As Much Art as Science - Examining the Realization of Business Models Driven by Machine Learning Through a Dynamic Capabilities Perspective

Aug 6, 2026

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Machine learning (ML) technologies enable major entrepreneurial opportunities, leading to new business models where ML sits at the core. Because ML differs from other digital technologies in both organizational characteristics and effects, the realization process and success drivers remain insufficiently understood. Using a qualitative, cross-industry study with 20 expert interviews, the paper explains ML-specific complications and identifies underlying reasons for successful realization. A dynamic capabilities perspective is applied to conceptualize eleven microfoundations that clarify how firms build, implement, and transform ML-driven business models.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages19

Reading Time48 min

AccessFree to download

Introduction

Research gap and motivation

Dynamic capabilities perspective

Methodology

Qualitative study and expert interviews

Conceptualization

ML-specific complications

Eleven microfoundations

Association for Information Systems AIS Electronic Library (AISeL) | ECIS 2023 Research Papers | ECIS 2023 Proceedings | | --- | --- | | 5-11-2023
As Much Art as Science-Examining the Realization of Business Models Driven by Machine Learning Through a Dynamic Capabilities Perspective
Oliver A. Vetter
Technical University of Darmstadt, [oliver.vetter@tu-darmstadt.de](oliver.vetter@tu-darmstadt.de)
Maren F. Mehler
Technical University of Darmstadt, [maren.mehler@tu-darmstadt.de](maren.mehler@tu-darmstadt.de)
Peter Buxmann
Technical University of Darmstadt, [buxmann@is.tu-darmstadt.de](buxmann@is.tu-darmstadt.de)
Follow this and additional works at: [https://aisel.aisnet.org/ecis2023_rp](https://aisel.aisnet.org/ecis2023_rp) | | Recommended Citation Vetter, Oliver A.; Mehler, Maren F.; and Buxmann, Peter, "As Much Art as Science-Examining the Realization of Business Models Driven by Machine Learning Through a Dynamic Capabilities Perspective"(2023) . ECIS 2023 Research Papers. 241. [https://aisel.aisnet.org/ecis2023_rp/241](https://aisel.aisnet.org/ecis2023_rp/241) This material is brought to you by the ECIS 2023 Proceedings at AIS Electronic Library (AISeL) . It has been accepted for inclusion in ECIS 2023 Research Papers by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact](contact elibrary@aisnet.org)[ elibrary@aisnet.org](contact elibrary@aisnet.org). AS MUCH ART AS SCIENCE – EXAMINING THE REALIZATION OF BUSINESS MODELS DRIVEN BY MACHINE LEARNING THROUGH A DYNAMIC CAPABILITIES PERSPECTIVE Research Paper Oliver A. Vetter, Technical University of Darmstadt, Germany, [vetter@is.tu-darmstadt.de](vetter@is.tu-darmstadt.de)[ ](vetter@is.tu-darmstadt.de)Maren F. Mehler, Technical University of Darmstadt, Germany, [mehler@is.tu-darmstadt.de](mehler@is.tu-darmstadt.de)[ ](mehler@is.tu-darmstadt.de)Peter Buxmann, Technical University of Darmstadt, Germany, [buxmann@is.tu-darmstadt.de](buxmann@is.tu-darmstadt.de) Abstract Machine learning (ML) technologies open up enormous potential to be unlocked through entrepreneurial activities in organizations, causing countless novel business models with ML at their core to emerge in the market. As ML technologies differ significantly from other digital technologies both in their characteristics and their effect on organizations, little is currently known about the complexities of the realization process for business models driven by ML and why only some organizations execute it successfully. By building on a qualitative study grounded on cross-industry insights from 20 expert interviews, this paper contributes to a greater understanding of the realization process by identifying ML-specific complications, before aiming to determine the underlying reasons for successful business model realization. We adopt a dynamic capabilities perspective and conceptualize eleven microfoundations that explicate how organizations build, implement, and transform business models driven by ML. Keywords: Machine Learning, Business Model Realization, Dynamic Capabilities. 1 Introduction Machine learning (ML) unlocks possibilities to support or entirely automate processes within organizations (Jordan and Mitchell, 2015) and further provides powerful opportunities for entrepreneurship by enabling entirely new services and business models (Chalmers et al., 2021; Davenport et al., 2020) . ML denotes a technology that can be utilized to create instances of artificial intelligence (AI) by allowing algorithms to learn patterns hidden within data and then make predictions for new data (Russell and Norvig, 2021; Brynjolfsson and Mitchell, 2017; Mitchell, 1997) . The novel business models with ML at their core are distinct from other types of business models enabled by information technologies (IT), as recent literature shows that ML not only opens up new possibilities for value proposition, but potentially impacts the overal

FAQs

What does the paper focus on regarding ML-driven business models?

It examines how organizations realize business models driven by machine learning and why some succeed while others struggle, going beyond ideation.

How does the study support its understanding of the realization process?

The paper relies on a qualitative study based on cross-industry insights from 20 expert interviews.

What analytical lens is used to explain successful realization?

The paper uses a dynamic capabilities perspective and develops eleven microfoundations describing building, implementing, and transforming ML-driven business models.

Aug 6, 2026

Upload by Finn