Global Journal of Management and Business Research, A: Administration and Management, Volume 22 Issue 8

The Impact of AI on Leadership: New Strategies for a Human - Machine - Cooperation Dr. René Rüth α & Dr. Torsten Netzer σ Abstract- AI is about to revolutionize the business world. As much as AI algorithms are widely used in many companies today, development is still in its infancy. But what is evident is, that AI is a challenge to leadership and will also profoundly change the way companies are managed. This chapter discusses recent proposals for an alliance between those in charge and AI. As it turns out, there are two approaches to this synergy. The first route, for which numerous publications are available, focuses on the implementation of AI and shows, which tasks leaders should perform to ensure acceptance amongst staff and smooth operation of AI in their companies. However, little can be learned about an AI as an established employee of a company. The perspective on the relationship between leadership and AI is only addressed by a small part of the research literature and forms the second route. It merely indicates procedural issues and ethical questions as challenges for AI leadership in years to come and also gives first considerations on future leadership by AI. Once the current issues concerning management and AI have been compiled, it becomes clear that the decisive challenges from AI can only be overcome, if basic principles are followed. These are called transparent leadership in the following, outlined at the end. I. I ntroduction: AI in C orporations he dream of machines executing the work, while enabling people to focus exclusively on what they enjoy goes back centuries (Skilton & Hovsepian, 2018). Today we are closer than ever to accomplishing this aspiration. But contrary to the original hopefulness, the image of endless bliss and sweet idleness seems far from being within reach. AI keeps us on our toes, and its translation into economic processes even increases the tasks to be accomplished. Frequently new technologies not only replace earlier action steps, but also create new ones in addition to existing procedure. Some examples from recent developments (may) confirm this notion. Software for optimizing business processes has been in use for a long time. SAP, one of the most prominent providers in this field of applications, covers many business activities in planning and execution, including: controlling, sales, logistics, HR, CRM and R&D (Leukert, Müller, & Noga, 2019). AI has given this management software a boost: Once connected to automated monitoring sensors in warehouses and interfaces with devices throughout the enterprise, AI is able to independently aggregate data, evaluate what should be done in future, and even execute decisions Author α : e-mail: rene.rueth@institut-mec.de (Leukert et al., 2019). Although this accelerates the usual business processes, care and conscientiousness must be invested in the adoption of such a system into the company, in the development and maintenance of the respective software tools and in monitoring the decisions of the automated system. Once firmly established, AI creates many more possibilities for analyzing and controlling business process than were previously achievable by employees only. However, instead of replacing these people, their tasks have changed to interpreting automatically generated data analyses and operating with new parameters for evaluation. Another example comes from the financial sector, which is particularly prone to the implementation of AI measures and in which the automation of workflows is also an issue, e.g., the automatic verification of a debtor’s credibility or the cross-checking of relationships between people involved in financial fraud (Kreutzer & Sirrenberg, 2020). However, AI-based applications were also developed to perform high- frequency trading and decide on huge investments in a fraction of a second (Kreutzer & Sirrenberg, 2020). In such cases the data quality and the precision of the algorithms must be double-checked consistently. Otherwise, a flash crash like the one on May 6, 2010, which shook the financial world to its bones, could occur (Bowley, 2010). Obviously, AI does not replace human beings, but confronts us with new tasks and requires a new kind of interaction. This is surprisingly little reflected in current leadership studies. Although there are many theoretical and best- practice models for implementing AI (Ashri, 2020; Bootle, 2019; Burgess, 2018; Castrounis, 2019) there is often little talk about how to tame, control and manage AI technology in companies (Klein, 2020) by enabling people to develop a thorough understanding of an ‘augmented collaboration’ to secure a company’s survival in the future. So, what about leadership with AI? How to co-work with machines? Does AI change leadership in terms of its goals and means? In this article we will look at leadership beyond digital leadership. It will present some very current strategies for leadership with and in the face of AI. Finally, it will build on these ideas and outline a comprehensive model of transparent leadership that will be able to integrate the various demands that AI presents to business leaders. T 1 Global Journal of Management and Business Research Volume XXII Issue VIII Version I Year 2022 ( ) A © 2022 Global Journals