Machine learning in organizations: The processes of diffusion, capability development, and reframing

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School of Science | Doctoral thesis (article-based) | Defence date: 2024-04-29
Degree programme
68 + app. 118
Aalto University publication series DOCTORAL THESES, 76/2024
The commercial diffusion of machine learning (ML) enables the development of novel and previously unattainable organizational capabilities. Over the past ten years, the rapid advances in ML algorithms, hardware, and tooling, increasing availability of data and computing resources, as well as highly publicized implementations of ML by tech giants and other firms have triggered and propelled further the diffusion of ML use in organizations globally. However, despite the relative ease of piloting ML projects, scaling and turning them into ML-based capabilities have proven to be challenging to the majority of organizations. The underlying reasoning behind this difficulty is the fundamentally different nature of ML development and updating. Unlike traditional information technology (IT), ML systems do not require explicit codification of task execution rules and their encoding by the developers into the inferential logic of the system. Instead, ML systems learn from data. This means that the existing approaches to traditional IT development are not enough for organizations to successfully build and keep up to date their ML-based capabilities. Motivated by these challenges and the practical relevance of the problem, the overarching objective of this thesis is to explore how organizations can successfully develop and use ML-based capabilities by uncovering the underlying processes and how they unfold over time. The initial development of such capabilities starts with the organizational adoption of the novel technology. Therefore, to scope out the status of ML technology commercial diffusion, Essay 1 of this thesis explores the extent of ML use by large firms and how it has changed over time. Essay 2 concentrates on the process of ML-based capability development in individual organizations. It uncovers the mechanisms inhibiting and promoting the successful development of organizational capabilities based on ML. Finally, Essay 3 concentrates on the organizational processes required for an ML system to function in novel operating environments or application domains. The third essay, thus, unpacks the process of reframing an existing operational ML system. This thesis contributes both theoretically novel and practically relevant insights into ML diffusion, development, and use by organizations. The promise of the transformative impact of ML – technologies which can learn from data and do not require explicit encoding of rules by humans – can be realized only if we advance our understanding of how organizations can productively harness these technologies. To this end, the thesis extends existing research by assuming an engaged scholarship approach and conducting in-depth longitudinal studies. The insights offered expand the understanding of organizational processes needed for the cultivation of ML-based capabilities beyond their initial development and implementation.
Supervising professor
Gustafsson, Robin, Asst. Prof., Aalto University, Department of Industrial Engineering and Management, Finland
Thesis advisor
Seppälä, Timo, University Lecturer, Aalto University, Department of Industrial Engineering and Management, Finland
artificial intelligence, machine learning, organizational capabilities
Other note
  • [Publication 1]: Mucha, Tomasz & Seppälä, Timo. 2022. AI Diffusion Monitoring among S&P500 Companies: Empirical Results and Methodological Advancements. DIGIT 2022 Proceedings.
  • [Publication 2]: Mucha, Tomasz; Ma, Sijia & Abhari, Kaveh. 2023. Riding a bicycle while building its wheels: The process of machine learning-based capability development and IT-business alignment practices. Internet Research, 33(7), 168–205.
    DOI: 10.1108/INTR-10-2022-0769 View at publisher
  • [Publication 3]: Mucha, Tomasz; Seppälä, Jane; Gustafsson, Robin & Lyytinen, Kalle. Reframing Machine Learning Systems through Data Work: A Case Study of a Medical Transcription Company. Working paper