AI-enabled business-model innovation and transformation in industrial ecosystems: A framework, model and outline for further research
By Thommie Burström, Vinit Parida, Tom Lahti, Joakim Wincent. 2021
318 citations recorded by the source corpus.
Abstract Manufacturing incumbents find it difficult to integrate AI in their traditional business models. This paper draws on the research question: How does manufacturing incumbents use AI for enabling business model innovation in industrial ecosystems? We use qualitative method in order to study four large global manufacturing incumbents that are transforming their business models with AI. We performed more than 30 semi-structured in-depth interviews with strategic key personnel in order to understand how they have succeeded with implementing AI and transforming business models. Our main contribution establishes the need for AI business-model innovation to be aligned with ecosystem innovation. Specifically, in short-term incumbents may use ecosystem reconfiguration strategy, whereas long-term strategies relate to ecosystem revitalization, and resilience. Thus, we contribute by connecting organizational microelements with ecosystem macro dimensions and provide an evolutionary model envisioning how incumbents attempt to promote strategic transitions in their firms and ecosystems.
Subfields: Management Science and Operations Research, Marketing, Strategy and Management
View Paper: https://www.semanticscholar.org/paper/d5edef8b2593ec3685d39904771d1cd72da4aee3
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Citations in the corpus
- Generative artificial intelligence (2023, methodology): “Prevalent collaborations between humans and intelligent systems in businesses and private life have been shaped by the adoption of AI in various ways, especially in the field of process optimization and decision-making (Brynjolfsson & McAfee, 2016; Burström et al., 2021; Moussawi et al., 2021).”