Int. Journal of Business Science and Applied Management, Volume 18, Issue 1, 2023
Artificial Leadership: Digital Transformation as a Leadership
Task between the Chief Digital Officer and Artificial Intelligence
Tobias Kollmann
Chair of Digital Business and Digital Entrepreneurship, University of Duisburg-Essen
Universitätsstraße 9, 45141 Essen, Germany
Email: tobias.kollmann@uni-due.de
Kilian Kollmann
Management Department, Frankfurt School of Finance & Management
Adickesallee 32-34, 60322 Frankfurt/M., Germany
Email: kilian.kollmann@fs-students.de
Niklas Kollmann
Faculty of Business Administration, Ludwig-Maximilians-University Munich
Geschwister-Scholl-Platz 1, 80539 Munich, Germany
Email: niklas.kollmann@campus.lmu.de
Abstract
Artificial Intelligence (AI) is increasingly being used in all business areas and will have a significant impact on
the associated business processes. This will be particularly the case where data is the basis for an improvement
as well as an acceleration of the associated workflows, because it is precisely this data that is the input for the
algorithms of AI. However, the rapid progress in the performance of these algorithms will also increasingly lead
to the output of the algorithms, which are not only being used to support data-driven business processes "for"
humans, but also transitioning into data-driven business decisions where the AI will also provide the resulting
instructions "to" humans. This will lead to a blending of the operational and strategic levels of business
management and will raise many new questions for the related theoretical and practical foundations. This paper
aims to highlight this area of tension, discuss the various theoretical influences, and identify the associated
research needs. This is exemplified in an area that is directly affected by this field of tension because it is
determined by data like no other: the "Digital Transformation" of existing, and the "Digital Innovation" of new,
business models and processes as well as the associated corporate management in the form of "Digital
Leadership". This is where the human (in the form of a Chief Digital Officer - CDO) and the machine (in the
form of Artificial Intelligence - AI) will meet directly. At the end - based on a decision-making theory for a
homo economicus vs. a machina economica - there is the first draft of framework for the influence of AI on
operational and strategic corporate management, which can be used as a basis for further research and practice-
related considerations. In this context, the term "Artificial Leadership", as a further development of "Digital
Leadership", is also introduced and defined for scientific research for the first time.
Keywords: digital transformation, chief digital officer, artificial intelligence, digital ambidexterity, digital
leadership, artificial leadership
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1. INTRODUCTION
Decision-making belongs to one of the fundamental disciplines of human life as well as the business area.
Therefore, it is not surprising that it has received a great deal of attention in research across various fields, trying
to understand how we make decisions, which factors influence decisions, and how future decision-making might
be improved (Johnson & Busemeyer, 2010; Letmathe & Noll, 2021). Furthermore, considering the significance
of decision-making in business, the discipline of strategic decision-making plays a crucial role in organizational
success and is highly influenced by the role of top management and the operational and strategic decision-
making process (Papulova & Gazova, 2016). Moreover, within the modern, globalized, and digitalized
economy, it is evident that information and data already play a critical role within organizations and
management (Tuffaha et al., 2022, p. 82). They have become a factor in production and competition, with a
direct or indirect influence on organizational management and decision-making (Kollmann 2022a, p. 56 ff.).
The competitive pressure, together with the growing availability of new technologies, which continue to
increase the availability and quality of data, have led to the implementation of data in the decision-making
process, termed “data-driven decision-making” (Brynjolfsson & McElheran, 2016).
Overall, the wide-scale emergence and adoption of data-driven decision-making has led to increased firm
performance in terms of output and productivity, as well as asset utilization, return on equity, and, eventually,
market value (Brynjolfsson et al., 2011). However, with increasing data availability, it is also becoming
increasingly difficult for management to keep track of the data. Today, the amount of data has become so
overwhelming that most companies have trouble making data-driven decisions, as merely 32% of German
decision-makers know which data is available to them (Amerland, 2021). Also, in the USA, only 28% of
workers said they would be comfortable using data for their jobs (Qlik & Accenture, 2020). In turn, this leads to
the conclusion that the vast majority of companies do not consider all the data that is available to them, leading
to an increased risk of less informed and poor decisions. Furthermore, the increasing availability and complexity
of structured but especially unstructured data is leading to an overload of information, becoming progressively
more challenging to collect and analyse. This development not only poses difficulties for decision-makers but
also for traditional information systems (Llave, 2018).
A possible remedy for the limitations of Information Technology (IT) in collecting, preparing, and
analysing large amounts of data from different sources are the advances in Business Analytics (BA), which have
started having an increasing impact across industries to leverage data for decision-making (Tamm et al., 2021).
Davenport (2018) has described how companies have developed analytical capabilities to enhance decision-
making over several stages, recently entering the era of “Artificial Intelligence” (AI). Over the last years, AI has
been at the forefront of technological developments and has proved to be applicable in a variety of use cases.
Overall, the adoption of artificial intelligence continues to rise, with over 56% of companies using AI in at least
one function in 2021 (Chui et al., 2021). As one of the most important, but also most complex, applications of
AI, advances in the capabilities of AI systems are bringing the benefits of the technology to the decision-making
field into sharper focus (Duan et al., 2019). However, in doing so, the use of data and the improved extraction of
insights through artificial intelligence (still) raise questions about the need for human judgement and oversight,
especially in strategic decision-making (McAfee & Brynjolfsson, 2012). Considering the human variable,
especially in its relationship to artificial intelligence, factors such as trust, acceptance and experience play an
important role in the overall decision-making process in this regard. The concept of intuition in particular has
been categorized as critical within organizational decision-making, especially when there is pressure for high
speed decision-making (Wilson & Daughtery, 2018). Therefore, it is crucial to understand the relationships
between the different factors that play a role in human decision-making and the respective interplay with
artificial intelligence when applied in data-driven decision-making.
Against this background, the use of AI will influence many theoretical and practical areas of the business.
In the area of marketing, AI will be able to identify new correlations and patterns in customer analysis and
customer behaviour forecasting. AI will also be able to better analyse the behaviour patterns of competitors and
derive strategies for its own positioning. Further, AI will be able to better analyse and thus optimize production
and organizational processes. Accordingly, AI will have a general impact on almost all scientific theories via the
following aspects: data analysis, prediction and simulation, the discovery of correlations, and an increased
consideration of rationality. In view of this contribution, however, what stands out surely is the theory of
corporate management (even if there is not only “one theory”, but different "theory areas”), which is of special
interest. The associated management approach deals therefore with the concrete arrangement of a company, thus
in particular the management with the associated organization as well as the additional functions such as
procurement and production. With regard to the influence of AI on related short-, medium- and long-term
decisions in a company, there will also be a central theoretical practical issue in particular, which addresses the
following question: where and how will an AI take over the concrete tasks of management in terms of actions
and decisions in the operational and strategic area with regard to current and future business models and
processes?
