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The modern business world presents managers with completely new challenges. Not only do traditional management tasks have to be mastered, but technological innovations also require a deeper understanding. This is where the Management development which today goes far beyond traditional training methods[1]. The integration of artificial intelligence in particular is revolutionising the way managers lead their teams and drive companies forward. Many organisations report that their managers are specifically asking for impulses on how they can use AI systems in their day-to-day work and support their employees in doing so[2]. The KIROI method offers a structured framework that is specifically tailored to the needs of contemporary leaders.
Why leadership development today cannot do without AI expertise
The world of work is changing rapidly and with it the demands on managers. Artificial intelligence is no longer a technology of the future, but is already part of everyday working life[1]. Leaders need to understand how these technologies work and what opportunities they offer. At the same time, they must be able to discuss ethical issues competently with their teams.
The Management development must therefore be continuously renewed. Traditional seminars alone are no longer sufficient. Instead, modern managers need a mix of different learning formats[1]. They must be able to make data-based decisions and at the same time support their teams in transformation processes.
An example from industry illustrates this: A manager in a manufacturing company learns how AI-controlled systems monitor production processes[2]. They understand the technological interrelationships and can train their employees in a targeted manner. The result is greater efficiency and a stronger capacity for innovation throughout the company.
Methods of modern leadership development in the digital age
The methods used to support leaders have changed fundamentally. Today, companies utilise a well thought-out mix of different approaches[1]. This ensures the sustainable transfer of knowledge, skills and practical experience.
Seminars and training as the basis for management development
Traditional training formats remain important, but are being expanded to include digital elements[1]. One day to one week of intensive training provides in-depth knowledge. Topics range from self-management to the latest leadership trends and technical content.
Such workshops were held in a service company, where managers learnt how to support their employees in using AI tools[2]. They were given practical exercises and concrete instructions. Online courses enabled flexible participation, while in-house training took specific company circumstances into account.
Coaching and mentoring for personalised leadership development
Individual counselling supports managers in achieving their personal goals[3]. Experienced mentors pass on valuable knowledge to aspiring leaders. This is done through regular dialogue and reflective processes.
One particularly innovative approach is AI-based mentoring. An app called KI.m conducts natural dialogues with managers[4]. It acts as a personal coach, advisor and trusted partner. A young up-and-coming manager who notices discrepancies after a meeting with their team can discuss the situation with KI.m immediately afterwards[4]. Through a structured process, they receive clarification of the assignment, opportunities for reflection and customised advice.
Job rotation and practical project work
Rotation programmes promote a broader understanding of business processes[1]. Managers get to know different departments and thus broaden their personal horizons. This promotes the exchange of knowledge and creates an understanding of operational contexts.
In one retail company, managers learned through such rotations how AI systems support the optimisation of supply chains[2]. They saw the practical applications and were later able to implement them in their own areas. Action learning enables managers to work in small groups on real, complex problems[3]. At the same time, they develop their learning and problem-solving skills.
The KIROI method: A structured approach to leadership development
The KIROI method provides a clear framework for the Management development in the digital age[2]. It takes into account the specific requirements that AI integration entails. Step 9 focuses in particular on the development of AI expertise among managers.
This method helps managers to understand and utilise AI as a tool[2]. They can use it to make data-based decisions. At the same time, they learn to actively support their teams in transformation processes. The KIROI method continuously supports leaders in their further development.
A concrete example shows the practical effect: in a medium-sized company, a Management development programme with AI expertise[2]. The managers were given workshops and practical exercises. They learnt how to use AI tools in their teams. After six months, the participants reported increased confidence in dealing with AI technologies. Their employees experienced transparent and responsible leadership. Satisfaction increased measurably.
Practical implementation: from analysing potential to targeted development
A successful Management development begins with a thorough analysis of the current situation[7]. It is necessary to work out exactly where problems and deficits lie. Only then can managers be strategically developed. This process follows several steps.
Step 1: Carry out a potential analysis
The first phase of management development consists of precisely analysing the current potential[7]. What skills do managers already have? Where are there gaps? What new competences are required?
A bank branch carried out such an analysis and identified a need for development in the use of AI for customer advice. A retail group discovered deficits in the understanding of data-supported decision-making. A logistics company recognised the need to understand AI-driven optimisation processes.
Step 2: Define development goals
Based on the needs identified, HR departments develop individual objectives for each manager[1]. These objectives serve as the basis for selecting suitable measures. They must be specific, measurable and achievable.
A sales manager might have the goal of training his team members in AI-supported customer analysis. A production manager wants to understand how predictive analytics can help with maintenance planning. A project manager wants to support her teams in the agile use of AI tools.
Step 3: Select suitable measures
The next step is to select the appropriate measures to achieve the objectives[1]. A well thought-out mix is ideal. Seminars on management techniques often form the basis. Practice-orientated projects promote direct application. Coaching enables individual support. Peer learning creates mutual knowledge transfer.
An IT company combined online courses on AI basics with practical workshops. Managers worked on real challenges from their working environment. A consulting firm used mentoring programmes in which experienced partners accompanied young managers. An industrial company relied on lunch & learn sessions with small GPT demos from everyday working life and open AI workshops in which teams collected use cases themselves[8].
AI expertise: key requirements for modern managers
Managers today need to develop new skills in order to be successful in the AI era[6]. This concerns not only technical knowledge, but also soft skills and strategic thinking.
Data understanding and data literacy
Managers need to understand how data is collected, processed and interpreted[8]. They make data-driven decisions. A retail manager uses AI analyses to optimise inventory management. A service area manager relies on data-supported predictions for personnel planning. A product developer interprets customer data for product innovation.
Systemic thinking and AI integration
Managers must recognise how AI systems are integrated into existing processes[8]. They understand the connections and consequences. A transport manager sees how AI-supported route optimisation improves efficiency and sustainability. An HR manager recognises how intelligent matching systems help with recruitment. A customer service manager understands how chatbots are transforming support processes.
Transformational leadership in digital change
Transformational leadership means leading teams through change[6]. Managers must convey vision and reduce uncertainty. They guide their teams through technological upheavals with transparency and empathy.
A factory manager communicates openly about how AI systems are changing work. He highlights opportunities, addresses fears and actively involves his team. A sales director establishes training programmes for his employees. A project manager promotes experimental learning and also accepts mistakes as a source of learning. This creates an awareness of the responsible use of AI.
Concrete examples of successful leadership development with AI
Practical experience shows that integrative approaches to Management development work. Various companies have reported successful implementations[2].
BEST PRACTICE with a customer (name hidden due to NDA contract): A management development programme with AI expertise was introduced in a medium-sized company. The managers were given workshops and practical exercises on how to use AI tools in their teams. After six months, the participants reported that they were more confident in using AI technologies and were able to actively support their teams. Employee satisfaction increased because managers handled AI transparently and responsibly. The company was able to implement new business processes more quickly and increase its ability to innovate.
Another example shows the effect of continuous support: in an industrial company, a consulting team held lunch & learn sessions with GPT demos[8]. Open workshops allowed the team to collect use cases themselves. The introduction of a custom GPT with self-developed prompts followed. The result was not fear, but curiosity. Employees took responsibility for using AI. The manager became an enabler for digital self-efficacy.
Challenges and solutions in leadership development
The integration of AI expertise into the Management development presents companies with new challenges[6]. It is not just about technical knowledge, but also about acceptance and continuous adaptation.
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