• Sena, V. and Nocker, M. (2021) AI and business models: the good, the bad and the ugly. Foundations and Trends® in Technology, Information and Operations Management, 14 (4). pp. 324-397. ISSN 1571-9545

    From Now Publishers website:

    Over the last five years, several scholars from a range of disciplines have started to analyze how Artificial Intelligence (AI) affects business outcomes. This research effort has produced many predictions on the expected impact of automation on labor demand and equilibrium employment. However, most of the expected results are dependent on how businesses change their behavior due to adopting AI. We argue that, as AI diffuses across the economy, changing behavior 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. 

  • Åstrom J, Wiebke R. and Parida V. (2022): Value creation and value capture for AI business model innovation: a three-phase process framework. Review of Managerial Science 16(2) January 2022.

    From the Researchgate website:

    The rise of AI technologies is generating novel opportunities for companies to create additional value for their customers by applying a proactive approach, managing uncertainty, and thus improving cost efficiency and increasing revenue. However, AI technology capabilities are not enough—companies need to understand how the technology can be commercialized through appropriate AI business model innovation. When emerging technologies are introduced, business-model concepts often need to be significantly altered. This is necessary to fully capitalize on disruptive technologies because it is just as important to innovate the business model as it is to build advanced technology solutions. Therefore, the purpose of this study is to explain how AI providers align value-creation and value-capture dimensions in order to develop commercially viable AI business models. To fulfill our stated purpose, this study has adopted an inductive and exploratory single case-study approach centered on a market-leading provider of AI-related services. The findings are consolidated into a process framework that explicitly illustrates the key activities that companies need to perform concerning value creation and value capture for AI business model innovation and commercialization. The framework explains that AI providers need to follow three phases—namely, identifying prerequisites for AI value creation, matching value capture mechanisms, and developing AI business model offer. We also find that AI providers need to test and develop multiple AI business models and operate them simultaneously to ensure commercial success.

  • Kanbach D., Heiduk L, Blüher G., Schreiter M. (2023). The GenAI is out of the bottle: generative artificial intelligence from a business model innovation perspective. Review of Managerial Science 18(3) September 2023.

    From Researchgate website:

    The introduction of ChatGPT in November 2022 by OpenAI has stimulated substantial discourse on the implementation of artificial intelligence (AI) in various domains such as academia, business, and society at large. Although AI has been utilized in numerous areas for several years, the emergence of generative AI (GAI) applications such as ChatGPT, Jasper, or DALL-E are considered a breakthrough for the acceleration of AI technology due to their ease of use, intuitive interface, and performance. With GAI, it is possible to create a variety of content such as texts, images, audio, code, and even videos. This creates a variety of implications for businesses requiring a deeper examination, including an influence on business model innovation (BMI). Therefore, this study provides a BMI perspective on GAI with two primary contributions: (1) The development of six comprehensive propositions outlining the impact of GAI on businesses, and (2) the discussion of three industry examples, specifically software engineering, healthcare, and financial services. This study employs a qualitative content analysis using a scoping review methodology, drawing from a wide-ranging sample of 513 data points. These include academic publications, company reports, and public information such as press releases, news articles, interviews, and podcasts. The study thus contributes to the growing academic discourse in management research concerning AI’s potential impact and offers practical insights into how to utilize this technology to develop new or improve existing business models.

  • Reim W, Åstrom J., Eriksson O. (2020): Implementation of Artificial Intelligence (AI): A Roadmap for Business Model Innovation. May 2020 AI 1(2):180-191

    From the Research Gate website:

    Technical advancements within the subject of artificial intelligence (AI) leads towards development of human-like machines, able to operate autonomously and mimic our cognitive behavior. The progress and interest among managers, academics and the public has created a hype among many industries, and many firms are investing heavily to capitalize on the technology through business model innovation. However, managers are left with little support from academia when aiming to implement AI in their firm’s operations, which leads to an increased risk of project failure and unwanted results. This paper aims to provide a deeper understanding of AI and how it can be used as a catalyst for business model innovation. Due to the increasing range and variety of the available published material, a literature review has been performed to gather current knowledge within AI business model innovation. The results are presented in a roadmap to guide the implementation of AI to firm’s operations. Our presented findings suggest four steps when implementing AI: (1) understand AI and organizational capabilities needed for digital transformation; (2) understand current BM, potential for BMI, and business ecosystem role; (3) develop and refine capabilities needed to implement AI; and (4) reach organizational acceptance and develop internal competencies.

  • Weber M., Beutter M., Weking J., Böhm M., Krcmar H. (2022) AI Startup Business Models Key Characteristics and Directions for Entrepreneurship Research. Research Paper, Volume 64, pages 91-109.

    From the Springer website:

    We currently observe the rapid emergence of startups that use Artificial Intelligence (AI) as part of their business model. While recent research suggests that AI startups employ novel or different business models, one could argue that AI technology has been used in business models for a long time already—questioning the novelty of those business models. Therefore, this study investigates how AI startup business models potentially differ from common IT-related business models. First, a business model taxonomy of AI startups is developed from a sample of 100 AI startups and four archetypal business model patterns are derived: AI-charged Product/Service Provider, AI Development Facilitator, Data Analytics Provider, and Deep Tech Researcher. Second, drawing on this descriptive analysis, three distinctive aspects of AI startup business models are discussed: (1) new value propositions through AI capabilities, (2) different roles of data for value creation, and (3) the impact of AI technology on the overall business logic. This study contributes to our fundamental understanding of AI startup business models by identifying their key characteristics, common instantiations, and distinctive aspects. Furthermore, this study proposes promising directions for future entrepreneurship research. For practice, the taxonomy and patterns serve as structured tools to support entrepreneurial action.

  • Aydin Ö., Karaarslan E. (2023): Is ChatGPT Leading Generative AI? What is Beyond Expectations? Academic Platform Journal of Engineering and Smart Systems 11(3), January 2023.

    From the Research Gate website:

    Generative AI has the potential to change the way we do things. The chatbot is one of the most popular implementation areas. Even though companies like Google and Meta had chatbots, ChatGPT became popular as it was made publicly available. Although ChatGPT is still in the early stages of its development, it attracted the attention of people and capital groups. It has taken the public interest; people from different fields, ages, and education levels started using ChatGPT. There have been many trials with ChatGPT. It is possible to see a lot of news and shares on the Internet. The study aims to shed light on what is happening in the literature and get an insight into the user expectations of ChatGPT and Generative AI. We also give information about the competitors of ChatGPT, such as Google’s Bard AI, Claude, Meta’s Wit.ai and Tencent’s HunyuanAide. We describe technical and structural fundamentals and try to shed light on who will win the race. We share the early stage due diligence and current situation analysis for all these points. We examine preprint papers and published articles. We also included striking posts on the LinkedIn platform and a compilation of various blogs and news. We also made use of ChatGPT in editing the content of these resources of this study. We can get an insight into the people's interests through their questions submitted to ChatGPT. We can also understand the capabilities of ChatGPT and also predict further enhancements.