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The aim of this paper is to build an initial structure for the broad new topic area of the influence of AI on
decision making in business management. As an example, the area of "Digital Transformation" is considered, as
this is strongly influenced by data. Here, the human as manager and the machine as AI meet directly, but this
will also be observed in other areas of the company (e.g. AI in the context of digital procurement vs. the
manager in a purchasing department). Based on a decision theory for a homo oeconomicus vs. a machina
economica, the first draft of a framework for the influence of AI on operational and strategic business
management is established. This will serve as a basis for further research and practical considerations. The term
"Artificial Leadership" is introduced and defined for the first time for scientific research in this context as a
further development of "Digital Leadership". Accordingly, the article is structured as follows: after a basic
motivation of a "Digital Transformation” for every company, the human factor of related corporate management
in the form of the Chief Digital Officer is presented first. We then focus on the machine factor in the form of
Artificial Intelligence and its increasing influence on corporate management. Through a subsequent double
differentiation of the application field between an existing business and an innovative business, as well as a
consideration of the possibilities between an exploitation and exploration, we come to the construction of a new
framework, in which a distinction is made between a Digital Leadership and an Artificial Leadership for
operational and strategic corporate management. The paper concludes with theoretical and practice-oriented
implications.
2. THE NEED FOR A DIGITAL TRANSFORMATION
The importance of the Digital Economy and the associated Digital Business is a significant factor for any
kind of economic nation. In the USA, digital value creation already accounts for 8.2% of total gross domestic
product, while in Germany it is only 5.7% (IW, 2021). Accordingly, it is important for a country's companies,
and thus for the entire economy, to be internationally competitive in the area of digitization. In 2022, Denmark
was the top country in the Global Digital Competitiveness Ranking (IMD, 2022), ahead of the USA. The IMD-
Ranking aims to analyse a country's ability to adopt digital technologies and implement these technologies in
businesses and government organizations. United Kingdom ranked only 16th, Germany only 19th, and Japan
29th. According to a PwC study, Germany will only be the ninth largest economy in the world by 2050
(Hawksworth et al., 2017). Other studies paint a similar picture – also for other industrial countries – if they do
not succeed in launching digital innovations and mastering the challenges of Digital Transformation.
This finding is independent of economic fluctuations and other economic or social developments that may
have an impact on the associated need for Digital Transformation. This is because it fundamentally changes
entire industries, companies, and their business models (Kollmann, 2022a, p. v ff.) and this development has
become even clearer and accelerated since 2020 and the COVID-19 pandemic, when lockdowns made the real
economy impossible. As a result, companies must shift to digital contacts and intensify the digital economy. The
Corona pandemic has put additional pressure on digital transformation and shown companies where previously
neglected weak points and areas for action lie in relation to digitization (Kollmann, 2022a, p. v).
Even economic development with the possibility of a looming recession since mid-2022 does not diminish
the importance of Digital Transformation as a cross-sectional task for all areas of a company. Either the need for
further cost reductions through digitization or the search for additional or new sources of revenue through digital
business models or processes is addressed (Nambisan et al., 2017). Therefore, according to Kollmann (2022b)
Digital Transformation continues to affect all companies “because the effects of digital processes, products and
platforms with the associated new digital business models continue to influence the familiar real trading level
just as they dictate a new digital trading level”. Small and medium-sized enterprises (SMEs) as well as family
businesses and also large established companies, such as industrial and retail companies, face particular
challenges. While SMEs and family businesses need to ensure that they can build up the necessary digital
Know-how and invest in a digital future, large companies also have to both continue to expand their existing
real business (through digital automation) and develop innovative digital business models. Consequently,
Digital Transformation can be defined as follows (Kollmann, 2022c, p. 2)
Digital Transformation (also referred to as "digital change") is an ongoing and far-reaching process of
change in society, business and politics based on digital technologies that has a fundamental impact on
information, communication, and transactions between the players involved and leads to a new
understanding and behaviour in the social, economic and political spheres of life.
Companies are subject to constant change and have to address new opportunities and threats to their
operational business (Nambisan, 2017). In this regard, from a more practical perspective, the concept of Digital
Transformation encompasses the "use of new digital technologies, such as social media, mobile, analytics or
embedded devices, in order to enable major business improvements like enhancing customer experience,
streamlining operations or creating new business models" (Horlacher & Hess, 2016, p. 5126). In this context,
digital technologies such as artificial intelligence (AI), Big Data, blockchains, cloud services, and sensor
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technology are repeatedly presented as the drivers of Digital Transformation (Wobser, 2022). In other words,
Digital Transformation can be understood as the digitization of a company's products and services, which
enables the company to pursue new business models to design greater customer benefits (Haffke et al., 2016, p.
2).
The questions that companies should ask themselves in this context are about responsibility: Who is taking
care of Digital Transformation in the company? Are the associated changes a "technical button" that can simply
be pressed in some digital (IT) system, or is an "evolutionary head" needed who understands and consistently
implements the digital business models and processes? In this regard, the role of a CDO is often brought into
play, who, as an acting leader, is to anchor the Digital Transformation in the company via corresponding Digital
Leadership and shape it both strategically and operationally (Kollmann, 2022c, p. 24). The focus here is on
human intelligence/competence and the associated experience. At the same time, enormous developments in
machine intelligence/competence with associated algorithms can be observed, with the result being that AI can
increasingly take over operational and strategic tasks in management (Agrawal et al., 2019). As an example, the
company NetDragon Websoft made a name for itself, as, in September 2022, it was probably the first company
in the world to appoint AI as its company director, who was christened Tang Yu (Ignor, 2022). Thus, there are
essentially two aspects to the initial question: can, should, or must a "human" or a "machine" take care of the
Digital Transformation in the company?
2.1 The Role of a Chief Digital Officer (CDO)
As with many other organizational processes, Digital Transformation also requires appropriate leadership.
In this context, the CDO has been established as a new C-level position representing Digital Leadership within
the company. The CDO is the executive responsible for the Digital Transformation of a company, including the
formulation, execution, and control of the Digital Transformation strategy for products, services, and business
models, seeking digital solutions that are only made possible by digital technologies (Berman et al., 2020, p. 32;
Singh, & Hess, 2017, p. 8, Neumann, 2017). In this context, there is a widespread perception that the CDO is a
role that is not clearly delineated and whose functions and responsibilities could also be distributed among
various members of the executive suite. Contrary to this view, a study by Mindtree (2019) found that 74% of
business and IT professionals surveyed today saw a clearly defined responsibility for the CDO within their
organization, while 81% also felt that responsibilities were sufficiently differentiated for a separate CDO
position to be necessary and warranted.
In addition to this rather theoretical view, the platform markenrebell.de (2020) has also elaborated on the
tasks of a CDO from a practical perspective: the CDO needs to determine which technologies and structures are
required to make internal company processes more efficient through digitization (Digital Processes). Further,
he/she has to identify which potentials of digitization can be exploited for the company. To this end, they also
need to develop new digital services and products that increase the company's revenues on the one hand and
enhance customer satisfaction on the other (Digital Services and Digital Products). The CDO has to discover
which tools and methods need to be used to drive the Digital Transformation forward in the company. In
addition, they have to evaluate what Know-how the company's employees need to be able to implement the
individual steps (Digital Culture and Digital Know-how). Further, he/she also has the task of developing a
digital marketing, sales, and communications strategy. They have to determine which digital channels are to be
used, what budget is required, how sales can be increased via digital channels, and how customer acquisition,
sales, and support are to be structured via digital channels. They are also responsible for developing a social
media strategy (Digital Marketing and Digital Sales). Finally, the CDO needs to have an overview of what data
the company collects and how it evaluates and uses it (Digital Big Data).
