Public defence in Industrial Engineering and Management, M.Sc. Tomasz Mucha

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

Doctoral student: Tomasz Mucha
Opponent: Prof. Nicholas Berente, University of Notre Dame, Indiana, USA
Custos: Prof. Robin Gustafsson, Aalto University School of Science, Department of Industrial Engineering and Management

From Pilot Projects to Business Advantage: Doctoral Thesis Offers Insights into How Firms Develop and Leverage Machine Learning Technologies.

A new doctoral thesis by Tomasz Mucha investigates the gap between the growing popularity of machine learning (ML) technologies and the difficulty businesses face in transforming ML projects into lasting organizational capabilities. Titled "Machine learning in organizations: The processes of diffusion, capability development, and reframing," the research explores the unique challenges of developing and maintaining ML-based systems. 

While ML adoption is surging, the study highlights the critical difference between initial ML projects and building long-term organizational capabilities. Unlike traditional IT systems with pre-programmed rules, ML systems rely on learning from data. This difference requires distinct approaches to development and maintenance of ML systems. 

The thesis tackles this challenge through three essays. The first examines the extent of ML use in large organizations. The second dives into the internal processes that hinder or promote successful development of ML-based organizational capabilities. Finally, the third essay explores how existing ML systems can be adapted to function in new environments. 

The thesis emphasizes the need for a holistic approach to developing and maintaining ML-based capabilities, going beyond initial implementation efforts. These insights can be applied by companies of all sizes to bridge the gap between pilot projects and lasting business value. By understanding the challenges and opportunities surrounding ML development, organizations can make informed decisions about adopting and utilizing ML technologies for long-term success. This research paves the way for further exploration of ML's contribution to business performance and digitalization of firms.

Key words: artificial intelligence, machine learning, organizational capabilities, technology diffusion

Thesis available for public display 10 days prior to the defence at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/ 

Contact information: 

Doctoral theses in the School of Science: https://aaltodoc.aalto.fi/handle/123456789/52