Navigating the Tension Between Innovation and Regulation - A Qualitative Comparative Study on AI Deployment in Heavy-Duty Vehicles versus Passenger Cars

Abstract

Artificial Intelligence (AI) is transforming the automotive industry by enabling innovations in safety, efficiency and sustainability. However, the integration of AI also raises ethical and regulatory challenges, a research dimension that is currently subject to a gap in existing literature. Hence, the purpose of this thesis is to explore how automotive firms navigate emerging AI technologies amid differing stakeholder interests and regulatory demands. The study addresses the current research gap by analyzing how firms operationalize ethical AI by offering sectoral comparisons between the heavy-duty vehicle and passenger car sectors. The conceptual model builds on the FATE Framework, Stakeholder Theory and Corporate Social Responsibility (CSR), which are the key frameworks used in this research. The study adopted a qualitative and comparative research design using the Gioia methodology and combined a systematic literature review with empirical material collected through semi-structured interviews. The interviews were conducted with experts in the field of AI and company spokespersons from the two sectors. The empirical findings revealed sectoral differences in how AI is operationalized and governed between the two sectors. While the passenger car sector emphasizes personalization, consent and individual safety, the heavy-duty vehicle sector focuses on operational efficiency and large-scale sustainability. A recurring challenge of the two sectors is to balance rapid innovation with increasingly complex regulations and geopolitical tensions. Furthermore, tensions between primary and other stakeholders, such as customers and regulators, illustrate the difficulty of aligning innovation with diverging expectations. An adapted conceptual model was proposed to illustrate the interplay between theoretical principles and empirical realities. It highlights the necessity of ethical AI deployment through ongoing stakeholder engagement, ethical assurance, and regulatory responsiveness. The study concludes that ethical AI deployment is not a static achievement, but a dynamic, context-dependent process shaped by technological advancement, stakeholder tensions, trade-offs and and evolving legal frameworks.