
doi: 10.1561/0200000100
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.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 8 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