Against this background, companies are increasingly handing over responsibility for digital transformation
to a Chief Digital Officer. According to the Chief Digital Officer Report (Strategy& & PwC, 2016), the global
share of companies with a CDO rose from 6% to 19% between 2015 and 2016, thus tripling. In the following
years, the share increased again to 21% until 2018 (Strategy& & PwC, 2019). Europe continues to record the
highest CDO density world-wide. There, the proportion of companies with a CDO more than doubled from 13%
to 34% compared to 2015, according to the Chief Digital Officer Report (Strategy& & PwC, 2016). In North
America, the CDO share climbed to 23%. 13% of South or Latin American companies have a CDO, and only
7% of companies from the Asia-Pacific region entrust digitization responsibility to a specific manager. Within
Europe, France is the clear leader with a CDO rate of 62%, followed by Germany (39%), the UK (35%), Spain
(33%) and Switzerland (33%). Globally, according to the Chief Digital Officer Report (Strategy& & PwC,
2019), large companies with annual revenues of more than $24 billion in particular delegate their digital strategy
to a dedicated manager; their 39% share is by far the most significant. The vast majority of digital officers are
named CDO; other common titles are also CIO (Chief Information Officer) and CTO (Chief Technology
Officer).
The increased importance of the position of a CDO in practice has subsequently made this figure
increasingly a research subject for theoretical consideration as well. Research on CDOs originated in the
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information systems literature and then transitioned into the strategic management literature. Following Moker
(2020, p. 4), CDO research can be divided into several literature streams, such as position, person, and
environment. However, the research on the person and the environment is somewhat underrepresented in this
process, compared to the extensive research on the position of the CDO. The literature on the CDO's position
includes work on their anchoring in the company (e.g., Stein & Kollmann, 2021; Stein et al., 2022), their tasks
(e.g., Berman et al., 2020; Horlacher & Hess, 2016), and leadership role types (e.g., Horlacher & Hess, 2016;
Tumbas et al., 2020). In addition, the competencies of a CDO (e.g., Kollmann, 2022c; Singh & Hess, 2017;
Tahvanainen & Luoma, 2018) and their position within organizations have been examined (e.g., Kollmann,
2022c; Singh et al.; 2020). Regarding the CDO as a person, previous research has tended to consider its personal
characteristics as a by-product while analysing its position within the organization (e.g., Berman et al., 2020;
Chhachhi et al., 2016). Finally, there have been studies on the environment, with research focusing on the
interaction between the CDO and the proximate chief information officer (CIO) (e.g., Haffke et al., 2016) and
the impact of the appointment of a CDO on stock market performance (e.g., Drechsler et al., 2019).
Against this backdrop, Sebastian et al. (2017) studied 25 large companies and found two digital thrusts,
which they pursued during the Digital Transformation process: improving customer engagement and
implementing digital solutions. The companies studied aimed to "build customer loyalty and trust by creating
thoughtful, innovative, personalized, and integrated customer experiences" (Sebastian et al., 2017, p. 199).
Additionally, companies intended to improve their operations by digitizing their product and service offerings
(Sebastian et al., 2017, p. 199). The CDOs can support both thrusts, as they are inherently responsible for
developing new digital products, services, and business models and can thus contribute to repositioning the
company's value proposition vis-à-vis the customer. Therefore, it is also suitable for improving the customer
experience on this basis.
Another issue is where and how the CDO is anchored in the company. This organizational design
parameter refers to the structural embedding of CDOs in the company. Research has found that the influence of
the CDO on Digital Transformation depends in particular on the degree of their integration into the organization
(e.g., Horlacher & Hess, 2016; Singh et al., 2020) According to Singh et al. (2020, p. 9) the CDO can be
integrated centrally into or decentralized in the organization. Either the CDO is integrated centrally into the
executive board with all decision-making powers or is decentralized in an individual department, which, in the
latter case, leads to distributed decision-making power between the business units (Horlacher & Hess, 2016, p.
2; Singh et al., 2020, p. 9). According to Horlacher and Hess (2016), decentralized CDOs have a difficult time
mastering Digital Transformation, as they do not have sufficient decision-making authority and may lack
support from the management level. Only by adding a CDO to the executive board (C-level) does the position
have the same significance as other management positions. However, this can also lead to interface conflicts
between the CDO and other leaders. However, is there a correspondingly large number of CDOs at the board
level in large German companies? Stein and Kollmann (2021) and Stein et al. (2022) answered this question
based on their study of the DAX DIGITAL MONITOR and a corresponding analysis of DAX30 (2021) and
DAX40 (2022) companies in Germany (www.dax-digital-monitor.de). At 72% of the companies (Stein et al.,
2022), digitization responsibility and competence are firmly anchored at the board level (in 2021, the figure was
60%). However, an independent CDO, who would explicitly represent digitization responsibility and
competence at the board level as a separate department, could only be observed at five companies in 2022 (3 in
2021).
Sungkono (2021) used the analysis technique of DAX DIGITAL MONITOR and applied it to the
companies in the Dow Jones-Index: “According to the analysis, it has been identified that 19 out of 30
companies are anchoring digital responsibility or have digital competence at the executive board level. This
indicates that 63% of the Dow Jones companies fulfil this criterion. Be that as it may, out of all 19 companies,
there is only one which has the role of Chief Digital Office clearly assigned in the executive board level. Other
companies typically embed digital responsibility into existing positions such as Chief Development Officer,
Chief Innovation Officer, Chief Information Officer, or Chief Technology Officer. Based on this finding, it can
be concluded that the role of Chief Digital Officer is not commonly employed in the Dow Jones companies.”
This is all the more problematic if, in addition to the operational tasks of a CDO already mentioned, their
strategic orientation as a leader is added. It is in the nature of a company's management to be (co-)responsible
for both the initiation of strategies and their operational implementation by creating the appropriate environment
for this (Gibson & Birkinshaw, 2004, p. 223; Raisch & Birkinshaw, 2008, p. 391). The CDO reviews the
existing business models and, if necessary, adapts them to new framework conditions with new digital solutions
and reviews, develops, and implements the new business models associated with digitization (Kollmann, 2022c,
p. 26). However, the examination and development of new digital business models and processes, in particular,
is a strategic component because it affects the future orientation or realignment of the company. Therefore, the
CDO can be defined as follows (Kollmann, 2022c, p. 26):
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The Chief Digital Officer (CDO) is responsible for the strategic and operational development of the
digital strategy based on IT. His main focus is on the effective and efficient development of new digital
business models with the help of information technology. He tends to be oriented toward a flexible
organization with modern and agile tools.
In all these operational and strategic tasks relating to the digitization of existing and future business models
and processes, the CDO can, should, or must be supported by data to make the right decisions, particularly
regarding the associated Digital Transformation. Thus, data have a hybrid degree of effectiveness: They are the
basis for existing or new operational digital portfolio business (digital value creation; Kollmann, 2022a, p. 56
ff.), and they are also the basis for strategic developments regarding future digital innovation business (data-
driven business decision making). However, when the data are used, the CDO remains the ultimate authority for
the actual interpretation of the data and the subsequent final decision making at the operational and strategic
level with the associated work instructions to an organization.
2.2 The Role of Artificial Intelligence (AI)
With the advent of information technology (IT), its impact on decision-making processes and decision
making in a company has increased to a new level. Information technology can be defined as "computer-based
technology for the acquisition, storage, processing and communication of information" (Molloy & Schwenk,
1995, p. 285). One of the earliest empirical studies on the use of IT in decision making, by Molloy and Schwenk
(1995), concluded that IT improves both the efficiency and effectiveness of the strategic decision-making
process. They also found that the impact of IT on firm performance was positively related to the extent of IT use
by actors within a firm. Referring to Simon's (1960) original sequential decision-making process, there have
been numerous subsequent adaptations of related theoretical considerations with the addition of IT (e.g.,
Citroen, 2011; Darioshi & Lahav, 2021; Mintzberg et al., 1976). It has been consistently found that the use of IT
is usually most beneficial, especially in the initial collection or preparation phase.
