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This thesis explores the imperative of intelligent data and potential analysis in the realm of mechatronic product development. The persistent challenges of synchronization and efficiency underscore the need for advanced methodologies. Leveraging the substantial advancements in Artificial Intelligence (AI), particularly in generative AI, presents unprecedented opportunities. However, significant challenges, especially regarding robustness and trustworthiness, remain unaddressed.

In response to this critical need, a comprehensive methodology is introduced, examining the entire development process through the illustrative V-Model and striving to establish a robust AI landscape. The methodology explores acquiring suitable and efficient knowledge, along with methodical implementation, addressing diverse requirements for accuracy at various stages of development. 

As the landscape of mechatronic product development evolves, integrating intelligent data and harnessing the power of AI not only addresses current challenges but also positions organizations for greater innovation and competitiveness in the dynamic market landscape.

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.