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This thesis explores the potential of Artificial Intelligence (AI) in mechatronic product development, focusing on the identification, utilization, and evaluation of AI-driven approaches. The increasing complexity of cross-domain collaboration, coupled with the demand for efficiency and reliability, necessitates structured methodologies to systematically integrate AI into engineering processes. While AI offers significant opportunities, challenges related to trustworthiness, robustness, and effective implementation remain critical considerations.

To address these challenges, this work introduces a generalized five-step methodology, providing a structured framework for assessing AI’s role in mechatronic development. The methodology enables the targeted identification of AI potential, structured integration into engineering workflows, and systematic evaluation of its impact. By applying this framework to real-world industrial case studies, the thesis demonstrates its practical applicability across different AI use cases, including translation, interpretation, and prediction.

As mechatronic product development continues to evolve, leveraging AI in a structured and validated manner ensures that organizations not only overcome current challenges but also enhance innovation, decision-making, and cross-domain collaboration. The findings of this thesis provide a scalable foundation for AI-driven advancements while maintaining a balance between AI potential and investment considerations.