In this regard, it is important for further discussion to understand how managers (and thus CDOs) gather
their information at the beginning of the decision-making process. Nutt (2008) distinguished between two types
of approaches to information gathering. Under the "idea-imposition approach," the decision maker gathers only
limited information that corresponds to the original goal or idea. In the "discovery approach," on the other hand,
the decision maker also has to gather information about possible alternative courses of action outside the
original goal or idea by first collecting a variety of information. Nutt (2008) examined the two approaches in an
empirical study and concluded that the "discovery approach" is more successful in almost all areas. Based on
this finding, Guerra-Lopez and Blake (2011) put Nutt's (2008) findings into a data context by using information
gathering and data collection synonymously. Their results showed that leaders were more satisfied with the data
collection process when they took more of a "discovery approach."
Following this realization, extensive sources of information and data were built up as part of the Big Data
approach. The Big Data approach has thus had, and continues to have, a major impact on decision making and
the associated process. To efficiently use the Big Data approach for management decision making, the
development of analytical skills has been identified as one of the driving factors for organizational success
(Davenport, 2006). Davenport (2013) and by extension Kollmann (2020) identified three (later four) distinct
developmental phases of data-related business analytics: "Analytics 1.0" (Davenport) or the "Data Phase"
(Kollmann) was the era of management information programs (MIPs) that dealt with descriptive analytics (e.g.,
spreadsheets). This first phase was driven by computer technology, which addressed the initial collection of
relevant internal data for fact-based insights as the basis for management decision making. "Analytics 2.0"
(Davenport) or the "Big Data Phase" (Kollmann), on the other hand, was characterized by management
information systems (MIS) and the use of Internet technology to access not only internally generated data linked
through various programs but also external and thus real-time data in bulk. Finally, "Analytics 3.0" (Davenport)
or the "Big Intelligence Phase" (Kollmann) and the associated Business Intelligence Systems (BIS) introduced
data-enriched processes that analysed data at the interface with the customer and the market. Every device,
every delivery, and every customer leave a data trail in the process. With these data trails, companies have the
ability to incorporate analytics and optimization into every business decision made for the customer. With
"Analytics 4.0" (Davenport) or the "Big Responsibility Phase" (Kollmann) and the associated artificial
intelligence systems (AIS), an individual and (hopefully also) responsibility-oriented customer orientation come
into play in this respect. It is a question not only of shaping the current customer contact but also of predicting
the customer's future need for products and services in a targeted and appropriate manner (prescriptive/
predictive analytics).
Against this backdrop, empirical research has shown that companies using data-driven decision making
outperform their competitors on both the financial and operational levels. Therefore, it has long been argued that
data-driven companies tend to make better decisions (Brynjolfsson et al., 2011; McAfee & Brynjolfsson, 2012;
Russom, 2011). However, the human use of data also has its limitations. Alharthi et al. (2017) analysed some of
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the barriers to Big Data and categorized them into three types. In addition to "organizational barriers" related to
culture and "human barriers" related to a lack of digital skills, "technological barriers" related to the
complexities of data also play a significant role. Accordingly, conventional information systems can and will
reach their limits. This is precisely where systems with AI should be used now and in the future for "Analytics
4.0" (see above). AI is a highly effective technology that is becoming increasingly widespread. In 2021, more
than half of all companies were already using "artificial intelligence" (Chui et al., 2021). Among the multitude
of applications, the field of decision making has been identified as one of the most important tasks (Duan et al.,
2019).
The increasing amount of data as well as the rapidly growing possibilities of processing data enable an
increasingly better machine imitation of human thought and behaviour patterns. Consequently, the term AI in
particular is increasingly used linguistically. In the literature, there are many different definitions of AI, so there
is no uniform definition in the narrower sense. The term was first used in a proposal for a research project on
this topic by McCarthy et al. (1955). Their understanding of AI was quite broad and aimed at the simulation of
human intelligence by machines. A modern interpretation by Kaplan and Haenlein (2019, p. 17) defines AI as "a
system's ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve
specific goals and tasks through flexible adaptation." Uniformly, however, AI is described as a subfield of
computer science in which "intelligent agents" (Franklin & Graesser, 1997, p. 21) are researched and developed
(Buxmann & Schmidt, 2018). An "intelligent agent" is characterized by its ability to independently solve
problems and thus autonomously produce artificial content (Buxmann & Schmidt, 2018; Carbonell et al., 1983;
Kollmann & Schmidt, 2016, p. 49 ff.).
One particular aspect of AI is machine learning. Samuel (1959) defined this as a field of research that
enables machines to learn without having been explicitly programmed. This capability thus enables knowledge
generation based on experience. Machines can be fed with existing data sets (experiences), evaluate them, and
draw optimal conclusions based on a developed function. A subfield of machine learning that is becoming
increasingly important is so-called deep learning. Deep learning is a concept that aims to better recognize
patterns (also called representations) in data by overlaying and linking multiple successive learning layers
(Chollet, 2018). Due to the structure of the different layers, which are based on a natural neural network and
thus resemble it, the literature often refers to (artificial) neural networks (Rojas, 2013). The possibility of
machine learning opens up a very large spectrum for the potential application fields of AI, which are
conceivable in almost all areas of life. For companies, the use of artificial intelligence can lead to increases in
efficiency and productivity and enable a better response to customers, which can create added value (Gentsch,
2019). Especially in industries where large amounts of data are generated, the application of AI can lead to
competitive advantages (Brynjolfsson et al., 2011).
With regard to decision making, Berente et al. (2021) bring even more exciting aspects into the discussion.
With the aspect of "autonomy," they emphasize the increasing ability of artificial intelligence systems to act
independently of human intervention. In addition, the aspect of "learning" refers to the ability of AI to
automatically improve itself by learning from data and experience. Finally, the "inscrutability" aspect describes
AI's ability to develop new algorithmic models on its own that are understandable only (if at all) to a specific
audience, which sometimes does not include humans. The question that can now be asked is whether and to
what extent AI can completely take over a decision-making and decision-implementation process against this
background, and thus replace humans in the form of a manager (without authority to issue directives) or a leader
(with authority to instruct). The first practical applications of AI as a decision-making authority already exist.
According to a report in the NGG (2019), Aqua Römer Mineralbrunnen in Göppingen is using an AI called
"Mary" that independently "talks" to the logistics employees via radio, guides them to the exact pallets to be
picked up on the basis of its own decisions, and organizes a large warehouse via these work instructions. This
has both advantages and disadvantages because, while "Mary" makes work easier, AI has also led to a reduction
in the number of logistics personnel required by about 25% and a subsequent loss of jobs. In addition, the
employees no longer communicate with each other during work, since "Mary" now constantly issues and
demands instructions and reports.
