The business value of Information Systems (IS) is one of the most prominent topics in IS research and the collective effort of scholars interested in this topic has offered a thorough understanding of the organisational effects of IS investments. However, the organisation-level view obscures how IS business value emerges from individual-level IS use. This analytical blind spot reduces the plurality of IS business values that users realise through IS use to organisation-level outcomes. We fill this blind spot by theorising about how IS business value is realised through individual-level IS use. Building on the findings of a case study, we theorise the relationships between users’ valueception of IS, their IS use and the realisation of IS business value through this use. Our theorising advances knowledge on IS business value realisation at the individual level and shows that individual-level IS use realises more than one IS business value. This insight into the plurality of IS business value opens the door for further value-reflexive investigations. Our theorising offers analytical vocabulary that future studies can utilise to examine and explain phenomena of value realisation through IS use.

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.