Publication
Exploring AI adoption in the European automotive industry : drivers and barriers
| Summary: | The European automotive industry faces increased competitive pressure and the use of artificial intelligence emerges as a crucial factor to drive improvements and maintain competitiveness. This study explores how AI is being adopted within the automotive industry using the Technology-Organisation-Environment-Framework. Based on eleven interviews with managers, data scientists and AI experts, the study identifies key drivers and barriers in the adoption process. Findings indicate that AI adoption is frequently initiated with a bottom-up approach, in which functional departments identify potential applications that are then developed together with central AI or Data Analytics teams. Organisational barriers, including limited resources, lengthy approval processes, and data compliance challenges, emerged as particularly significant, while technical barriers stemmed from complex infrastructure, legacy systems, and data quality and accessibility issues. External barriers were mainly due to regulatory and policy constraints. Despite these challenges, strategic sponsorship, strong top management support, AI’s business value, effective data management, and robust, updated IT infrastructure were identified as essential to enable AI adoption. Additionally, addressing barriers through organisation-wide, top-down initiatives appeared to be of importance. This study contributes to current literature by deepening the understanding of the adoption process, presenting influencing factors and revealing the interplay of organisational, technical and environmental aspects. |
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| Main Authors: | Wernado, Julius Leon |
| Subject: | Artificial intelligence AI adoption Automotive industry TOE framework Exploratory research Inteligência artificial Adoção de IA Indústria automóvel Quadro TOE Pesquisa exploratória |
| Year: | 2025 |
| Country: | Portugal |
| Document type: | master thesis |
| Access type: | open access |
| Associated institution: | Universidade Católica Portuguesa |
| Language: | English |
| Origin: | Veritati - Repositório Institucional da Universidade Católica Portuguesa |
| Summary: | The European automotive industry faces increased competitive pressure and the use of artificial intelligence emerges as a crucial factor to drive improvements and maintain competitiveness. This study explores how AI is being adopted within the automotive industry using the Technology-Organisation-Environment-Framework. Based on eleven interviews with managers, data scientists and AI experts, the study identifies key drivers and barriers in the adoption process. Findings indicate that AI adoption is frequently initiated with a bottom-up approach, in which functional departments identify potential applications that are then developed together with central AI or Data Analytics teams. Organisational barriers, including limited resources, lengthy approval processes, and data compliance challenges, emerged as particularly significant, while technical barriers stemmed from complex infrastructure, legacy systems, and data quality and accessibility issues. External barriers were mainly due to regulatory and policy constraints. Despite these challenges, strategic sponsorship, strong top management support, AI’s business value, effective data management, and robust, updated IT infrastructure were identified as essential to enable AI adoption. Additionally, addressing barriers through organisation-wide, top-down initiatives appeared to be of importance. This study contributes to current literature by deepening the understanding of the adoption process, presenting influencing factors and revealing the interplay of organisational, technical and environmental aspects. |
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