There are numerous aspects and questions associated with this example. Digital Transformation is
particularly suitable for AI because in this area—perhaps even more so than in other areas—it is precisely data
that forms the basis. In this respect, the following discussion is particularly relevant to this area. Data is
becoming more and more extensive through increasing digitization and is thus the basis for operational as well
as strategic decisions and related work instructions for an organization. This means that, in addition to human
knowledge and existing experience, data are also the basis for the work of a CDO. At the same time, data
becomes the basis for growing machine knowledge, in which experience is built up very quickly via the
associated algorithms. The question is whether there is a trade-off or a threshold in this respect above which
decision making by AI leads to a better result than that made by a CDO. On the one hand, a possible answer
must certainly be differentiated into a more automated area within the framework of a current existing business,
in which it is a matter of clear (also calculable) operational efficiency or effectiveness criteria within the
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framework of known knowledge (exploitation). On the other hand, it enters into a more creative area in the
context of a future innovation business, where it is about assessable (not necessarily calculable) strategic
development and positioning criteria in the context of unknown knowledge (exploration). This leads to the
ambidexterity of Digital Transformation.
3. THE AMBIDEXTERITY OF A DIGITAL TRANSFORMATION
Due to the increasing complexity and speed of digitization and the associated market and competitive
environments, it is particularly important for today's companies to succeed in balancing the digitization of
existing and new business areas to remain competitive in the long term (Kollmann, 2022c, p. 32). Christensen
(1997) has already described a similar problem in the context of the "innovation dilemma", in which companies
are often unable to devote themselves to technological innovations because they concentrate too greatly on
optimizing existing business areas. Kodak and Boeing are just two examples of formerly dominant companies
that have been unable to adapt to technological changes in the market and have thus lost an enormous amount of
competitiveness (Stein, & Kollmann, 2021). While Kodak was the leading supplier of analogue photography
and missed the leap to digital cameras, Boeing was unable to defend its former leading market position against
Airbus in the aviation industry. Although this dilemma between existing business and innovation business is a
well-known challenge in strategic management (Ijigu et al., 2022, p. 48), many companies find it difficult to
combine these two fields of activity successfully. Almost two-thirds of companies currently see themselves as
competitive in the future with regard to their core business, but only about one-third of respondents rate their
company as competitive with regard to new business areas and topics (HAYS, 2018). Against this backdrop,
executives (or perhaps AI) are particularly in demand to steer their companies in relation to the changing market
circumstances and define strategic guard rails in the process. According to Kollmann (2022c, p. 33), they have
to be able to maintain the efficiency of the (real) existing business (exploitation), on the one hand, and to
address the agility and adaptability of the (digital) innovation business (exploration), on the other. The
compatibility of these two aspects in a balanced relationship, with the aim of ensuring the necessary ability to
act, particularly in connection with Digital Transformation, can be described as Digital Ambidexterity (Latin for
using both hands; Kienbaum, 2019; Kollmann, 2022c, p. 33):
Digital Ambidexterity describes the ability of organizations to simultaneously maintain (real) existing
business (exploitation) and promote (digital) innovation business (exploration) in order to remain
competitive for the digital economy.
Both the digital management level and the digital organization are of central importance for the successful
reconciliation of existing business and innovation business. This raises the question of how decision-making
processes are designed and whether more human- or machine-based data-driven decisions are (or should be)
used. This must certainly be differentiated with regard to the "cannibalization" of the (real) existing business as
exploitation or to the development of the (digital) innovation business as exploration.
3.1 The Digitization of the Existing Business
The first task to be considered is digitization of the existing business. In this area, the aspect of
"exploitation" includes the refinement and improvement of existing business processes, routines, and structures.
However, the result here is often "only" a more or less familiar digital automation of existing processes.
However, this automation of processes is a simple necessity, as is the response to related topics such as the
digital customer journey, dynamic pricing, interactive ordering, and tracking. Alongside this, the digitization of
products will play an increasingly important role; sensors, the Internet of Things, and remote maintenance are
just a few of the keywords here. However, the development of digital platforms for or around existing product
or service offerings should not be overlooked, as these platforms have proven to be a superior business model in
the network. In line with this 3-P model (processes, products, and platforms), the digitization of existing
business focuses on the following aspects (Kollmann, 2018, 2022c, p. 4): Digitizing and automating existing
business processes and, thus, supporting existing and known business activities (Digitization of old processes);
Digitizing and supplementing existing products and services with digital value creation based on data
(Digitization of old products); Building associated digital market and customer platforms for existing business
models and processes (Digitization of old platforms).
In principle, this area can be reduced to the following statement: "You digitize what you already know."
Here, the operational focus is clearly on the existing business. This means that the operational and related
organizational processes can be improved at this point (strengthened by digital automation), whereby the
existing business can be made more effective. One example is a digitally supported approval process, which, in
the context of purchasing, relieves the control authorities (i.e., primarily senior employees) and leads to an
acceleration of the procurement process. This is justified, in particular, by the new possibility of digital
automation of procurement-related decisions. In the normal case, decision rules are still given by humans, who
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now (also) have, on the basis of digitization and pertinent data analysis to procurement behaviour, a data-based
foundation for this default. However, if data is the essential basis for a decision, then AI could arrive at the right
insights just like a human being and perhaps control the processes better, especially those related to routines.
Consequently, the area of procedural decision making in the routine-oriented inventory business has
become one of the most important applications of AI. The importance of the choice of human or machine
decision-making structures and their impact on business performance is increasingly influenced by the rapid
adoption of AI, and, therefore, represents a new strategic factor that needs to be considered by management
(Shrestha et al., 2019). In general, the application of AI in decision making is not initially unproblematic. The
general normative nature of formalizing, replicating, and simulating human decision making is as much an issue
for AI programming as the specific behavioural nature in individual situations (Pomerol, 1997). Considering
this, organizational decision making, in particular, requires both analytical and intuitive approaches. AI's
strengths certainly lie (at this moment) in the first area. Therefore, a collaborative or complementary approach is
often proposed that enables synergies between humans and machines (Puranam, 2021).
Figure 1. Support and transfer function of AI-Applications for decision making in the context of
corporate governance
Source: In further development of Duan, Edwards, and Robins (2000, p. 44)
Although AI applications currently fall short of expectations, they help deploying companies achieve
higher productivity and profits through improved decision making (Durkin, 1996, cited in Edwards et al., 2000;
Moody et al., 1999). In search of a theoretical framework for the use of AI applications in business, Edwards,
Duan, and Robins (2000) examined the role of AI expert systems and their respective effectiveness at different
levels of an organization. Their results showed that AI can replace decision makers on an operational and
tactical level, while on a strategic level, AI only plays a supporting role (see Fig. 1). In the existing digital
business, with its related routine-oriented processes or predefined development paths for products in the context
of an exploitation, this would mean that the executives (and thus also a CDO) could be pushed further and
further into the background by an AI application. However, this might not be the case.
3.2 The Digitization of the Innovative Business
The second task to be considered is the digitization of the innovative business. In this area, the aspect of
"exploration" includes the development and construction of new business models and processes. Exploration
thus involves the creation of space and time to enable the innovation process in the context of idea and solution
generation (Hobus & Busch, 2011). The result is the partial or complete rebuilding of processes, products, or
platforms with the help of digitization. This rebuilding of new digital processes could, for example, lie in the use
of blockchain technology, while new digital products could be designed specifically for the metaverse. By
building new digital platforms (e.g., digital marketplaces), one could transfer any trading expertise to
completely new industries in which one had not previously been active. In line with the 3-P model already
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introduced above (processes, products, platforms), the digitization of the innovation business focuses on the
following characteristics (Kollmann, 2018, 2022c, p. 4): Digitizing new business processes as the basis of new
and future business activities (Digitization of new processes); Digitizing and developing new products and
services with digital value creation based on data (Digitization of new products); Building new digital market
and customer platforms for future business models and processes (Digitization of new platforms).
