Over the last five years, several scholars from a range of disciplines have started to analyse how Artificial Intelligence (AI) affects businesses outcomes. This research effort has produced many predictions on the expected impact of automation on labour demand and equilibrium employment. However, most of the expected results are dependent on how businesses change their behaviour due to adopting AI. We argue that, as AI diffuses across the economy, changing behaviour is a necessary outcome for incumbents: the argument is that the diffusion of AI across an industry generates the conditions for a process of value migration from incumbents to new entrants (Helper et al., 2018); in these cases, the only mechanism available to incumbents to offset the negative impact of the migration process is by changing the architecture of their business, i.e., the business model. However, companies can choose from several AI-driven business models; their preference for one model is driven by many industry-level factors such as technical standards, the structure of the technology industry and the presence of an ethical framework for the use of AI. This monologue summarises the existing literature on business model innovation and AI; it then analyses the industry-level factors that may shape the business-level preference for specific business models. Finally, the monologue offers some suggestions for future research in the area.
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3 May 2021
Research Article|
May 03 2021
AI and Business Models: The Good, The Bad and The Ugly
Manuela Nocker
Manuela Nocker
Essex Business School,
University of Essex
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Online ISSN: 1571-9553
Print ISSN: 1571-9545
© 2021 Vania Sena and Manuela Nocker
2021
Vania Sena and Manuela Nocker
Licensed re-use rights only
Foundations and Trends in Technology, Information and Operations Management (2021) 14 (4): 324–397.
Citation
Sena V, Nocker M (2021), "AI and Business Models: The Good, The Bad and The Ugly". Foundations and Trends in Technology, Information and Operations Management, Vol. 14 No. 4 pp. 324–397, doi: https://doi.org/10.1561/0200000100
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