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F​‌‍‍‍‌‍‌‍‍‍‌‌‌‍‌‌‌‌‌‌​or this literature review chapter, I would like to put em

F​‌‍‍‍‌‍‌‍‍‍‌‌‌‍‌‌‌‌‌‌​or this literature review chapter, I would like to put emphasis on the theoretical foundations and state of research concerning business models, business model innovation and the innovation of artificial intelligence business models. This should show how value is created/defined and how business models can aid in the decision making/strategy of the company. It should also emphasise on the value created via co-creation with stakeholders in the innovation process. For the innovation phases, we will try to find out if less attention has been placed to the BMI process phases Prototyping, Decision-making as suggested in the “Business Model Innovation Processes: A Systematic Literature Review” Bernd W. Wirtz 1 and Peter Daiser.
As part of the background, I can send the following introduction summary, which can be used as basis with the focus on AI business model innovation.
When it comes to AI, the realization of value and its implementation is not easy as it is domain-specific and different business requirements and knowledge are needed (Lee et al., 2019). Most business models consider more generic factors and fail to consider industry-specific aspects (Veit et al., 2014, p. 46). The different advances in AI technology and data analytics can create opportunities and challenge delivery systems, so leaders experience challenges while staying open and encouraging innovations that can shift the company’s Business Model (BM) (Lee et al., 2019, p. 9). In the conceptualization of value proposition and the role of BMI is important to complement different forms of innovation and value logic for the business (Fielt, 2012), which needs to be considered given the domain-specific nature of AI application. Teece (2010) s​‌‍‍‍‌‍‌‍‍‍‌‌‌‍‌‌‌‌‌‌​tates that when more radical and challenging innovations and revenue architectures are available, the greater the changes likely to be required to traditional business models (p.186). According to Chesbrough (2007) & Chesbrough et al., (2018), real tensions exist between aspects of a BM that create and helps to capture value. Therefore, it suggests that there should be a balance between the high-valued technology that enables profit earnings and the alignment between value creation and value capture processes in inter-organizational relationships. In this case, both economic and technological developments require a more customer-centric approach in the value creation and capture (Teece, 2010), for AI business models (AIBM), the mutual understanding can be achieved for example via an agile customer co-creation approach of BMI (Sjödin et al., 2021; Sjödin et al., 2020) that involves customers in the explorative agile design to identify different stakeholder needs (Bianchi et al., 2020; Kahkonen, 2004; Paluch et al., 2020). Furthermore, process steps across different approaches to innovate business models vary concerning the number of BMI steps (Foss & Saebi, 2017; Parida et al., 2019; Reim et al., 2020; Wirtz & Daiser, 2018) as well as the core elements of AIBMs (Ignatyeva et al., 2019; Lu, 2020; Metelskaia et al., 2018), different roadmaps exist for AI business models innovation (AIBMI) (Mishra & Tripathi, 2021; Reim et al., 2020). The scientific BMI knowledge has developed largely in silos (Zott et al., 2011), and is dispersed across various fields (Schneider & Spieth, 2013), which makes it even harder to create common knowledge and understanding when innovating AI business models, for exa​‌‍‍‍‌‍‌‍‍‍‌‌‌‍‌‌‌‌‌‌​mple.

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