Revising Business Model Innovation: Towards a value process framework for AI-based Offerings
Time: Fri 2022-06-10 13.00
Location: E3, Osquars backe 14, Stockholm
Video link: https://kth-se.zoom.us/j/65238678514
Subject area: Machine Design
Doctoral student: Girish Agarwal , Integrerad produktutveckling, Piab
Opponent: Professor Mariano Corso, Politecnico di Milano, Italien
Supervisor: Professor Mats Magnusson, Integrerad produktutveckling
Advances over the last few decades in digital technologies in general and artificial intelligence (AI) technology in particular have transformed many industries. There are many successful AI use cases in industry. However, the adoption rate of AI technology by incumbent traditional industrial manufacturing firms in their offerings remains far too low compared with the big claims made about the contribution of AI to the world economy. Incumbents’ current view of AI as merely a technology resource with which to increase automation and efficiency is far too narrow and needs to be changed. Instead, AI can be a dynamic capability giving competitive advantage to incumbents if they explore AI’s value implications in their business models (BMs). Furthermore, current value discussions both generally and within BMs are too individualistic, transactional, and operational and lack the process orientation required for a more comprehensive understanding of the value potential of AI, leading to business model innovation (BMI) for incumbents.
With the overall ambition to support AI incorporation into incumbents’ offerings, this thesis proposes a process-based value framework for AI-driven BMs. For this purpose, this thesis research has produced five studies, including various methods, to understand the value processes within BMs in light of digitalization. Owing to the complex nature of the phenomenon under study, the methods used in the studies include quasi-experiments, case studies, semi-structured interviews, in-depth interviews, card sorting, longitudinal research, quantitative survey analysis, literature review, and literature mapping as required and relevant for the different studies.
The studies highlight that digital and AI technologies could potentially create new values (e.g., self-learning and intelligent offerings) for different stakeholders, provide new mechanisms for value delivery through digital servitization, and enable previously impossible value-capture techniques such as value-based dynamic pricing within BMs. It can be observed that value in digital BMI is constantly changing and hence needs to be focused on explicitly within BMs and introduced as a value-identification process. Furthermore, AI entails new value process relationships in which value creation and delivery are much more integrated, dynamic, and personalized per customer, highlighting the required emphasis on hyper-personalization.
This thesis analyzes the challenges and opportunities AI has provided within BMI in order to propose a modified value process framework for AI-enabled BMs, including value identification, value manifestation, and value capture, compared with the commonly proposed BM value processes of value creation, value delivery, and value capture. The proposed view consolidates value processes, including the individual, relational, and transactional values required by AI-based BMs, rather than just the transactional view of value covered through standard BM value processes, a view that highlights only the operational aspect of value within BMs.
Furthermore, this thesis discusses how the current approach to AI within BMI is more from a resource perspective and therefore cannot realize the full potential of AI technology. The thesis elaborates on how incumbents can utilize AI technology within BMI to create a competitive advantage by concentrating on the process view of value through the proposed new framework for handling highlighted opportunities and challenges. The new role of ecosystem stakeholders as innovation partners within BMI utilizing data/AI-driven capabilities and organizational value changes is discussed. Finally, this thesis highlights implications for BMI theory in terms of new value processes and implications for practice in terms of the BMI framework, concluding by presenting challenges and opportunities arising from the usage of AI within BMI by incumbents.