Firms’ use of predictive artificial intelligence for economic value creation and appropriation

Firms are increasingly investing in the use of artificial intelligence (AI). Some succeed in creating and appropriating substantial economic value, but many fail. There is no consensus as to how a firm should use AI to create and appropriate economic value. This paper provides an answer to that question. A novel research model is advanced based on the notion of data network effects being realized within a firm’s business model. This research model is tested in a unique and natural industrial setting of two competing firms that simultaneously adopt the use of similar predictive AI. This setting is researched with two distinct empirical studies that employ mixed-methods research. The results shows that one firm fails to convert its AI use into economic value creation and appropriation while the other succeeds. Value is created and appropriated by ensuring that AI users perceive high user value that realize data network effects while being located in the firm’s business model architecture so as to activate business value drivers. These findings confirm the here proposed research model and offer novel theoretical contributions and specific managerial implications. • Only some firms successfully use AI for economic value creation and appropriation. • AI must create perceived value for users to add value creation and appropriation. • We move towards a multi-theoretical perspective on the study of AI-driven BM. • AI technology activates two business model themes: novelty and efficiency. • A Business Model with AI activates DNE and some BMTs, boosting economic performance.