Partnership with Siemens and Contact Software
Mercedes-Benz puts AI at the heart of vehicle development
PLM.AI is part of Mercedes-Benz’s broader “AI First Strategy“, aimed at embedding artificial intelligence across the value chain.
Mercedes-Benz
Mercedes-Benz is building PLM.AI as a digital backbone for vehicle development. Together with Contact Software and Siemens, the OEM is linking engineering, production and aftersales, while AI agents analyse complex trade-offs across functions and could cut development times by up to 30 per cent.
Mercedes-Benz is overhauling the digital foundation of vehicle development with a new group-wide product lifecycle management platform developed together with Contact Software and Siemens. Called PLM.AI, the system is intended to connect engineering, purchasing, production, sales and aftersales through a continuous digital data model. The aim is to link information and decisions more closely across the full vehicle lifecycle and shorten development cycles.
Mercedes-Benz says the platform could reduce the development time of new vehicles by up to 30 per cent. In individual engineering processes, the company expects efficiency gains of as much as 50 per cent, equivalent to cutting vehicle development by around 17 months. “With PLM.AI, we are creating the digital backbone for the future of product development at Mercedes-Benz. At the same time, we are laying the foundation for connecting vehicle development, production and the customer experience in a new dimension,” says Jörg Burzer, Mercedes-Benz Board Member for Development.
How does PLM.AI fit into Mercede’s AI First strategy?
The project forms part of the carmaker’s broader AI agenda. Mercedes-Benz has been pursuing an “AI First” approach that is intended to embed artificial intelligence across the value chain rather than use it only to optimise individual processes. AI applications are already used in engineering, production, software development and administration. The longer-term goal is to make AI a structural element of decision-making and value creation.
Mercedes-Benz AI strategist Daniel Eitler has said the company wants to prioritise use cases that “create the greatest added value along the value chain”. At the same time, the manufacturer is focusing on standardised data structures, governance models and systematic skills development across its workforce. PLM.AI is intended to provide the technological basis for applying that strategy at scale in engineering. Mercedes-Benz says it is investing around €500 million in the platform.
How will AI agents handle engineering trade-offs?
Specialised AI agents are a central part of the new system. They are intended to analyse complex relationships across different business functions and automatically connect relevant information. Mercedes-Benz uses vehicle weight optimisation as one example. A change to a component can affect cost, safety requirements, software functions and production processes at the same time. The agents are expected to assess these interactions in parallel and provide engineers with more robust information for decisions more quickly.
AI already supports material-cost analysis, certification processes, fault detection and software development at Mercedes-Benz. PLM.AI is intended to connect these relatively discrete applications within an end-to-end digital development process. The approach reflects a broader shift in which AI agents can support requirements, validation and other engineering workflows rather than remaining limited to individual assistance tasks.
Why is agentic AI moving into core automotive processes?
The platform also reflects an industry trend beyond conventional generative AI. While GenAI has so far often acted as an assistant, agentic systems are designed to coordinate tasks, assess information and prepare decisions with greater autonomy.
BMW is testing a similar principle in business-critical processes. In purchasing, its “Mr. Flexibility” agent supports management with capacity and cost decisions and is intended to analyse complex scenarios much faster. The aim is to speed up decisions across highly dynamic supply and production networks while grounding them in available data.
Mercedes-Benz and BMW are therefore pursuing a comparable direction: AI agents are moving from isolated applications into central business processes. The important change is less the presence of an AI interface than the ability to connect data, tools and decisions across organisational boundaries.
How are Siemens and Contact Software shaping the architecture?
Technically, Mercedes-Benz is relying on two strategic partners for PLM.AI. Contact Software supplies Contact Elements as the open PLM core layer that brings together product data, processes and applications. Siemens contributes technologies for AI-assisted vehicle design, simulation and digital twins. In this context, simulation and digital twins can move verification earlier in the development process, helping engineering teams assess changes before they reach later physical stages.
Overall responsibility for the architecture, governance and transformation remains with Mercedes-Benz. The company also stresses that it will retain full control over its data and the AI models built on top of it. This point becomes increasingly important as product data, engineering logic and AI-supported decisions are combined in one environment.
PLM.AI is therefore intended to be more than a replacement for an existing PLM system. Mercedes-Benz is building a data-driven engineering environment that combines AI agents, digital twins and company-wide process data on one platform. The decisive test will be whether this integration translates into shorter development cycles and faster innovation processes.