In principle, this area can be reduced to the following statement: "It's digitizing what you don't know yet."
Here, the strategic focus and future business are clearly at the centre. This means that at this point (strengthened
by digital development), the strategic and related organizational structures can be improved, making the
innovation business more effective. An example would be the decision of a textile producer to venture the new
development of a digital fashion label in the metaverse in addition to the real business, where the avatars of the
users are dressed by him with a purely digital pixel fashion. A decision here is based on a complex construct of
data-related but also experience- and situation-related influencing factors. In addition, there is a high degree of
uncertainty about the further development of all framework parameters (technology, user acceptance, market
access, etc.) with simultaneous high/higher costs for the development and construction of this new digital
business model or the new digital business processes. The question is whether one would leave such a decision
to AI, follow the competence or experience of a CDO, or find a combination of humans and machines.
In this context, Jarrahi (2018) investigated how humans and machines (AI) can collaborate in
organizational decision-making and concluded that there are two possible types of collaboration. Either the AI's
focus is on the analytical approaches and the human's focus is on the uncertain and intuitive approaches, or,
since all complex decisions have some degree of uncertainty, almost all complex decisions are a combination of
human and machine. In this setting, Shrestha et al. (2019) defined one of the first organizational structure
frameworks applicable to decision making as involving AI (see Fig. 2). By considering five specific dimensions
(horizontal—with no "examples" column), management can determine which of the four organizational
structure options (vertical) is most appropriate for decision making involving AI.
Figure 2. Support and transfer function of AI-Applications for decision making within the organizational
structure.
Source: Shrestha et al.,2019, p. 71.
In full human-to-AI (AI) delegation, decisions are made autonomically by AI algorithms without human
intervention. AI/AI-to-human and human-to-AI/AI are both sequential hybrid models related to Jarrahi's (2018)
first mode of collaboration (see above). Within these processes, humans and AI algorithms make their decisions
separately but sequentially so that the output of one decision maker is the input of the other (Shrestha et al.,
2019). However, according to Shrestha et al. (2019), the final aggregated human‒AI decision-making structure,
which is also consistent with the second type of collaboration in Jarrahi (2018) (see above), is best suited to
managerial decisions. In this structure, whole or parts of decisions are assigned to the human or AI decision
maker according to their respective strengths, and the results are merged into a collective decision. Given that
there is often no clear human or AI superiority for a decision or parts of it, Puranam (2021) also argues that an
aggregated form of collaboration is most beneficial and can lead to improved decision quality. This would mean
that in the digital innovation business and its related new processes and unknown development paths for
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products in the context of an exploration, the executives (and thus also a CDO) could at least be supported but
not displaced by an AI application. However, it is possible that this has not yet been taken to its conclusion or
that no one has as yet dared to take this to its conclusion.
Such considerations would immediately lead to the realization that if the AI components of Berente et al.
(2021) and the possibilities of deep learning are combined, the increasing amounts of data as well as the rapidly
growing possibilities of processing data would enable an AI algorithm to increasingly imitate human thought
and behaviour patterns more accurately. However, if AI continues to replace humans, taking over more and
more of their creative tasks on the basis of self-learning algorithms, then the question must inevitably be asked
as to whether, at some point in the future, creative and intuitive or strategic tasks in the context of an innovation
business might not also be taken over by a machine. Kollmann and Kleine-Stegemann (in press) have already
thought these considerations through with regard to entrepreneurship. They determined, at least conceptually,
that there could certainly be a variant with intelligence entrepreneurship in the future, in which the probabilities
of particularly promising future startups could create outputs with the help of AI (predictive business forecast).
In the future, according to Kollmann and Kleine-Stegemann (in press) "it can also be expected that AI will also
be able to make investment decisions independently and thus still be able to implement an entrepreneurial
decision (prescriptive business movement)."
However, in the present decision space of Digital Transformation with associated corporate management,
we will initially stick to the distinction drawn between the influence of AI for digitization in the existing or
innovation business and the support or takeover of decisions within the framework of either operational or
strategic corporate management. It is now clear that in the case of operational decisions in the existing business,
where exploitation is the focus, the transfer of decisions to an AI application, and the subsequent authority to
issue instructions, appears perfectly understandable (so-called Artificial Leadership or AI-Leadership). It is also
clear that in the case of strategic decisions in the innovation business, where the focus is on exploration, the
competence, intuition, and experience of the human (CDO) "still" remains decisive for decision-making (so-
called Digital Leadership), and this can at best be supported by AI applications. However, the combination areas
that, together with the two basic orientations, span a conceptual framework as a leadership model for decision
making in the context of Digital Transformation are certainly also exciting.
4. THE LEADERSHIP MODEL FOR A DIGITAL TRANSFORMATION
Based on the previous explanations, it is now possible to set up an overarching conceptual framework for a
management model for Digital Transformation in a company. First, a distinction must be made between the
digitization of existing business in the areas of processes, products, and platforms and the development of new
digital business models and processes with a view to innovation business (vertical axis; see Fig. 3). In addition,
a differentiation must be made between an operational and a strategic level for the effects of the associated
decisions with regard to the orientation of the associated corporate management (horizontal axis; see Fig. 3).
This opens up a new decision-making space, and thus a "double Digital Ambidexterity," between the existing
and innovation business and the operational or strategic decision-making via humans (CDO) or machines (AI).
As a further differentiating characteristic of the solution with regard to the use of humans (CDO) or machines
(AI) in this double Digital Ambidexterity, a distinction between exploitation and exploration as the direction of
thrust could also be helpful (see Fig. 3).
The different combinations now result in two approaches for an already known "Digital Leadership" and
two approaches for a new "Artificial Leadership". In the first case, the human being—in the area of Digital
Transformation, the CDO in particular—sits in the "driver's seat" for the associated decision-making and
implementation. In the second case, it is the machine—in the area of Digital Transformation, artificial
intelligence in particular—that is responsible for the associated decision making and implementation. In this
context, the latter consideration is initially made in a completely value-free and rational manner. It is also
significant in this case that it is made without discussion about the ethical and social question of whether a
machine may decide over a human being. At this point, it must suffice to assume that the associated legal and
social framework conditions have been clarified and that both the human being (CDO as homo economicus) and
the machine (AI as machina economica) "mean well", making their decisions as independently as possible from
external disruptive factors. Both associated management approaches will be described in the following section
to fill the framework with life.
4.1 Digital Leadership
Overall, "Digital Leadership" can be summarized as leadership behaviour that integrates the external
influences and patterns of digitization (by a person) and transfers them into a contemporary leadership style
(Buhse, 2014, p. 230). However, this also makes it clear that digitization means change and that people have to
really want the change. Many managers already find this difficult because they actually want to continue to
profit from their experience and the positions they have acquired as they did in the past (Kollmann, 2022b, p.
42). However, this usually leads to a defensive attitude and a clinging to the status quo—which no longer works
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in view of the profound changes brought about by digitization. This is because companies are aggressively
targeted by these from the outside and cannot be managed from within. According to Kollmann (2018, 2022c, p.
