Chancen und Risiken von KI-basierte Methoden im Projektmanagement in der Automobilindustrie

Abstract

The automotive industry is facing a profound transformation, driven by electromobility and autonomous driving. This leads to shortened development cycles and intense pressure for innovation. Traditional project management methods are reaching their limits here, as projects are becoming increasingly complex, globally distributed, and regulated, which increases coordination efforts and uncertainties. Artificial intelligence (AI) offers enormous potential for efficiency, flexibility, and precision in project management. However, its use also presents challenges: complex technical requirements, the need for high data quality, data privacy concerns, ethical questions, and the risk of a loss of competence due to reliance on AI systems. Given the high failure rate of AI projects, a systematic investigation of the opportunities, risks, and necessary framework conditions for successful AI implementation in automotive project management is essential. This master's thesis is divided into four main chapters. The introduction outlines the problem, objectives, and methodology. Chapter 2 defines AI, explains its methods such as neural networks, machine learning, and deep learning, and describes their areas of application. Chapter 3 addresses project management in the automotive industry, its specifics, and challenges, particularly in vehicle development. Chapter 4 analyzes the interplay between AI and project management in the automotive industry by identifying opportunities and risks, necessary framework conditions, and concrete application examples. The thesis concludes with a summary and outlook. A systematic literature review was chosen as the methodology, based on comprehensive research in scientific databases (Scopus, ScienceDirect, Elsevier, Google Scholar) using specific English keywords. The results of this master's thesis show that AI offers significant opportunities in automotive project management by serving as an early warning system, improving planning, and automating risk management. Furthermore, AI promotes cross-functional collaboration and knowledge exchange, while digital assistance systems ease daily project work. Despite these potentials, significant risks remain, such as the intransparency of AI models ("black-box problem"), dependence on high-quality data, the risk of bias, cybersecurity threats, and a potential loss of human judgment. Ethical aspects and legal regulations like the GDPR and the EU AI Act pose additional challenges that require careful consideration. Successful and sustainable AI implementation therefore requires strategic anchoring, clear organizational structures, a powerful data and system architecture, and the development of new personnel competencies, as demonstrated by various case studies from the automotive industry

Date of Award	2025
Original language	German (Austria)
Supervisor	Hans-Peter Feichtenschlager (Supervisor)