Siemens is redefining what it means to build and use digital twins by pairing artificial intelligence with software-defined design principles to make these tools more accessible and impactful for engineering teams. Machine Design tells that at the center of this shift is Siemens’ PAVE360 Automotive platform, a cloud-based digital twin system that helps automakers manage the complexity of software-defined vehicles by bringing hardware and software design together early in development. That integration can cut into the difficulty and cost of late-stage testing and integration, letting teams refine components in a virtual environment before anything physical exists.
Traditionally, hardware and software in products were developed in separate silos, leading to integration issues late in the process. Siemens views the software-defined product concept as a way to eliminate that gap. By modeling both domains simultaneously and iterating continuously in the cloud, engineers can catch integration problems long before prototypes are built. Virtual tests run at scale replace some physical testing, and continuous integration, delivery, and testing pipelines improve confidence in design outcomes.
Digital twin models now include two core parts: an executable twin, which simulates system behavior using physics-based models, and a declarative twin, a continuously updated representation of system requirements. Linking these through verification threading helps teams align physical performance with the design intent as it evolves.
AI amplifies these trends by automating data preparation, reducing manual effort to structure and contextualize design information. That’s important because high-value data often remains trapped in fragmented formats, slowing twin creation and limiting usefulness. AI tools can interpret engineering data, connect it to simulation environments, and help teams test thousands of scenarios quickly.
By making digital twins more responsive and integrated with real-world design workflows, Siemens is lowering barriers to adoption. Engineers in automotive and other complex systems can prototype, validate, and optimize performance virtually, reducing risk and accelerating time to market.