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

II. L eadership and A rtificial I ntelligence a) Envisioning the Future Managers have long been recognized as leaders in business (Zaccaro, 2014). A few decades ago, however, two roles were distinguished (Kotter, 1990). Since then, managers are identified with executives who run the day-to-day operations, review the numbers and take care of all business processes (Yukl, 2013). This must be separated from the role of leadership in companies (Algahtani, 2014). Of course, the tasks of both roles can also be performed by one and the same person. But the larger the corporation and the higher the hierarchical position, the more these two roles should be separated. In contrast to management, leadership goes beyond the daily business processes and focuses on the future of a company (Northouse, 2016). The reason for this is a need for envisioning future business models with a better ROI by anticipating innovations that could improve the company’s market position, or by reflecting the behavior of customers and competitors in order to predict how the market might change in future (Yukl, 2013). In times of rapid change such as the current digital transformation, it is particularly important to think about the future and dissociate from the everyday perspective, because the changes ahead could be revolutionary for many companies. Therefore, being “innovative visionary” and “having a futurist entrepreneurial mentality” are two crucial qualities leaders should possess (Klein, 2020, p. 895). At least this applies to digital leaders. But will it also have a bearing when it comes to AI? b) Revolutionary AI The digital transformation is omnipresent: laptops, smartphones, smart products. All of which have become quite familiar in recent years and are a big topic in the leadership literature. AI is just as universal in today’s world, often secret and obscure to many people. This could be the reason why leadership is a major topic in studies on digital transformation, such as Kreutzer, Neugebauer, and Pattloch (2017) but less so in studies on AI, since it can be illustrated by an equally comprehensive publication by the same author on AI: Kreutzer and Sirrenberg (2020). This is surprising, as AI has far more disruptive power than the mere digitization of business models and processes. As is often the case with jargon, the term AI “is notoriously hard to define” (Ashri 2020, 15). When it was first coined about 70 years ago, scientists were driven by the dream of soon developing machines that could compete with humans in terms of intellectual, emotional and even cognitive characteristics (Taulli, 2019). For several decades, computers were far too slow to even come close to something remotely similar to human intelligence. It is only in the last decade or so that computer performance has risen to a level that can create some kind of intelligence. However, it turned out that AI is still not quite identical to what we call human intelligence, but it shares some characteristics that scientists did not expect when AI was first conceived. To understand what AI is, it helps to describe what it does. Recent innovation leaps have led to a sharp increase in computer power. This was not only due to Moore’s law (Brock, 2006) but also because graphic processing units (GPU) have proven to be much more suitable for calculating large data blocks in parallel than the traditional central processing units (CPU) (Schürholz & Spitzner, 2019). Even before the introduction of GPUs, a method called machine learning (ML) had been developed in the 1980s. It enables computers to structure large amounts of data by breaking down terms into tiny elements. Which of these an AI uses is determined by programmers who execute algorithms with different sets of elements and who finally decide which produce the best results (Zhou & Chen, 2018). Once these elements are set, machine learning systems improve autonomously through the amount of experience they gain in resolving given data into these elements and rearranging the information of each to complete the task for which it was created. Based on the better performance of GPUs, a new and more powerful type of AI, called deep learning (DL), has been developed in recent years. DL takes advantage of the structures of natural neuronal systems that have been uncovered by neuroscience. While ML was based on elements that require conscious decisions by programmers, DL only requires specification on how many neuronal layers are to be established between the input and the output levels of the algorithm (Sejnowski, 2018). The number of those layers determines the complexity and increment of computations that a particular DL algorithm is capable of. Their number is limited only by the power of a computer and the time taken to achieve the result (Gentsch, 2018). The big difference between ML and DL is, that in ML the analytical structure of a given algorithm is defined, while in DL the algorithm independently finds out which elements it should distinguish in order to perform the given task most efficiently. Since DL machines can not only learn how to do things, but also assess what needs to be done, they are the driving force behind the current AI evolution (Ertel, 2016; Lanzetta, 2019). Two reasons for the dramatic increase in AI performance in recent years have already been mentioned: Processing power and intelligent algorithms. The third reason is the availability of big data (Hildesheim & Michelsen, 2019). Only with the help of huge data sets can AI algorithms be trained to function correctly. However, researchers and practitioners of AI point out, that AI is currently often over- and underestimated (Skilton & Hovsepian, 2018; Wess, 2 Global Journal of Management and Business Research Volume XXII Issue VIII Version I Year 2022 ( ) A © 2022 Global Journals The Impact of AI on Leadership: New Strategies for a Human - Machine - Cooperation