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).
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.
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.
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.
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