, MDI Inside | 0 Comments

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AI Transformation: How Companies Need to Adapt

AI Transformation: How Companies Need to Adapt

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.

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Mastering Communication: The Why, How, and What

Mastering Communication: The Why, How, and What

by Marcin Swierkocki | Jan 14, 2025 | Agile Leadership, Leadership Tips, Short Knowledge Bits | 0 comments

Mastering Communication: The Why, How, and What

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

Mastering Communication: The Why, How, and What

Have you ever heard an employee say something like, “I won’t be able to meet the upcoming deadlines”? While the words may seem straightforward, they often hint at deeper underlying concerns. In this article, I delve into this statement through the lens of two well-known iceberg models, uncovering what lies beneath the surface…..

The iceberg models of Paul Watzlawick (of communication) and William Ury’s/Roger Fisher’s and Daniel Shapiro’s (positions vs. interests) share a common structure that emphasizes the elements of human interaction. Aligning these two models provides a deeper understanding of the interplay within communication strategies.

Watzlawick’s communication iceberg model:

  1. Above the surface – Fact-based communication: The pure content of the communication, such as words, facts or data communicated to the interlocutor. E.g.: ‘I want to complete this project by the end of this week’.
  2. Below the surface – Relational communication: The unspoken and emotional dynamic that underlies the actual content, such as tone, trust, non-verbal signals, and power. E.g.: A leader’s tone of voice suggests frustration or urgency, which influences how the message is received.

Ury and Shapiro’s iceberg model – positions vs interests:

  1. Above the surface – Positions: Explicit messages about what someone wants. Positions are noticeable but do not yet reveal underlying motivations. E.g.: ‘I need a 10% increase in my head count next year’.
  2. Below the surface – Interests: The deeper needs, desires, fears, and motivations that drive a position. Interests often refer to humanistic needs such as autonomy, recognition, or security. E.g.: Asking for a higher amount of workers next year reflects my need for recognition, security, or justice.

Both models emphasize the importance of understanding not only what is visible, but also what lies beneath the surface in human interaction. They agree with each other to the following extent:

Watzlawick’s iceberg model of communication

Ury’s/Shapiro’s iceberg model – positions vs interests

Fact-based communication (above the surface): Focuses on the pure content of the communication, such as words, facts or data.

Positions (above the surface): A clear request or message about what is wanted.

Relational communication (below the surface): Focuses on implicit dynamics such as tone, emotions, trust and the quality of the relationship.

Interests (below the surface): The deeper needs, concerns and motivations driving our positions.

Key findings: What is said is influenced by relational aspects (e.g. trust, power). Ignoring relational dynamics quite often leads to misunderstandings.

Key findings: Specific positions often mask hidden interests. Only an understanding of mutual interests can lead to a more constructive outcome.

Communication models

Practical implementation (1):

1. Communication while staying in touch with your Stakeholders:

Factual communication (based on your position) only serves as a starting point in reaching a solution, while relational communication (tone, trust) determines how your position is perceived. Recognizing deeper interests guides relational communication to build rapport and mutual understanding.

2. Conflict resolution:

Relationship breakdown (the hidden dynamic in Watzlawick’s model) results from long-established positions (evident in the Ury/Shapiro model). Addressing relational issues (e.g. respect, autonomy) generally leads to the discovery of shared/agreed interests.

3. Key questions so as to ‘dive below the surface’:

  • What is being said by the other side? (Positions/facts)
  • How is it being said? (Relationship dynamics)
  • Why is it being said? (Interests/basic motivations).

Practical implementation (2): Conflict in the workplace.

Scenario: An employee says: ‘I am unable to meet the upcoming deadlines’.

  • Watzlawick’s perspective: The actual message is failure to meet a deadline; the relational message may (our assumption) include frustration or fear of being perceived as an incompetent co-worker.
  • Ury’s/Shapiro’s perspective: The position is to refuse to meet a deadline; the interest may be to avoid burnout, being overloaded, or the need for greater clarity on priorities.

Conclusions:

By addressing both relational communication (e.g. showing empathy, and understanding people’s psychological needs) and underlying interests (e.g. redistribution of tasks or a new role in the team), a leader is able to solve issues/problems more effectively than by merely focusing on positions or factual messages.

