Rahild Neuburger

How is leadership changing in companies in the AI age?

Rahild Neuburger: In principle, AI systems are technological tools that can support managers and employees. This is associated with direct and indirect implications for the management of companies. First of all, AI systems in the company must interact with employees and thus require new types of organization and collaboration to be implemented by managers. At the same time, AI-supported assistance and automation systems can relieve managers of operational tasks. As a result, freedom is created that managers can use on the one hand for human-centered, cooperative personnel management; on the other hand, for innovation and strategic projects. Both are tasks of increasing relevance, especially in the current times. For example, it is becoming increasingly difficult to recruit employees for the company and to retain them in the long term. At the same time, the current economic developments and turbulences require creative solutions to strengthen the innovative and competitive power of companies. In this respect, AI offers real potential here to focus on the most important leadership tasks - creative problem solving, employee motivation and coaching, and open and appreciative communication with employees. In the AI age, managers are thus allowed to focus on their human strengths such as communication, intuition, empathy, creativity, or even mastery of complex situations.

How can AI systems support managers in their tasks?

Rahild Neuburger: The strengths of AI systems are their ability to analyze and evaluate large volumes of data and to automate recurring standardizable cognitive processes and tasks. Both open up considerable potential for managers. In particular, AI systems can assist with recurring management tasks, such as creating and managing duty rosters and shift schedules, assigning tasks, assembling and configuring teams, or budget controls. AI-supported process mining solutions can map, analyze and sustainably optimize business models and processes; in the area of human resources, AI systems help with application processes. Analyses created with the help of AI serve managers as a basis for operational and strategic decisions. Concrete examples are process analyses, profitability analyses or analyses of current and expected customer behavior. Thanks to such analyses, executives are not only able to make relevant decisions more quickly. They can also quickly identify inconsistencies or problems in, for example, production or customer behavior and respond to them in a timely manner. Finally, AI systems can also support managers in their duty of care, for example when they use such analyses to warn of high levels of stress and burn-out risks among employees.

What do companies need to consider to ensure that the introduction of AI systems in the workforce is successful?

Rahild Neuburger: AI systems are not just a new technology. Due to their ability to learn and draw conclusions quasi independently, they represent a novel element in the organizational and working world. This can cause employees concerns, such as whether AI use can lead to external control, to fears of losing their own sphere of activity. Recognizing these fears at an early stage and counteracting them is one of the essential prerequisites for ensuring that the introduction of AI systems is successful and their potential is exploited. This initially requires appreciation of the employees and their concerns; but also constructive handling of fears that arise. These fears can possibly be prevented if the benefits and relief effects of an AI system as a tool can be brought to the fore. After all, the clearer the added value for each individual becomes and at the same time the fear of losing one's own job is reduced, the greater the openness and acceptance among employees is likely to be. Open communication with employees is therefore recommended. In addition, the risk of negatively perceived conclusions and decisions on the part of the AI systems must be reduced through the appropriate design, further development and training of the AI systems.

For more information on the topic, see the whitepaper Leadership in Transition - Challenges and Opportunities through AI from Plattform Lernende Systeme.