This doctoral dissertation explores the strategic management of artificial intelligence as a general purpose technology and its value creation in the context of multiple industries. I study what makes AI-based value creation challenging from the management and organization perspective despite the high technological performance of AI. I analyze this through five sub-research questions, and by applying grounded theory.

Empirically, I turn to 34 AI solution developers from 18 different industries who have both technical and business understanding of using AI. The AI solution developers suit this study because of their skills, capabilities, and power position to shape the present and future through the combined use of machine learning (ML) and other AI related technologies that are already impacting our daily lives in and out of work context.

The extant literature on AI in premium outlets on general management and organizational studies can be typified into five AI use phases: 1) antecedents of AI use, 2) AI use, 3) (empirical) impacts of AI use, 4) expected (cumulative) impacts of AI, and 5) AI-related paradigm shift. The five sub-research questions of this doctoral dissertation explore the definition of AI and the use phases 1-4 by approaching AI as the subject of study. The fifth AI use phase is excluded from this study as it would require using AI also as the research method.

The main contributions of this doctoral thesis include giving an overview of AI in management and organization, and pre-theoretically identifying the technical and socially constructed decision-making criteria for AI investments, six AI use types, how empirical AI impacts have been measured, what temporal dimensions are expected to be impacted by AI, and what AI strategies organizations have already adopted to create AI-based value and overcome its productivity paradox.