Marcin Swierkocki

Marcin Swierkocki

Trainer, Coach & MDI Partner

Marcin Swierkocki works as an HR business consultant who has specialized in L&D, change- and project management. He brings over 25 years of international experience in change management and operational development with him. His personal motto is influenced by Viktor Frankl: ‘Between stimulus and response there is a space. In that space is our power to choose our response. In our response lies our growth…’. His personal inspiration comes from his optimistic and positive character and by draining the energy that successfully supporting others gives him.

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Self-Leadership: Harnessing the Power Within

Self-Leadership: Harnessing the Power Within

by Meike Hinnenberg | Dec 17, 2024 | Best Practice, Leadership Tips, Short Knowledge Bits | 0 comments

Self-Leadership: Harnessing the Power Within

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

Self-Leadership: Harnessing the Power Within

In today’s fast-paced and unpredictable world, we often lack control over external circumstances or how others treat us. However, we possess one powerful tool: the ability to choose how we respond.

This doesn’t mean conforming to every situation or forcing solutions. Instead, self-leadership focuses on understanding and managing our thoughts and emotions —skills that can profoundly influence our relationships and leadership abilities.

The Core of Leadership: The “Me” Circle

Imagine leadership development as a ripple effect. At the center lies the self—the starting point for influencing all outer circles: individuals, teams, organizations, and the wider environment. The “me” dimension is foundational.

How we connect with others or design organizational systems stems directly from our inner state. For now, let’s focus on this pivotal core: self-leadership.

Key Dimensions of Leadership we Focus on

Empathy vs. Sympathy: Choosing Connection

Empathy is a cornerstone of self-leadership, fostering connection, while sympathy can unintentionally create distance. According to Brene Brown, empathy encompasses:

  • Perspective-taking: Seeing the world through someone else’s eyes.
  • Non-judgment: Withholding judgment about others’ experiences.
  • Emotional recognition: Identifying and validating others’ emotions.
  • Feeling with others: Truly sharing in their emotional experience.

Leaders often struggle with vulnerability, imperfection, and the uncontrollable aspects of life and relationships. Our biological wiring doesn’t help—we’re programmed to detect and react to potential threats, a survival mechanism that can hinder connection. Recognizing and addressing this is key to empathetic leadership.

Cultivating “Response-Ability”

While we can’t control external events, we can control how we react—our “response-ability.” Tools like deep, conscious breathing and regular meditation can help calm the brain’s alarm system, creating space between stimulus and response. Gratitude practices can shift our focus toward the positive, reducing the weight of perceived threats.

These habits empower leaders to make decisions thoughtfully, even amid complexity and conflicting needs.

Leadership Decision-Making: Navigating Complexity

Making decisions as a leader is inherently challenging. It’s impossible to satisfy everyone, and every decision invites criticism. Understanding this reality helps us navigate conflict, a normal part of organizational life

Decisions will always carry trade-offs, but by including diverse perspectives and maintaining self-awareness, leaders can strive for the best possible outcomes.

Embracing Your Inner Team

Self-leadership also involves recognizing and harmonizing the many voices within us—our “inner team.” Each voice represents a perspective or trait, such as the Perfectionist, the Empathetic, or the Fearful. Listening to and integrating these voices strengthens decision-making and communication.

Embracing your inner Team

Practical Exercise:

Start by asking yourself a question, like, “Should I address this conflict?” Visualize the various inner voices involved and assign them roles or characters. For instance:

  • The Perfectionist: Insists on flawless execution.
  • The Fearful: Warns of potential risks.
  • The Rational: Seeks logic and balance.
  • The Angry: Advocates fiercely for boundaries.

Identify which voices are louder or more resistant and allow each to speak. Then, appoint an “inner team leader” to mediate, ensuring all perspectives are heard and integrated into a coherent plan.

In my own inner team, I balance voices like the empathetic, the strong, the fearful, and the rational. Each one has value, and my inner leader works to harmonize them, fostering collaboration and guiding decisions that reflect the best of all perspectives.

Conclusion

Self-leadership isn’t about perfection—it’s about awareness and intentionality. By cultivating empathy, practicing response-ability, and embracing the wisdom of your inner team, you can make decisions with clarity and confidence.

