TY - JOUR
T1 - Shifting ML Value Creation Mechanisms
T2 - A Process Model of ML Value Creation
AU - Shollo, Arisa
AU - Hopf, Konstantin
AU - Thiess, Tiemo
AU - Müller, Oliver
PY - 2022/9
Y1 - 2022/9
N2 - Advancements in artificial intelligence (AI) technologies are rapidly changing the competitive landscape. In the search for an appropriate strategic response, firms are currently engaging in a large variety of AI projects. However, recent studies suggest that many companies are falling short in creating tangible business value through AI. As the current scientific body of knowledge lacks empirically-grounded research studies for explaining this phenomenon, we conducted an exploratory interview study focusing on 56 applications of machine learning (ML) in 29 different companies. Through an inductive qualitative analysis, we uncover three broad types and five subtypes of ML value creation mechanisms, identify necessary but not sufficient conditions for successfully leveraging them, and observe that organizations, in their efforts to create value, dynamically shift from one ML value creation mechanism to another by reconfiguring their ML applications (i.e., the shifting practice). We synthesize these findings into a process model of ML value creation, which illustrates how organizations engage in (resource) orchestration by shifting between ML value creation mechanisms as their capabilities evolve and business conditions change. Our model provides an alternative explanation for the current high failure rate of ML projects.
AB - Advancements in artificial intelligence (AI) technologies are rapidly changing the competitive landscape. In the search for an appropriate strategic response, firms are currently engaging in a large variety of AI projects. However, recent studies suggest that many companies are falling short in creating tangible business value through AI. As the current scientific body of knowledge lacks empirically-grounded research studies for explaining this phenomenon, we conducted an exploratory interview study focusing on 56 applications of machine learning (ML) in 29 different companies. Through an inductive qualitative analysis, we uncover three broad types and five subtypes of ML value creation mechanisms, identify necessary but not sufficient conditions for successfully leveraging them, and observe that organizations, in their efforts to create value, dynamically shift from one ML value creation mechanism to another by reconfiguring their ML applications (i.e., the shifting practice). We synthesize these findings into a process model of ML value creation, which illustrates how organizations engage in (resource) orchestration by shifting between ML value creation mechanisms as their capabilities evolve and business conditions change. Our model provides an alternative explanation for the current high failure rate of ML projects.
KW - Artificial intelligence (AI)
KW - Machine learning (ML)
KW - Value creation machanisms
KW - Knowledge creation
KW - Augmentation
KW - Automation
KW - AI strategy
KW - Interview study
KW - Artificial intelligence (AI)
KW - Machine learning (ML)
KW - Value creation mechanisms
KW - Knowledge creation
KW - Augmentation
KW - Automation
KW - AI strategy
KW - Interview study
U2 - 10.1016/j.jsis.2022.101734
DO - 10.1016/j.jsis.2022.101734
M3 - Journal article
SN - 0963-8687
VL - 31
JO - Journal of Strategic Information Systems
JF - Journal of Strategic Information Systems
IS - 3
M1 - 101734
ER -