Artificial intelligence (AI) is already fundamentally changing how products are designed, developed, and brought to market. In a comprehensive joint study, the German Research Center for Artificial Intelligence (DFKI), Accenture, and the Fraunhofer Institute for Software and Systems Engineering ISST examine how AI can accelerate and transform product development. The white paper describes a scalable framework for introducing AI across various engineering disciplines, thereby closing the gap between isolated pilot projects and company-wide transformation.

The study highlights that many companies are currently using AI to optimize individual engineering activities. However, the real potential lies in connecting data, software tools, and disciplines across the entire product lifecycle. By establishing a robust digital thread, i.e., a continuous flow of data from the concept phase to production, companies can liberate knowledge from data silos, enable cross-functional collaboration, and accelerate the development of innovative products.

The white paper identifies five key dimensions that are critical for scaling AI in engineering: data quality, interoperability, AI platforms, context management, and federated governance. Together, they form the foundation for a sustainable AI ecosystem in product development and ensure that technological advances are aligned with organizational and strategic goals.

Practical examples and industry insights show how AI can improve all phases of engineering: from requirements management and product architecture to simulation and system testing to release management. Vertically integrated AI use cases demonstrate domain-specific optimizations, while horizontally integrated applications link engineering disciplines and enable cross-system thinking and knowledge sharing.

The study also emphasizes that the future of engineering lies in agentic AI, i.e., systems capable of autonomous reasoning and orchestrating workflows across different disciplines and tools. These systems will play a key role in realizing cross-domain automation, for example in complex processes such as change and configuration management, which could be fully automated in the future.

However, the authors emphasize that these capabilities require more than just technological innovation. Companies must invest in AI-enabled infrastructures, define clear governance models, and establish interdisciplinary collaboration between data, IT, and engineering teams. Without these foundations, AI will remain limited to isolated pilot projects with limited business value.

The conclusion is clear: companies that act now to connect their engineering data and build scalable AI capabilities will secure a decisive competitive advantage. Those who hesitate risk fragmentation, inefficiency, and the loss of innovation momentum in an increasingly AI-driven engineering landscape.

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