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The digital transformation is changing the way companies develop their skilled workers. The ninth step of the KIRONI model focuses on a forward-looking topic: the combination of classic leadership development with modern AI competencies. This combination is becoming a decisive success factor in today's world of work. Companies are increasingly recognising that traditional approaches alone are not sufficient. They must train their leaders in innovative technologies simultaneously. Only in this way can they support their employees in a future-oriented manner. Leadership development is thus becoming a strategic investment for long-term company success.[1][2][3]
Why Leadership Development Requires AI Competence Today
Artificial intelligence is already permeating almost every area of business today. From personnel management to strategic planning: AI tools are fundamentally changing workflows. Managers must not only understand these changes. They must also shape them and guide their team through the transformation.[1]
Classic leadership development covers communication, delegation, and team leadership. These skills remain important and relevant, but they are no longer sufficient these days. Leaders additionally need an understanding of AI systems, data utilisation, and digital work processes. This knowledge enables them to drive innovation while simultaneously identifying and mitigating risks.
An example illustrates this: a manufacturing company introduces AI-powered optimisation systems. On-site managers need to understand how these systems work. They need to know what decisions the AI makes and why. Without this understanding, they cannot guide their employees effectively. Management development with an AI focus closes this gap.[3]
The pillars of modern AI-focused leadership development
Technical understanding and digital literacy
The first foundation is solid technical knowledge. Leaders need to understand what AI can and cannot do. They should be familiar with typical AI applications in their industry. These include customer service chatbots, logistics predictive models, or HR analytics in the field of human resources.[1]
This leadership development programme emphasizes practical knowledge over theoretical depth. A sales manager doesn't need to understand every algorithm, but they should know how an AI-powered analytics system identifies customer trends. An HR manager needs to be able to grasp how AI tools screen job applications.[2]
Practical examples support this learning process: a finance company shows its leaders live how AI systems detect fraud patterns. A retail group demonstrates how algorithms optimise stock levels. An insurance company explains which AI chatbots handle customer enquiries.
Solid digital literacy is the foundation for everything else. Leadership development builds upon this to tackle more complex topics.
Change Management and Change Leadership
AI introductions are change processes. They require special leadership skills. Employees often have fears or resistances. Leaders must take these resistances seriously and address them.
An essential aspect of leadership development is therefore change management. Leaders learn how to communicate change. They learn how to involve employees rather than oppose them. They understand how fears arise and how to reduce them.
Three sectors show different requirements: In manufacturing, concerns often revolve around job losses. in the creative industry, the focus is on the question of whether AI can replace human creativity. In the financial sector, the concerns lie with data security and ethical issues.
Targeted leadership development prepares for these diverse scenarios and imparts concrete strategies for dealing with concerns and resistance.
Ethics, Responsibility, and AI Governance
AI brings ethical questions with it. Who bears responsibility for decisions made by AI systems? How is data protection ensured? How is discrimination by algorithms prevented?
Leadership development must address these topics. Leaders will become actors in the responsible use of AI. They must know what policies exist. They must understand what risks can arise. They bear responsibility for ensuring that AI systems are used responsibly.
An example from the HR department: A recruitment AI system is being introduced. Managers must understand that this system can have potential bias errors. They need to know how to identify and correct them. Management development prepares them for this role.[3]
In the banking sector, AI governance is even a regulatory requirement. Executives must understand and follow governance processes. Their executive development therefore has an additional compliance focus.
Leadership Development in Practice: Proven Methods and Formats
Blended Learning and digital formats
Modern leadership development uses blended formats. Online modules impart fundamentals flexibly in terms of time. In-person workshops facilitate discussions and exchange.
An insurance company successfully combines these formats: Online courses cover AI fundamentals. In-person workshops analyse company-specific use cases. Virtual coaching sessions support implementation thereafter.
Peer learning also plays a role. Leaders who already have AI experience share their knowledge with others. A major retail chain is successfully using this approach: store managers who have implemented AI-powered inventory optimisation are mentoring other managers.
Coaching and individual support for AI transformation
Not all leaders have the same starting conditions. Some already have AI experience, others do not. Coaching offers individual support.[1]
A good leadership development coaching programme takes these differences into account. Coaches help leaders to identify their personal AI challenges. They support them in developing strategies to overcome these challenges.[2]
A telecommunications company uses coaching strategically: managers who feel resistance to AI implementation work with coaches. Together, they reflect on their fears and concerns. The coach helps them develop new perspectives. This significantly accelerates the transformation. [3]
transruptions-Coaching specifically supports leaders with projects concerning leadership development. The focus is on practical solutions for transformation.
Mentoring and reverse mentoring for knowledge transfer
Experienced managers can help less experienced ones. And sometimes the opposite is true: Junior employees often understand technology better. They can support more senior managers.
Reverse mentoring is an innovative form of leadership development. A 50-year-old department head is mentored by a 25-year-old data specialist. Together they learn from each other: the older brings experience, the younger brings technical knowledge.
An energy company has successfully implemented this model. Executives and digital talent work in pairs. Both benefit enormously. Executive development thus becomes a mutual learning process.[3]
Project-based learning and action learning
The best learning method is often practical application. Action Learning focuses precisely on this.
Leaders work on real AI projects. They analyse real problems. They develop solutions. Learning happens during the work, not after. This is a particularly sustainable form of leadership development.[2]
A pharmaceutical company uses this approach: executives form small groups. Each group works on a real-world AI use case. One group optimises supply chains with predictive analytics. Another improves customer interactions with chatbots. A third automates administrative processes.[3]
At the end of the project, the groups present their results. This creates real knowledge and immediate added value. Leadership development becomes a value-adding activity.
Typical challenges in leadership development with an AI focus
Resistance to change and technology scepticism
What challenges do clients bring to transruptions-coaching? Clients frequently report leaders who are sceptical of AI. Some see AI as a threat to their authority.[1]
These fears are understandable and often justified. AI is indeed changing power dynamics and workflows. Leadership development must take these fears seriously. It must not ignore or dismiss them.[2]
Successful approaches support rather than pressure. They show real application examples and real benefits. They enable experiences with AI tools. They create space for questions and concerns.
A retail chain responded to this by: instead of mandating AI courses for management, they first allowed them to play with the systems. Acceptance increased significantly. Only then did formal training follow. This management development was more successful.
Rapid technological change and knowledge gaps
AI is developing rapidly. What is current today may be outdated tomorrow. Leadership development must deal with this.
A classic mistake is one-off training. Managers attend a seminar and are done. This doesn't work with rapid changes.
Modern leadership development is continuous. Monthly webinars cover new developments. Quarterly workshops update knowledge. Learning platforms enable self-directed learning. A financial services provider offers its leaders precisely this mix.
Continuous support through coaching additionally aids this process. Managers can quickly clarify their current questions.
Transferring to practice and sustainable implementation
A major problem with training: much is learned, but little is implemented. The transfer into practice fails.
Effective leadership development plans for this transfer from the outset. Participants are asked to set concrete goals for their work. They reflect on how they will apply what they have learned. They receive follow-up support.[2]
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