Prof. Gianvito LANZOLLA, Cass Business School,
Prof. Antonio MESSENI PETRUZZELLI, Politecnico di Bari,
Prof. Umberto PANNIELLO, Politecnico di Bari,

Track's contacts:

Gianvito.Lanzolla.1[AT]city.ac.uk
antonio.messenipetruzzelli[AT]poliba.it
umberto.panniello[AT]poliba.it

Artificial intelligence (AI) is driving changes of business and organizational activities, as well as of the underlying processes and competencies (van der Meulen, 2018), thus attracting the interest from both scholars and practitioners due to its huge impact on processes, products, services, and business models (e.g., Bughin et al., 2017; Dean 2014).
AI refers to the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction. Among the various types of AI solutions, reactive machines and limited memory technologies can recognize objects and make predictions, with the latter using past experiences to inform future decisions (some of the decision-making functions in autonomous vehicles have been designed in this way), while self-awareness AI systems have a sense of self, consciousness and can understand their current state using this information to infer what others are feeling.

The effect of AI technologies is particularly relevant when referred to the business model unit of analysis and, in particular, on the development of new business models or on the changes introduced in existing ones. Recently, the literature seems to converge on defining business model as the “design or architecture of the value creation delivery, and capture mechanisms” of a firm (Teece, 2010). As long with refining the construct of business model and its theoretical and practical relevance (Lanzolla and Markides, 2017), both research and practice realized that business models are subject to innovation in response to changes in their competitive and industrial environment (Chesbrough, 2007; Lindgardt et al., 2009). Innovating a business model does not mean necessarily to introduce a new product, service or technology (Lindgardt et al., 2009), but rather it calls to innovate at least one of its elements, such as the value proposition or the revenue model, thus providing the firm with a new value source that can be used to create a sustainable competitive advantage (Zott and Amit, 2010). Technological change has often been associated with business model innovation and nowadays we have observed a variety of new business models patterns based on the exploitation of AI applications in different industries (e.g., IBM Watson is revolutionizing different sectors, offering novel business opportunities in healthcare, education, weather forecast, fashion, and tax preparation).

We aim at discussing about how AI systems are reshaping business models’ mechanisms, approaches and founding elements (such as organization, infrastructures, customers or value propositions). Specifically, questions include, but are not limited to:

- main managerial and organizational implications related to the adoption of AI in existing business models;
- risks and weaknesses of the adoption of AI in existing business models;
- types and archetypes of AI based business models;
- differences between AI based vs. traditional business models;
- boundary conditions enabling the adoption of AI solutions in existing business models;
- policy-based initiatives and AI based business models;
- the performance implications of adopting AI solutions in incumbents’ or new entrants’ business models;
- antecedents and consequences of the adoption of AI solutions in business models;
- characteristics of the AI solutions that mostly affect business models performance;
- governance mechanisms of business models using AI;
- resources and capabilities underlying the introduction and adoption of AI solutions in business models;
- emerging trade-offs going along with the adoption of AI solutions in business models.

References

Bughin J., McCarthy B., Chui M. 2017. A Survey of 3,000 Executives Reveals How Businesses Succeed with AI. Harvard Business Review, https://hbr.org/2017/08/a-survey-of-3000-executives-reveals-how-busines….
Chesbrough, H. 2007. Business model innovation: it's not just about technology anymore. Strategy & leadership 35(6): 12-17.
Dean J. 2014. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners. Jon Wiley and Sons, New Jersey, USA.
Lanzolla G., Markides C. 2017. Does the strategy field need the business model construct? Working paper (2nd round at Journal of Management Studies).
Lindgardt Z, Reeves M, Stalk G, Deimler M. 2009. Business Model Innovation: when the game gets tough change the game. The Boston Consulting Group, http://www.bcg.com/documents/file36456.pdf
Teece, D. J. 2010. Business models, business strategy and innovation. Long Range Planning 43: 172-194.
van der Meulen R. 2018. 5 Ways Data Science and Machine Learning Impact Business. Gartner https://www.gartner.com/smarterwithgartner/5-ways-data-science-and-mach….
Zott C, Amit R. 2010. Business model design: An activity system perspective. Long Range Planning, 43: 216–226.