When we lead ourselves effectively, we empower others to thrive, creating ripples of positive change that extend far beyond the “me” circle.

Meike Hinnenberg

Meike Hinnenberg

Learning & Development Consultant

Meike Hinnenberg is a trainer at MDI Management Development GmbH and specializes in communication, conflict management, diversity & inclusion, and lateral leadership.

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by Raphaela Pouzar | Aug 27, 2024 | Leadership and AI, Leadership in the digital transformation, Short Knowledge Bits | 0 comments

 

Navigating the Ethical Challenges of Algorithmic Decision-Making

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Navigating the Ethical Challenges of Algorithmic Decision-Making

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Navigating the Ethical Challenges of Algorithmic Decision-Making

Artificial Intelligence (AI) is transforming our world, from personalized recommendations to advanced medical diagnostics. However, as AI becomes more integrated into our lives, a critical issue has emerged: algorithmic bias. This bias occurs when AI systems are trained on flawed or incomplete data that lacks diversity, leading to unfair or discriminatory outcomes and often perpetuating existing inequalities.

What is AI Bias?

AI bias arises when algorithms reflect and amplify societal prejudices embedded in their training data. For example, suppose an AI system is trained predominantly on data from one demographic group. In that case, it may perform poorly for others, leading to unfair decisions in areas like hiring, healthcare, and law enforcement.

The Dual Nature of AI

AI offers immense benefits, but these are accompanied by significant risks. In healthcare, for instance, AI can assist in diagnosing diseases, but if the training data lacks diversity, the AI may misdiagnose conditions in underrepresented groups, exacerbating health disparities.

Global Implications: Data Colonialism

AI bias isn’t just a local issue—it has global ramifications. In many cases, data from the Global South is used to train AI systems that benefit the Global North, a practice known as data colonialism. This exploitation deepens global inequalities, as those in the Global South often do not share in the benefits of AI technologies.

 

Global Implications: Data Colonialism

Addressing AI Bias

To mitigate AI bias, it is essential to adopt a responsible approach to AI development. This involves recognizing the duality of AI—its potential for both benefit and harm—and taking proactive steps to minimize risks. Here are some key strategies:

  1. Diverse and Representative Data: Ensuring that AI systems are trained on diverse and representative datasets is crucial. This includes data from different demographic groups, geographic regions, and socio-economic backgrounds to minimize bias.
  2. Transparency and Accountability: AI systems should be transparent, with clear explanations of how decisions are made. Additionally, there should be mechanisms for accountability, allowing users to challenge and understand decisions that negatively impact them.
  3. Ethics by Design: Integrating ethical considerations into the design and development of AI systems is vital. This includes ongoing assessments of how AI systems impact different groups and the potential risks involved.
  4. Global Collaboration: Addressing AI bias requires global collaboration, with input from diverse stakeholders, including those from marginalized communities. Policymakers, industry leaders, and civil society must work together to create regulations and standards that ensure AI development is fair and inclusive.

Conclusion: A Path Forward

As we move forward into an increasingly AI-driven world, it is crucial to remain vigilant about the biases embedded in these technologies. Only by acknowledging and addressing these issues can companies ensure that AI serves as a tool for positive change, rather than perpetuating existing inequalities.

Murikah, W., Nthenge, J. K., & Musyoka, F. M. (2024). Bias and ethics of AI systems applied in auditing – A systematic review. Scientific African, 16, e02281. https://doi.org/10.1016/j.sciaf.2024.e02281

Ukanwa, K. (2024). Algorithmic bias: Social science research integration through the 3-D Dependable AI Framework. Current Opinion in Psychology, 58, 101836. https://doi.org/10.1016/j.copsyc.2024.101836

Caro-Burnett, J., & Kaneko, S. (2022). Is society ready for AI ethical decision-making? Lessons from a study on autonomous cars. Journal of Behavioral and Experimental Economics, 98, 101881. https://doi.org/10.1016/j.socec.2022.101881

Arora, A., Barrett, M., Lee, E., Oborn, E., & Prince, K. (2023). Risk and the future of AI: Algorithmic bias, data colonialism, and marginalization. Information and Organization, 33(3), 100478. https://doi.org/10.1016/j.infoandorg.2023.100478

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Raphaela Pouzar

Marketing Team Assistant

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