37) it is particularly important for companies in the digital economy that executives want Digital Transformation
(Digital Mindset), have the necessary knowledge for this Digital Transformation (Digital Skills), and
consistently implement the resulting measures as part of the Digital Transformation (Digital Execution). Only
then is the complete scope of action of a digital leader addressed (Kollmann, 2022b, p. 42).
Figure 3. The Leadership-Model for Digital Transformation between Digital Leadership for innovative
and Artificial Leadership for the existing business.
A digital leader should, therefore, be open to change and disruptive digital innovations and, ideally with the
help of a CDO, have the necessary digital skills to implement a corresponding operational implementation and
conceptual strategy in the company. Kollmann (2022c, p. 38 ff.) describes these three aspects as follows: “The
Digital Mindset is understood as the inner basic attitude and positive attitude toward already known and new
digital possibilities. This includes openness and curiosity about digital technologies and forms of work,
questioning existing procedures and processes, and the will to proactively bring about changes in the future. The
Digital Skills are understood as concrete background knowledge and know-how relating to digital business
models and processes in relation to the digital economy. This includes basic knowledge of digital data as well as
the resulting digital value creation for processes, products, platforms, and related developments. The Digital
Execution refers to the content-related and organizational implementation or management of digital projects
and/or the associated company in the course of the Digital Transformation of existing real business or the
establishment of new digital business models and processes.” As a result, "Digital Leadership" can be defined as
follows (Kollmann, 2022c, p. 37):
Digital Leadership describes a management style in which a person (in the best case as CDO) not only
wants the Digital Transformation (Digital Mindset), but also has the necessary knowledge for this Digital
Transformation (Digital Skills) and can finally also consistently implement the resulting measures within
the framework of the Digital Transformation (Digital Execution).
In merging these fundamentals with the framework already outlined for a leadership model for Digital
Transformation in a company, the respective axes (existing/innovation business and operational/strategic
management) must now be merged with the thrust of exploitation and exploration. This results in two fields of
action in particular, where Digital Leadership would be more advantageous in terms of decision making and
implementation (see Fig. 3).
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Digital Leadership as Exploitation in the Context of the Innovation Business for Operational Leadership
• Future business is analysed by a leader at the operational level via the available capabilities and existing
data on internal business processes/models (competence-oriented exploitation).
• Insights are formed based on the digital mindset and digital skills of the leader and transferred into an
individual digital execution for future production and/or work processes according to predefined target
agreements (digital transformation).
• Individual decisions also lead to immediate or mid-term effects on the work instructions to members of an
organization (data-driven directives by humans).
Digital Leadership as Exploration in the Context of the Innovation Business for Strategic Leadership
• Future business is analysed by a leader at the strategic level via the discovery of new opportunities and the
generation of new knowledge about internal and external business processes/models (competence-oriented
exploration).
• Insights are formed based on the digital mindset and digital skills of the leader and transferred into an
individual digital execution for future business models/processes according to self-generated opportunity
and risk rules (digital revolution).
• Individual decisions also lead to indirect or long-term effects on the organizational development of the
organization (data-driven development by humans).
4.2 Artificial Leadership
Overall, "Artificial Leadership" (or AI-Leadership) can be summarized as a leadership behaviour that
integrates the inner influences and patterns of the algorithms (by a machine) and transfers them into a data-
driven leadership style. On the one hand, AI as a machine is particularly strong at performing repetitive, routine
tasks and thinking systematically and consistently. This already implies, according to de Cremer (2020, p. 3 f.),
“that the tasks and the jobs that are most likely to be taken over by AI are the hard skills, and not so much the
soft skills. In a way, this observation corresponds with what is called Moravec's paradox: What is easy for
humans is difficult for AI, and what is difficult for humans seems rather easy for AI.”
This clearly argues for the use of AI applications in the area of automation, and, in terms of exploitation,
this is most likely to come into play in the operational management of existing business. An Inside Business
article on the WLW (n.d.) platform (AI is interpreted here as a robot boss) states that “a study by MIT not only
confirms higher productivity in plants with robo-bosses, but employee satisfaction is also greater. This is due to
the fact that they receive their instructions from a robot in a sober, analytically sound, and fairly distributed
manner. The examples of Amazon and Hitachi could set a precedent. Robots not only record the work processes
of individual employees. They can also quickly register the workload and productivity of the entire plant and
offset them against variable external factors. They issue their work instructions not on the basis of personal
preferences or spontaneous emotions, but on the basis of objective facts. Thanks to modern machine learning
programs, the robots are constantly evolving, automatically contributing to process improvement.”
On the other hand, AI applications are already capable of performing creative and intuitive tasks (e.g.,
music compositions and art creations) and can thus also be used for exploration in the context of strategic
corporate management for the consistent further development of the existing business. In science, different types
of creativity are distinguished (Boden, 2016). Laux (2022) picks up on these types and states: "It can be briefly
summarized that AI applications with recourse to big data can be particularly convincing in the case of
formative creativity." This includes the combination of existing elements or the imitation of a certain style.
However, AI reaches its limits when it comes to leaving a given conceptual space and transgressing existing
rules. For this transformative creativity, there have thus far been hardly any well-functioning AI applications
(Laux, 2022). Nevertheless, the formative creativity of AI seems to be sufficient for the explorative task of
logical further development of the existing digital business, also in the context of strategic corporate
management, or it will become sufficient in the near future.
However, it is particularly important for companies in this area in the digital economy that AI is provided
with the required data in sufficient quantities and with sufficient quality (Data Source) so that AI can/may be
allowed to evaluate this data with self-learning algorithms without interference (Data Analysis). The results are
then not only comprehensible to humans, but are also implemented as instructions for action (Digital Results).
Consequently, these three aspects can be described as follows: “In the context of the Big Data-Approach, the
Data Source describes the necessity of the data that is provided to a higher-level AI for an analysis being made
available in sufficient quantity, variety, speed, and quality. In the context of the Deep Learning-Approach, Data
Analysis describes the possibility of the algorithms that are used for an analysis learning more with each
calculation and thus being allowed to improve continuously; the higher-level AI is allowed to adjust
independently. In the context of the Data-Driven-Approach, Data Decision describes the consequence of the
results that are obtained from the analysis being comprehensible to humans; thus, the resulting orders of action
are accepted. Therefore, "Artificial Leadership" (or AI-Leadership) can be defined as follows:
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Artificial Leadership describes a style of leadership in which a machine (in the best case as an AI) not
only obtains the required data via a Big Data approach (Digital Source), but can also evaluate it
independently with the associated algorithms via a deep learning approach (Digital Analysis) and finally
the results which emerge are also accepted as an order for action by humans via a Data-Driven approach
(Digital Decision).
In merging these fundamentals with the framework already outlined for the leadership model for a Digital
Transformation in a company, the respective axes (existing/innovation business and operational/strategic
corporate management) must now be merged with the thrust of exploitation and exploration. This results in two
fields of action in particular, where Artificial Leadership would be more advantageous in terms of decision
making and implementation (see Fig. 3).
Artificial Leadership as Exploitation in the Context of the Existing Business for Operational Leadership
• Existing business is analysed by artificial intelligence at the operational level via the available capabilities
and existing data on internal business processes/models (data-oriented exploitation).
• Insights are formed based on non-self-learning algorithms and transferred into auto-mated decisions for the
execution of existing production and/or work processes according to predefined efficiency and effectiveness
rules (digital automation).
