The Transition to Autonomous – Impact & Challenges in the Race toward Self-Driving Cars

Abstract

Background and Problem Artificial Intelligence (AI) and specific Machine Learning (ML) are on the verge of gaining traction and significance within every industry. Learning Machines will lead to Automated Vehicles (AV), able to take judgement and decisions and ultimately steer themselves. This development will be the greatest disruption of the car automotive industry in the past hundred years. Organizations are forced to adapt to such a radical change in order to stay competitive and fulfill their customer needs. Purpose This dissertation examines the effect of ML-enabled Autonomous Driving (AD) on car manufacturers until 2030. It does that under two different lenses: First, the effect on the value proposition and the business models of the car manufacturers. Second it identifies hurdles for the implementation and draws strategic implications for the car manufacturers. Method This dissertation uses a qualitative research approach to answer the research questions, comprising of a multiple-case study using semi-structured interviews in order to gain insights from a number of relevant experts from different organizations. The primary findings are complemented by a secondary research that through triangulation assists identification of hurdles, which computed in a scenario analysis assess level of AD available in 2030. Results and Conclusion The effect of AD within the car automotive industry for car manufacturers is subjected to the hurdles of technology progress, legislation, need for new competencies, the need to collaborate, costs, ethics, safety & customer trust. By 2030 the likeliest scenario is that fully autonomous vehicles are solely available for particular high value use cases, affecting the value proposition toward provision of mobility services, increase of customer- & value-centric value propositions fostered by continuous interactions between the OEM and the user.