The study highlights that while many organizations focus on applying AI to optimize single engineering tasks, the true potential lies in connecting data, tools, and domains across the entire product lifecycle. By establishing a robust Digital Thread (the continuous flow of product data from concept to production), companies can unlock knowledge trapped in silos, enable cross-domain collaboration, and accelerate the creation of innovative products.
The white paper identifies five key dimensions essential 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, ensuring that technical progress aligns with organizational and strategic goals.
Practical examples and industrial insights illustrate how AI can enhance all stages of engineering, from requirement management and product architecture to simulation, system testing, and release management. Vertically integrated AI use cases demonstrate domain-specific optimization, while horizontally integrated applications connect engineering disciplines to enable system-level reasoning and knowledge transfer.
The study also underscores that the future of engineering lies in agentic AI capable of autonomous reasoning and workflow orchestration across domains and engineering tools. These systems will play a crucial role in realizing cross-domain automation, in which complex processes such as change and configuration management processes are automated end-to-end.
However, the authors emphasize that achieving these capabilities requires more than technology alone. Companies must invest in AI-ready infrastructures, define clear governance models, and establish cross-functional collaboration between data, IT, and engineering teams. Without these foundations, AI risks remaining confined to isolated pilots with limited business impact. The conclusion is clear: enterprises that act now to connect their engineering data and build scalable AI capabilities will gain a decisive competitive advantage. Those that delay risk fragmentation, inefficiency, and loss of innovation momentum in an increasingly AI-driven engineering landscape.
Fraunhofer Institute for Software and Systems Engineering