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Session Overview
19-PM2-07: ST1.1 - How Artificial Intelligence is Reshaping Business Models
Wednesday, 19/June/2019:
2:45pm - 3:45pm

Session Chair: Gianvito Lanzolla, Cass Business School
Session Chair: Umberto Panniello, Politecnico di Bari
Location: Amphi Curie

Session Abstract

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.

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Using service blueprints to explore changes in user experience and corporate investment induced by AI

Caroline Nowacki, Angéla Martin

Altran, France


Technological companies that revolutionized whole industries also had strong business models (BM) (Johnson, 2008). Using AI will also need to serve a clear customer value proposition, and a well-suited “architecture of the value creation delivery, and capture mechanisms” (Teece, 2010), but AI's impact on BM still needs to be characterized.


Contrary to traditional services, AI-based services need to collect and analyze large amounts of data to bring forth their added value. They also often present a different interface to users, using voice, text or robots (Rzepka & Berger, 2018). Both can lead to failures as they create fears among users. Indeed, data privacy is a top concern, as is the potential dehumanization of services (BCG-Ipsos survey, 2018).

The Service Blueprint is a tool that shows these two aspects of resources and interactions with the customer trough time, and is commonly used by designers to ensure the user needs are met by the service (Norman, 2013; Farrel, 2017). It has been used to analyze the changes brought by AI in specific services in healthcare, an academic library, and restaurants, but without analyzing the resulting impact on services' business models (Lin et al. 2017, Gasparini et al. 2018, Lai et Tsai 2018).

Literature Gap

AI is pushed by technological capabilities rather than customers' needs. However, such a strategy still requires to find a fit with customers’ need (Osterwalder et al., 2014). Understanding the difference between traditional and AI-based business models requires an understanding of changes in both resources and interactions with customers.

Research Questions

How does the service blueprint of “taking a taxi” change as more AI is incorporated and what is the impact on the user experience and company's business model?


Our goal is to systematically analyze the differences between traditional and AI-based business models. To do so, we propose to analyze the taxi / private hire driver service. Indeed, the success of new AI-based business models (and the similarity of the business models of companies in that space), as well as the availability of business models for the next improvement in the industry brought by AI: the autonomous vehicle makes it an exemplary case.

Empirical Material

Using interviews and creativity workshops with designers and engineers working in the automobile industry, we build the service blueprint for three business models for the service of “taking a taxi”. The first business model corresponds to the traditional service of taking a taxi without any help from AI or a digital platform. The second business model corresponds to the current state of using AI-based apps that propose to transport passengers in a car not driven by the user of the service. The third business model is prospective and corresponds to the autonomous vehicle. Based on these three service blueprints built with experts, we systematically analyze the differences in the customer experience, the frontline and background processes of the organization proposing the service to identify the main changes in resources, processes and relationships with the customer that AI brings to traditional business models for a given service.


Based on preliminary results, we observe that AI-based services see the number of touchpoints with the user decrease. The frequency of user decision-making and direct interactions between the company and the user also decrease. While this decrease leads to increased ease of use, fluidity, speed and performance, it also comes with less control from the user and might decrease the level of acceptance and trust the user gives to the service. We tentatively conclude that in AI-based services, user acceptance needs to be a key element of the assessment of the value proposition, beyond the assessment of user needs.

Contribution to Scholarship

This study shows how the value proposition part of AI-based business models needs to go beyond market needs and spend more time on the fit between specific users and the solution to uncover emotional reaction that could be contradictory to objective needs. We also show how the service blueprint can be insightful to analyze parts of companies’ business models.

Contribution to Practice

For practitioners, this means spending more time early in the development of the service testing what increasing the user’s trust and level of acceptance in the service, as well as plan for continuous adaptation to adapt design choices as users get used to using more and more services using AI.


By studying the change in service delivery and business models of AI-based taxi services, we believe we’ll help bridge research, industry and society.


Anthes, G. (2017) "Artificial Intelligence Poised to Ride a New Wave," Communications of the ACM (60:7), pp. 19-21.

BCG Gamma & Ipsos (2018) "Artificial Intelligence : Have No Fear, The revolution of AI at work, available" at:

Christensen, Raynor, McDonald (2015) "What is disruptive innovation? " Harvard Business Review

Farrell, S. 2017. “UX Research Cheat Sheet”, available at:

Gasparini, A. A., Mohammed, A. A., & Oropallo, G. (2018, July). Service design for artificial intelligence. In ServDes2018. Service Design Proof of Concept, Proceedings of the ServDes. 2018 Conference, 18-20 June, Milano, Italy (No. 150, pp. 1064-1073). Linköping University Electronic Press

Johnson, M., Christensen, C., Kagermann, H. (2008) "Reinventing Your Business Model" Harvard Business Review,

Lai, C. J., & Tsai, C. P. (2018, March). Design of Introducing Service Robot into Catering Services. In Proceedings of the 2018 International Conference on Service Robotics Technologies (pp. 62-66). ACM

Lin, S. Y., Shanafelt, T. D., & Asch, S. M. (2018, May). Reimagining Clinical Documentation With Artificial Intelligence. In Mayo Clinic Proceedings (Vol. 93, No. 5, pp. 563-565). Elsevier

Norman, Don. (2013) “The Design of Everyday Things”. Basic Books: 217–219

Osterwalder, A. ; Pigneur, Y. ; Bernarda, G. ; Smith, A. (2014). Value Proposition Design: How to Create Products and Services Customers Want. John Wiley & Sons

Russell, S. J., and Norvig, P. (2010) Artificial Intelligence: A Modern Approach. Upper Saddle River, New Jersey: Pearson.