• Automated decisions also lead to immediate or short-term effects on the work instructions of the members
of an organization (data-driven directives by a machine).
Artificial Leadership as Exploration in the Context of the Existing Business for Strategic Leadership
• Existing business is analysed by artificial intelligence at the strategic level via the discovery of new
opportunities and the generation of new knowledge about internal and external business processes/ models
(data-oriented exploration).
• Insights are formed based on self-learning algorithms and transferred into automated digital execution for
the further development of existing business models/processes according to self-generated opportunity and
risk rules (digital evolution).
• Automated decisions also lead to indirect or medium-term effects on the organizational development of the
company (data-driven development by a machine).
5. DISCUSSION AND IMPLICATIONS
Research into the influence of AI on corporate management and the related operational and strategic
decisions is certainly only just beginning. What is certain is that the proportion of data-driven decision-making
will continue to increase as more and more AI-Systems are deployed in companies. This certainly also applies to
any resulting "Artificial Leadership" (often interpreted in the press as "robot boss"), which will be increasingly
influenced by the further development of AI-Technology. In this context, there are certainly political and
ethically relevant aspects that must be included in the human–machine consideration. Perhaps it would be
helpful to transition from a "better" or "worse" view to a goal-oriented view in the span of hard and soft skills
that are needed to make and implement decisions more effectively (also with regard to the Digital
Transformation) and to implement them. Accordingly, it is not (at least at the moment) an "either/or" decision
but rather an "also" decision when it comes to shaping the Digital Transformation in a company with a Chief
Digital Officer (CDO) and AI. Because the digitization of data is the basis for all development in both the
existing business and the innovation business, we have a particularly exciting field of investigation between the
competence of a human (CDO) and the algorithms of a machine (AI). Used correctly, both the one, in the
context of Digital Leadership, and the other, in the context of Artificial Leadership, can lead to Digital
Transformation for a company to succeed.
With regard to further research, however, the presented framework can only be a starting point to first test
it in the context of an empirical review and thus to validate it theoretically in order to subsequently motivate its
proper use in practice. With regard to the review and validation, the following questions are central:
• Will there be a measurable and significant difference for the differentiated consideration of an application of
AI between the tasks in operational (e.g. optimization of production processes) and strategic corporate
management (e.g. investment in new business areas)?
• Will or must the related conception of AI with the associated algorithms be different for the data-driven
business processes (e.g., arrangement in the context of production processes in the inventory business) as
opposed to the data-driven business decisions (proposal in the context of a competitive positioning in the
innovation business)?
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• Will there be a measurable and significant difference for the differentiated consideration of a use of AI in
the areas of exploitation (more formative creativity) and exploration (more transformative creativity)?
• Where and why will analyses, results, interpretation as well as decisions based on them be significantly
different between a human (Digital Leadership) and a machine (Artificial Leadership) and how can the
consequence be measured?
• What is the measurable impact of data-driven business processes and decisions based on Artificial
Leadership as opposed to Digital Leadership on the respective followership of employees at the
implementation level and are there significant differences here?
The results of the related further research would accordingly lead to a confirmation of or the necessity for
an adaptation of the framework (Limitation) presented here. Either way, from a theoretical point of view, this
model is intended to provide a direct and differentiated view of the use of AI in the company from the outset, as
this differs in the various areas. It calls for empirical measurement models that are still necessary in this regard
to be differentiated according to the various areas (existing vs. innovative business and operational vs. strategic
leadership) and influences (exploitation vs. exploration). The model thus offers a first structured approach to the
field of application of AI for business management, which could certainly find an application in other task areas
beyond the first exemplary topic area of Digital Transformation. In addition to these model-related issues and
reference points for related future research, however, there are certainly numerous other aspects that can be
explored with respect to the impact of an AI on the theoretical areas of the business:
• What influence does the use of an AI have on the associated acceptance at the various hierarchical levels
and how can the associated dimensions of attitude, action and use be measured?
• What impact does the use of an AI have on the operational functions of work and organizations and how
can an associated value creation between humans and machines be measured?
• What is the impact of how an AI arrives at an outcome and how can this outcome be measured and
evaluated in terms of consequences in the form of AI-KPIs?
• What changes will be observed when AI outperforms humans in terms of outcomes, and how will this
impact the evaluation of human performance, power structures, and careers?
• How can the value of an AI or its associated algorithm be determined from a shareholder and stakeholder
perspective for business valuation?
All points are subject to the requirement of developing empirical measurement models that can
unambiguously separate and measure the impact of humans and AI in order to work out the relevant influences.
Assuming that this is possible and that the results from the research that is still needed support the validity of the
framework, against the background of these theoretical requirements, there are also some exciting implications
and necessary analyses and topics for the practical point of view, among others:
• Through the framework, the CDO (or manager in another area) can be relieved of routine tasks in the
existing business through the justified delegation of control, decision-making and ordering authority to the
AI, so that he or she can concentrate more on the innovation business.
• The framework can lead to an expansion of the CDO's (or manager's in another area) area of responsibility,
who now acts as a rule maker and control authority for the formative creativity of an AI in the existing
business.
• The framework can lead to a new positioning of the CDO (or manager in another area), who can now be
interpreted as a user and controller for the transformative creativity of an AI in the innovation business.
• The framework can support the implementation of an AI in the company by the CDO (or manager in
another area), in which the individual areas are considered one after the other (for example, first
digital/artificial leadership in the area of exploitation, then digital/artificial leadership in the area of
exploration).
• The framework can support the CDO's (or manager's in another area) analysis of AI in the company,
looking at the origin, request and use of data under the different fora in each area in terms of cultural,
regional, political, and ethical influences.
Tobias Kollmann, Kilian Kollmann and Niklas Kollmann
91
Overall, with this paper we have addressed the future influence of AI on data-driven decision-making in the
context of corporate management, focusing on the field of digital transformation as an example, since this direct
influence is obvious here due to the data as a basis for decision-making. We wanted to highlight the general
importance of this topic area and illuminate it by means of the encounter between humans (in the form of a
CDO) and a machine (in the form of an AI) in goals and tasks in this area. As a result, we differentiated between
the associated decision-making and implementation by a CDO or an AI in the respective areas of existing and
innovation business, considering the use of resources (exploitation and exploration). The result was a leadership
model for digital transformation between digital leadership for innovative and artificial leadership for the
existing business. Subject to empirical verification, researchers can use this model for a better structure of their
future thinking in this area and consider effectiveness in the resulting fields under the different influences.
Practitioners can use this framework to guide their implementation of AI.
Again, the Leadership Model for Digital Transformation between Digital Leadership for innovative and
Artificial Leadership for the existing business - presented in this paper - can help to determine the application or
reference point according to the different areas (existing vs. innovative business and operational vs. strategic
leadership) and influences (exploitation vs. exploration). Overall, there will be many more questions associated
with the use of AI in the context of business management that we do not have answers to today. But because AI
has now arrived in society and business, at least since the hype surrounding Chat GPT, we need to address it.
Among many legal, political and ethical questions, we count the following in particular: Is homo economicus
being replaced by machina economica? Will there also be artificial followership by humans for artificial
leadership by a machine? Who will finally take the responsibility for the consequences of a decision and/or
instruction by an AI? The machine (if that is possible at all) or in the end the human who programmed it or who
used it? It will be crucial that we deal with these questions and developments, both theoretically and practically,
for the economy, society and politics - not at some point, but now!
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