BMW: From AI Experimentation to AI Embedded in All Areas

The debate over whether the artificial intelligence bubble will burst continues to occupy market analysts, but for the BMW Group, the technology is viewed as a definitive long-term pillar of industrial survival.

The German automotive manufacturer, a brand synonymous with engineering precision, is currently incorporating AI into hundreds of operational areas. Its leadership predicts a near future where every process at BMW Group will be AI-supported, moving the technology from the periphery of research into the core of its global value chain.

By scaling AI capabilities across development, production, and sales, BMW has established a Group-wide AI platform. This infrastructure enables custom solutions ranging from complex manufacturing optimisation to generative AI in customer communications.

Marco Gorgmaier, VP Enterprise Platforms and Services, Data, AI at BMW Group, says: “We’re scaling artificial intelligence along the value chain, from development and production through to sales. In the foreseeable future, every process at the BMW Group will be AI-supported. We already have hundreds of use cases in series production today.”

Driving innovation through business imperatives

Marco says that efficiency, innovation and a rigorous focus on return on investment (ROI) are the primary drivers of the move. Dr Nicolai Martin, Member of the Board of Management of BMW AG, Purchasing and Supplier Network, suggests that digitalisation has become a daily operational reality rather than a future aspiration.

"At the BMW Group Purchasing Division, digitalisation and artificial intelligence are no longer just future topics – they are part of our daily reality," he says.

The company believes that leading the industry's digital shift requires a departure from isolated internal systems toward collaborative data ecosystems. A key component of this is Catena-X, the first open data ecosystem for the automotive industry.

Fully operational as of early 2026, Catena-X allows partners across the value chain to address resilience and sustainability through secure, standardised data exchange. It enables the precise calculation of product carbon footprints, tracing emissions from raw material extraction to the final product.

Collaboration platforms enhancing value chains

Nicolai cites a demonstration project involving the BMW iX kidney grille produced in Landshut as a benchmark for this transparency.

"Thanks to Catena-X, it is possible to calculate the product carbon footprint across the entire value chain – from raw material extraction to the final product," he says. For technology executives, the integration of AI with such ecosystems is transformative, enabling earlier risk identification and more efficient resource use.

To support this across its workforce, BMW has developed a generative AI self-service platform. This infrastructure provides employees with straightforward access to AI tools, allowing non-technical users to develop their own AI solutions through the BMW Group AI Assistant.

This democratisation of technology increases efficiency while maintaining a robust governance framework to ensure secure and compliant usage.

The company maintains a strategy of technological openness, avoiding dependence on specific large language model (LLM) providers to ensure long-term architectural flexibility.

Product development and virtual simulation

In engineering and product development, AI is being deployed to handle technical tasks that involve extensive simulations. These applications include crash testing, aerodynamics and autonomous driving scenarios.

By shifting the burden of testing to virtual environments, BMW is reducing its reliance on physical prototypes and accelerating development cycles. This is further supported by the company’s AI Lab at its Landshut facility, where employees explore cutting-edge technologies hands-on to bring new ideas to production.

In 2026, the company is also pushing the boundaries of "Physical AI" – the symbiosis of digital intelligence and mechanical hardware. This includes the deployment of humanoid robots in production environments, such as the AEON project at the Leipzig plant.

These systems learn from real-world production data and are integrated into existing series manufacturing. "We are now entering the next chapter: scaling AI across our organisation to unlock new levels of efficiency and to empower smarter, faster and more forward-looking decision-making," Nicolai explains.

Procurement intelligence and multi-agent systems

The digital nervous system also extends to procurement operations via the AIconic multi-agent system. This centralised chat interface incorporates tools such as the Tender Assistant and the Offer Analyst.

The Tender Assistant supports teams in creating high-quality documents by selecting appropriate templates and best practices from previous tenders. Meanwhile, the Offer Analyst streamlines the comparison of supplier documents, helping users review legal aspects and departmental requirements efficiently.

These AI-driven tools enable data-driven decision-making through natural language processing and intelligent search algorithms.

Nicolai says that the journey into this digital era can only succeed through "radical collaboration" with partners in the supplier network.