Organizational Readiness for Machine Learning

Organizational Readiness for Machine Learning: Exploring the key readiness factors for business adoption of machine learning

Yoon, Ahhyun (TU Delft Technology, Policy and Management)

Janssen, M.F.W.H.A. (mentor)
Scholten, V.E. (graduation committee)

Delft University of Technology

Management of Technology (MoT)

2019-08-26

Demand for machine learning is ever-growing in today’s business. Situated at the convergence point of big data and Artificial Intelligence (AI), machine learning allows companies not only to unlock hidden insights from the data deluge but also to fundamentally revolutionize their products and services. Recognizing the opportunities, industrial players are on the lookout to partake in the machine learning journey, or some are already experimenting with machine learning. However, the disparity between expectation and action is still substantial, and subsequently, machine learning adoption remains elusive for many companies. This is partly due to the relative immaturity of the technology, but also due to a myriad of uncertainties conjoined with the adoption process. As of now, a lack of understanding of machine learning adoption in business is prevalent in both academia and practice impeding companies from creating values at scale. In brief, the way to best prepare for machine learning is still an unsolved question. In this regard, it is timely to reflect such contemporary managerial needs into academic research. With the research main question of “What are the key readiness factors for business adoption of machine learning?”, this study investigates the factors which can increase companies’ overall readiness towards machine learning. This research utilized three research strategies (i) literature review, (ii) expert interviews, and (iii) multiple case studies to answer the main research question. The main research outcomes of this research are threefold. Firstly, the research concept of organizational readiness for technology adoption is clarified and two distinctive research streams – users’ and exploiters’ readiness – are subsequently identified. Secondly, the barriers to business adoption of machine learning are consolidated. Based on this, the key readiness factors which can mitigate the barriers are identified and empirically tested. Thereby the model of machine learning readiness is developed with its constituting factors: (i) Top management support, (ii) Vision and strategy, (iii) Open culture, (iv) Multi-disciplinary team, (v) Data governance, (vi) Existence of a translator, (vii) Machine learning infrastructures, (viii) Ambidexterity, (ix) Strategic partnership, and (x) Awareness. Theses outcomes are valuable to both academia and practice. This study contributes to academia by clarifying the ambiguous theoretical concept of organizational readiness for technology adoption. For industry, this paper can be used as a white paper to understand the phenomenon of business adoption of machine learning.

Artificial intelligence
Machine learning
Machine learning readiness
Organizational readiness for technology adoption
Organizational technology adoption

http://resolver.tudelft.nl/uuid:f32458de-b842-46c2-b92d-508143e844c2

Student theses

master thesis

© 2019 Ahhyun Yoon