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The digital transformation is changing the way companies develop their specialists. The ninth step of the KIRONI model focuses on a forward-looking topic: the combination of traditional leadership development with modern AI skills. This combination is becoming a decisive success factor in today's working world. Companies are increasingly recognising that traditional approaches alone are not enough. They need to train their managers in innovative technologies at the same time. This is the only way they can support their employees in a future-oriented manner. Management development is therefore becoming a strategic investment for the long-term success of the company[1][2][3].
Why leadership development today requires AI expertise
Artificial intelligence is already permeating almost every area of a company. From HR management to strategic planning: AI tools are fundamentally changing work processes. Managers not only need to understand these changes. They also need to shape them and guide their team through the change[1].
Traditional management development deals with communication, delegation and team leadership. These competences remain important and relevant. But they are no longer enough today. Managers also need an understanding of AI systems, data utilisation and digital work processes. This knowledge enables them to drive innovation. At the same time, they can recognise and mitigate risks[2].
An example illustrates this: A production company introduces AI-supported optimisation systems. The managers on site need to understand how these systems work. They need to know which decisions the AI makes and why. Without this understanding, they cannot guide their employees in a meaningful way. Management development with an AI focus closes this gap[3].
The pillars of modern leadership development with an AI focus
Basic technical understanding and digital literacy
The first foundation is solid technical knowledge. Managers need to understand what AI can and cannot do. They should be familiar with typical AI applications in their industry. These include chatbots in customer service, predictive models in logistics or personnel analyses in HR[1].
This management development emphasises practical knowledge rather than theoretical depth. A sales manager does not need to understand every algorithm. But they should know how an AI-supported analysis system recognises customer trends. An HR manager must be able to understand how AI tools screen application documents[2].
Practical examples support this learning process: a financial company shows its managers live how AI systems recognise fraud patterns. A retail group demonstrates how algorithms optimise stock levels. An insurance company explains which AI chatbots process customer enquiries.
Solid basic digital skills are the foundation for everything else. Management development builds on this to tackle more complex topics.
Change management and change leadership
AI introductions are change processes. They require special management skills. Employees often have fears or resistance. Managers must take this resistance seriously and address it[1].
Change management is therefore a key aspect of management development. Managers learn how to communicate change. They learn how to take employees with them instead of going against them. They understand how fears arise and how to reduce them[2].
Three sectors have different requirements: In manufacturing, the focus is often on concerns about job losses. In the creative sector, the focus is on the question of whether AI can replace human creativity. In the financial sector, concerns centre on data security and ethical issues.
Targeted management development prepares managers for these different scenarios and provides specific strategies for dealing with concerns and resistance.
Ethics, responsibility and AI governance
AI raises ethical questions. Who is responsible for decisions made by AI systems? How can data protection be ensured? How can discrimination by algorithms be prevented?[1]
Management development must address these issues. Managers become actors in the responsible use of AI. They need to know what guidelines exist. They need to understand the risks that can arise. They are responsible for ensuring that AI systems are used responsibly[2].
An example from the HR department: A recruiting AI system is being introduced. Managers need to understand that this system may have potential bias errors. They need to know how to recognise and correct them. Management development prepares them for this role[3].
In the banking sector, AI governance is even a regulatory requirement. Managers must understand and follow governance processes. Your management development therefore has an additional compliance focus.
Leadership development in practice: proven methods and formats
Blended learning and digital formats
Modern management development utilises mixed formats. Online modules teach the basics flexibly in terms of time. Face-to-face workshops enable discussions and dialogue[1].
One insurance company successfully combines these formats: online courses cover the basics of AI. On-site workshops analyse company-specific use cases. Virtual coaching sessions accompany the implementation afterwards[2].
Peer learning also plays a role. Managers who already have AI experience share their knowledge with others. A large retail chain uses this approach successfully: store managers who have introduced AI-supported inventory optimisation mentor other managers.
Coaching and individual support for AI transformation
Not all managers have the same starting conditions. Some already have AI experience, others do not. Coaching offers individualised support[1].
A good coaching programme for leadership development takes these differences into account. Coaches help managers to identify their personal AI challenges. They support them in developing strategies to solve these challenges[2].
A telecommunications company makes targeted use of coaching: Managers who feel resistance to the introduction of AI work with coaches. Together, they reflect on their fears and concerns. The coach helps to develop new perspectives. This accelerates the transformation considerably[3].
transruptions-Coaching provides managers with targeted support in projects relating to 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: younger employees often understand technology better. They can support older managers[1].
Reverse mentoring is an innovative form of management development. A 50-year-old division manager is mentored by a 25-year-old data specialist. Together they learn from each other: the older person brings experience, the younger person technical knowledge[2].
One energy company has successfully implemented this model. Managers and digital talents work in pairs. Both benefit enormously. This turns management development into a mutual learning process[3].
Project-based learning and action learning
The best learning method is often practical application. This is exactly where action learning comes in[1].
Managers work on real AI projects. They analyse real problems. They develop solutions. The learning happens during the work, not afterwards. This is a particularly sustainable form of leadership development[2].
A pharmaceutical company uses this approach: managers form small groups. Each group works on a real AI use case. One group optimises supply chains with predictive analytics. Another is improving customer interactions with chatbots. A third is automating administrative processes[3].
At the end of the project, the groups present their results. This creates real knowledge and immediate added value. Management development becomes a value-creating activity.
Typical challenges in leadership development with an AI focus
Resistance to change and technology scepticism
What challenges do customers come to transruptions coaching with? Clients often report that managers are sceptical about AI. Some see AI as a threat to their authority[1].
These fears are understandable and often justified. AI does indeed change power relations and work processes. Management development must take these fears seriously. It must not ignore them or explain them away[2].
Successful approaches support rather than push. They show real application examples and real advantages. They enable experience with AI tools. They create space for questions and concerns[3].
One retail chain reacted to this: instead of prescribing AI courses for managers, they first let them play with the systems. Acceptance increased significantly. Only then did formal training follow. This management development was more successful.
Rapid technological changes and knowledge backlogs
AI is developing rapidly. What is current today may be outdated tomorrow. Leadership development must deal with this[1].
One-off training is a classic mistake. Managers attend a seminar and are done with it. This does not work with rapid change[2].
Modern management development is continuous. Monthly webinars cover new developments. Quarterly workshops update knowledge. Learning platforms enable self-directed learning. A financial services provider offers its managers precisely this mix[3].
Ongoing support through coaching provides additional support for this process. Managers can quickly clarify their current questions.
Transfer into practice and sustainable implementation
A major problem with training courses: A lot is learnt, but little is put into practice. The transfer into practice fails[1].
Effective leadership development plans this transfer from the outset. Participants are asked to set specific goals for their work. They reflect on how they apply what they have learnt. They receive follow-up support[2].
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