Rzepka, C., Berger, B. (2018) "User Interaction with AI-enabled Systems : A Systematic Review of IS Research", Thirty Ninth International Conference on Information Systems, San Francisco.

Shostack, G. L. (1984) “Designing Services That Deliver” Harvard Business Review.

Zott, C., & Amit, R. H. (2008) The Fit Between Product Market Strategy and Business Model: Implications for Firm Performance. Strategic Management Journal, 29 (1), 1-26.

AI in the Space Industry. Business Model Innovation and its antecedents.

Loretta Latronico, Luisa Pellegrini, Luca Fanucci

UniPisa, Italy


Artificial Intelligence (AI) systems are expected to be an integral part of leading aerospace and defence companies in just the next few years. The rate of AI adoption is in fact exponentially growing thanks to the need of efficiently processing the considerable amount of data provided by the Copernicus Earth Observation satellites.


Building on the fact that a Business Model (BM) – whatever is its theoretical conceptualisation - is to be interpreted as a system (Simon, 1962) made up of different components [recurring components in the main BM frameworks proposed in the literature are: value proposition, customer segments, infrastructure - resources / activities / partners required for realising the value proposition - and the financial structure (Osterwalder and Pigneur, 2010)], innovation of the BM can be carried out at the architectural or at the modular level (Henderson and Clark, 1990; Demil and Lecocq, 2010; Morris et al., 2005). Innovation is modular if it mainly involves changes in one/more components, without relevant changes in the way components interact. Innovation is architectural if it regards the general structure or the interactions between the above components.

Literature Gap

According to Saebi and Foss (2017), up to now the extant scientific literature has scarcely investigated the antecedents of BM innovations (but see de Reuver et al., 2009) and has barely connected antecedents to the type of innovation of the BM, if modular or architectural.

Research Questions

Can specific antecedents be associated with modular and architectural BM innovation?

In case of modular BM innovation, in which circumstances a specific antecedent is associated with innovation in a specific BM component?

Are there similar patterns in the ramifications from the epicentre(s) towards the other parts of the BM?


The research methodology adopted in this paper is multiple case studies. Specifically, we selected five case studies of firms operating in the Space Industry, which engaged in changing their BM with the aim of tapping into the opportunities offered by AI technologies.

The multiple case studies methodology allowed us to control the variation of the BM component and BM architectural innovations.

In carrying out the five case studies, we followed several steps: definition of the research questions, case selection, identification of the unit of analysis, definition of the reference research framework, data collection, data elaboration, and data analysis.

Empirical Material

The case studies were conducted by means of Explorative questionnaire and Interviews that will enable a structured and repeatable data analysis.


Very preliminary findings show that artificial intelligence can ignite both architectural and modular innovation (RQ1), with examples of epicentres in each of the four components of the business model. However, epicentres usually enlarge their innovation effect to other components of the BM, so spreading changes in the entire model.

Contribution to Scholarship

The main aim of this paper is to contribute on the relevance of artificial intelligence as an external antecedent to different kinds of BM innovation, distinguished according to their modular or architectural level. Therefore, this paper, although retrospective and inductive, is a first step, which enforces the up to now scant literature that has empirically tested the effect of different antecedents on the propensity to innovate the BM.

Contribution to Practice

This study will enable emerging companies as well as national and international public institutions (e.g. European Space Agency, National Space Agencies and European Commission) to tune their business model in order to fully exploit the opportunities offered by the new emerging technologies as indeed Artificial Intelligence.


Applications of AI in the space industry are intended for the ultimate benefit of European society. The goal of this study is to provide recommendations to industry and research institution so that their business model can be adapted to exploit the opportunities offered by such innovative technologies.


Barczak, G., (2015) “From the Editor. Publishing Qualitative versus Quantitative Research”, Journal of Product Innovation Management, 32(5), 658-659.

De Reuver, M., Bouwman, H., and MacInnes, I., (2009) “Business Models Dynamics for Start-Ups and Innovating E-Businesses”, International Journal of Electronic Business, 7, 269-286.

Demil, B. and Lecocq, X., (2010) “Business Model Evolution: In Search of Dynamic Consistency. Long Range Planning, 43, 227–246.

Foss, N.J. and Saebi, T., (2017) “Fifteen Years of Research on Business Model Innovation: How Far Have We Come, and Where Should We Go?”, Journal of Management, 43(1) 1, 200–227.

Henderson, R. and Clark, K.B., (1990) “Architectural Innovation: The Reconfiguration of Existing Product Technologies and the Failure of Established Firms”, Administrative Science Quarterly.

Morris, M., Schindehutte, M. and Allen, J., (2005) “The Entrepreneur’s Business Model: Toward a Unified Perspective”, Journal of Business Research, 58, 726–735.

Osterwalder, A. and Pigneur, Y., (2010) Business Model Generation, John Wiley & Sons, Inc., Hoboken, New Jersey.

Simon, H.A., (1962) “The Architecture of Complexity”, Proceedings of the American Philosophical Society, 106, 467-482.

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