Artificial Intelligence Driven Product Development: An analysis of how the integration of Artificial Intelligence into the automotive development process can shorten the product development cycle

Abstract

This thesis explores the transformative potential of Artificial Intelligence (AI) in accelerating product development cycles within the automotive industry, amid rapid shifts toward electric vehicles (EVs) and sustainable mobility. As global competition intensifies—driven by market leaders like Tesla and Chinese OEMs such as BYD—traditional development processes face challenges in speed, e!ciency, and innovation. The study hypothesizes that integrating AI into key stages of the automotive product lifecycle management (PLM) can reduce developmenttimelines by 20-50%, enhancing competitiveness through optimized workflows and resource allocation. Building on a comprehensive literature review of PLM, technology innovation management, systems engineering models (e.g., V-Model and MBSE), and AI advancements, the methodology proposes a departmentalized AI agent structure tailored to automotive contexts. This includes Future Innovation (7-10 year horizon) for technology scouting, Research & Development (4-7years) for design exploration, and Serial Development (2-4 years) for production maturation. A practical case study on electric traction drive development illustrates AI’s role in requirement refinement, trade-o" analysis, and simulation, yielding significant time savings while maintaining focus on business outcomes like cost reduction and market agility. Key findings demonstrate that AI-driven processes, such as generative AI for ideation and model-based engineering for validation, can streamline the innovation funnel, mitigate risks in chaotic technology landscapes (via adapted Stacey Matrix), and support generational product pulls. Implications for the automotive sector include improved margins (up to 5% uplift per PwC insights), faster time-to-market, and strategic advantages in the EV transition. imitations include reliance on hypothetical scenarios and ethical AI considerations, with recommendations for future work emphasizing real-world implementations and workforce upskilling. Overall, this research underscores AI as a strategic enabler for automotive firms to thrive in a dynamic, technology-push environment.