Middle Management: How Sipgate and Rhenus are Dealing with AI

AI is fundamentally changing the role profile in middle management. Practical examples from Sipgate and Rhenus show that while automation takes over administrative tasks, it strengthens managers as strategic process designers and mentors. The transformation thus fosters new competencies.

Middle Management: How Sipgate and Rhenus Handle AI

The advancing integration of artificial intelligence into the working world in the fall of 2026 is reviving a classic debate in organizational theory: the future viability of middle management. While automation solutions increasingly take over administrative tasks, managers in the sandwich position are under particular pressure to adapt. Using the practical examples of the IT company Sipgate and the logistics service provider Rhenus, it becomes clear that the use of AI does not necessarily lead to the dismantling of hierarchies, but rather forces a fundamental transformation of the role profile. For corporate training, this means a shift away from purely controlling functions toward strategic process design.

At Sipgate, a pioneer of agile working methods, AI is primarily used as a tool to increase efficiency in internal communication and project management. Relief from repetitive coordination tasks allows managers to focus more on individual team development and complex problem-solving. Here, it becomes evident that AI does not replace middle management but frees it from bureaucratic dead weight. The role is changing from a mere information mediator to a mentor and internal coach who ensures that technological possibilities are used in line with the corporate culture.

Challenges and Opportunities in Logistics

In contrast, the global logistics sector, represented by Rhenus, faces the task of integrating AI into highly timed supply chains. Here, the technology primarily serves data-based decision-making. In this scenario, middle management acts as an interface between algorithmic optimization and operational implementation by the workforce. It is less about controlling individual steps and more about monitoring the system logic and intervening in the event of unforeseen deviations that cannot yet be independently captured by the AI.

These developments have a direct impact on the requirements for academic management training, such as the MBA or specialized Master's programs. Classical business administration is increasingly being supplemented by competencies in the areas of data literacy and AI ethics. Today, applicants must demonstrate that they are able not only to apply technological innovations but also to manage their impact on the human workforce in a socially responsible manner. The demand for programs that bridge the gap between technical understanding and empathetic leadership is seeing a significant increase at universities in the DACH region.