title: Business Models Powered by Machine Learning: Exploring How Machine Learning Changes the Ways Organizations Create, Deliver, and Capture Value
Creators: name: Vetter, Oliver Andreas
Publisher: name: UNSPECIFIED
Year: 2024

description: In today’s world, artificial intelligence (AI) permeates almost all areas of human life. Modern AI supports us both in our leisure time (e.g., built into applications on our smartphones that recommend music we may like, recognize people in pictures, or act as digital assistants) and in our work (e.g., by automating tasks or creating analyses, predictions, or almost perfectly formulated texts). For organizations, AI, particularly instances of AI built with the data-based learning approach of machine learning (ML), also unlocks entirely new possibilities. Such ML systems can, for instance, be integrated into organizational processes to achieve efficiency gains by automating (parts of) tasks or to elevate decision-making quality by provisioning information. Furthermore, as the examples above illustrate, ML also enables the creation of novel kinds of products and services. The associated entrepreneurial opportunities unveiled by the latest technological advancements in the field of ML are correspondingly diverse and numerous, offering enormous potential for exploitation through suitable business models. A business model is an activity system that illustrates the logic of how an organization conducts business, i.e., how it creates value through the creation of its products and services, how it delivers this value to its customers, and how it ultimately captures value for itself, e.g., in the form of profits.
However, at the same time, the challenges that accompany the integration of ML into the business models of organizations exhibit similar diversity and also differ from those posed by other digital technologies. Therefore, the existing literature underscores that organizations wishing to harness the power of ML to drive their business models must carefully consider the peculiarities of the technology to be able to benefit in the long term. Yet, the state of existing research on the actual implementation of the various facets of ML-driven business models is sparse and lacks insights into their alignment with the particularities of ML. To expand this understanding in both academia and practice, this dissertation incorporates five papers that successively investigate ML-driven business models along the three business model dimensions of value creation, value delivery, and value capture. It examines both the ML-induced challenges that arise in each of these dimensions and the opportunities unlocked by ML, elaborating on their influences on the business logic of organizations from the perspective of the respective dimension of the business model.
First, two studies address the dimension of ML-driven value creation. The creation of ML systems requires experts from various disciplines to collectively reflect on the organization’s existing knowledge (e.g., when making sense of data), which can lead to the creation of additional knowledge (e.g., through insights into inefficiencies in routines). Moreover, their data-based learning enables ML systems to generate knowledge in a way that complements the strengths of humans and thus to uniquely contribute to knowledge creation and revision in organizations. Existing literature on organizational learning hence regards productive ML systems as a new type of learner alongside humans. Yet, the potential for learning during ML development efforts, which include interactions of interdisciplinary groups of experts and (prototypical) ML systems, has to date remained largely unexplored. The first associated study therefore illuminates the beneficial learning processes that the creation of ML systems can stimulate. It also highlights the resulting human knowledge as a valuable additional by-product that can contribute to the knowledge base of the organization and thus to its long-term success. The second study examines a downside of the data-based learning approach of ML: the need for extensive experimentation during ML development. This runs counter to the demand of conventional business processes for efficiency and exploiting existing strengths, and organizations must allocate their limited resources between the two approaches, creating tensions during ML development that can take various forms in different structural approaches. Building on the theoretical foundation of organizational ambidexterity, the study identifies these tensions and corresponding tactics that organizations can employ to alleviate the tensions, depending on their manifestation, and facilitate ML-driven value creation.
Next, the dissertation discusses the second dimension, ML-driven value delivery. A particular problem in this context is that ML systems are often highly complex, making them and their outputs incomprehensible to humans. If they cannot use them due to a lack of understanding, customers of ML-driven business models may thus fail to benefit from the value the business model intends to deliver (i.e., the ML system or its outputs). Therefore, the literature on explainable AI contains approaches that can provide users of ML systems with explanations that disclose their inner workings and the reasoning behind their decisions. Yet, thus far, these approaches have lacked a focus on distinct user groups and their specific requirements of the system. Especially lay users have often been neglected in previous studies. However, fostering the lay users’ understanding is critical if they are to incorporate the output of ML systems in their decision-making to benefit from the products and services of ML-driven business models. Hence, the third study in this dissertation follows a design science research process and presents an approach to elaborate the requirements specific to the users of ML systems. On this basis, the study further derives design principles for designing ML systems that provide user-centric explanations and thereby enhance value delivery.
Finally, two more studies shed light on the third dimension of ML-driven value capture. In pursuit of their own goals, organizations must align all components of their business model to enable the capture of value, e.g., the reaping of profits from their business model in the long term. Only creating valuable solutions and supplying them to customers does not guarantee value capture for the organization, as the decade-long search of Twitter (now X) for a suitable way to profit from its unique offering and massive user base illustrates. With the current literature yielding little clarity on the nature of ML-driven business models, the fourth study in this dissertation aims to create a fundamental understanding of the business model components that organizations must align for successful value capture. Specifically, the resulting taxonomy offers insights into the components of ML-driven business models and is supplemented by archetypes that represent structural compositions of ML-driven business models commonly found in practice. Building on these findings, the fifth study investigates the question of how organizations seeking to profit from ML-driven business models can successfully realize them, which is under-researched in today’s scientific literature as well. Realizing business models is an inherently dynamic and iterative process. In the case of ML-driven business models, the particularities of ML systems further complicate the effort, due (for instance) to the additional uncertainty stemming from the experimental character of ML development. Therefore, the study shows that organizations must build dynamic capabilities to be able to successfully realize ML-driven business models in the long term. Moreover, the study develops microfoundations (e.g., practices or processes) that empower the creation of the necessary dynamic capabilities, consequently contributing to the understanding of how organizations can successfully capture value sustainably from their ML-driven business models.
The studies within this dissertation illustrate that organizations must consider the unique characteristics of ML when designing and implementing their ML-driven business models to achieve sustainable success. Specifically, they show that the effects of ML particularities, such as the need for extensive experimentation in ML development, can manifest themselves in all three dimensions of the business model and can be both inhibiting (e.g., through additional uncertainty in the realization of the business model) and value-adding (e.g., through stimulated learning processes). The studies further delineate how organizations can take these influences into account through appropriate responses. This dissertation thus represents an important step toward a holistic understanding of ML-driven business models, emphasizes the value of the business model perspective for investigating the influence of ML on the business logic of organizations, and yields contributions to strategic management, entrepreneurship, and information systems literature. Thereby, it provides fertile ground for future examinations of ML-driven value creation, value delivery, and value capture against the backdrop of the high-level technological and entrepreneurial dynamism in the field of ML.