AI In Production
Audi brings artificial intelligence to the shopfloor with 'Edge 4 Cloud'
The German carmaker is deploying cloud-based control systems and AI-powered tools across its production network, transforming quality monitoring, automation and worker support whilst eliminating thousands of industrial computers.
Audi is accelerating its deployment of artificial intelligence across production facilities, introducing systems that control manufacturing operations via cloud platforms rather than stationary factory computers. The shift marks a fundamental change in how vehicle manufacturers manage automation, quality control and workforce support.
At the German carmaker's Neckarsulm facility, robots now grind down weld spatter on car body underbodies after AI-powered cameras identify imperfections and mark them with light. The system eliminates a physically demanding task whilst demonstrating how machine learning can be integrated into high-volume manufacturing. Six additional installations are planned for Audi's Ingolstadt site.
Artificial intelligence is a quantum leap for efficiency in our production. With our AI and digitalisation roadmap, we are transforming our plants into smart factories where AI acts as a partner, providing our employees with tailored support
"Artificial intelligence is a quantum leap for efficiency in our production. With our AI and digitalisation roadmap, we are transforming our plants into smart factories where AI acts as a partner, providing our employees with tailored support," explains Gerd Walker, Member of the Board of Management for Production and Logistics at Audi. "The first AI-controlled robots are taking over tasks that are ergonomically strenuous, and chatbots are providing additional relief."
The technology builds on partnerships with institutions including the Innovation Park Artificial Intelligence in Heilbronn, which provides access to emerging applications and research talent. Audi's approach combines decades of manufacturing expertise with external innovation, creating a development model that accelerates the transfer from laboratory concepts to production-ready systems.
Edge 4 Cloud Production: Intelligent infrastructure replaces thousands of computers
The foundation for this transformation is Edge Cloud 4 Production, a virtualised control architecture that Audi has been developing to replace individual industrial computers across its facilities. The system uses local servers positioned at plants to handle data processing and distribution with millisecond-level latency, enabling centralised management of production equipment, robots and automated guided vehicles.
In assembly operations at German facilities, worker guidance systems now receive real-time information about vehicle specifications and regional variants from centralised cloud sources. The migration has already eliminated more than 1,000 industrial PCs, reducing hardware maintenance requirements and improving cybersecurity through standardised software deployment.
Edge Cloud 4 Production entered large-scale testing at the A5 and A6 body shop in Neckarsulm, where virtual programmable logic controllers replaced local hardware on production lines. Around 100 robots coordinate their operations through the cloud platform with precision timing sufficient to manufacture several hundred vehicle bodies daily across three shifts. The achievement represents the first time such a system has been deployed in high-volume automotive production.
The architecture also simplifies software updates and security patches, which previously required individual installation on each device during production breaks. With cloud-based deployment, changes can be implemented across multiple systems simultaneously, reducing downtime and accelerating the introduction of new manufacturing capabilities.
AI detects defects and predicts failures
Audi's ProcessGuardAIn platform combines decades of expert knowledge with machine and sensor data to monitor manufacturing steps in real time. The system identifies anomalies early and alerts specialists, enabling faster intervention before defects propagate through production sequences.
Two pilot implementations are underway in the Neckarsulm paint shop, focusing on dosage optimisation in pretreatment and anomaly detection in cathodic dip coating. Both applications are scheduled for series production introduction in the second quarter of 2026, with plans to expand the system across all manufacturing processes as a central tool for predictive maintenance and quality assurance.
The platform emerged from Audi's P-Data Engine, a cross-plant initiative that standardises system and equipment data at uniform quality levels. This consolidated database allows data scientists to develop and scale AI applications rapidly, creating modular solutions that can be deployed across the Volkswagen Group's production network.
Future versions of ProcessGuardAIn will provide data-based recommendations and guide workers through corrective procedures via mobile applications. The progression from detection to prescription represents a shift in how AI supports manufacturing decisions, moving beyond passive monitoring to active problem resolution.
Automating the wiring loom installation process
The Next2OEM project addresses one of automotive manufacturing's persistent manual operations. Working with ten partners at its Ingolstadt headquarters, Audi is demonstrating complete digitalisation and automation of wiring loom production and assembly, from supplier manufacture through to vehicle installation. Currently, less than ten percent of wiring loom work is automated across the industry.
A demonstrator funded by the Federal Ministry for Economic Affairs and Energy maps the entire process chain, including loom production, pre-assembly in the centre console with automation-compatible connectors, and automated installation controlled by a central system. The approach reduces logistical complexity and shortens lead times for engineering changes from weeks to minutes.
Knowledge gained from the project will inform large-scale production planning for future vehicle programmes, potentially transforming how manufacturers handle one of assembly's most labour-intensive processes. Audi is also testing AI-supported dryer operation in its Neckarsulm paint shop, developed through collaboration with appliedAI initiative and CVET GmbH. The system connects temperature and air volume controllers in longitudinal dryers to an AI model, enabling faster response to production line speed variations. Testing will continue until summer 2026 to measure energy savings potential.
Cross-border collaboration drives AI adoption
Approximately 60 specialists in Audi's Production Lab and P-Data Factory drive technology development from initial concepts through to production implementation. The company works with Broadcom, Cisco and Siemens on Edge Cloud 4 Production, integrating virtualisation platforms, network infrastructure and automation technology.
Human-centric design principles guide the development process. Teams create demonstrations and prototypes early, gathering feedback from production workers to ensure interfaces match shopfloor requirements. The approach reflects broader industry recognition that successful digitalisation depends on operator acceptance rather than technical capability alone.
Audi Hungary systematically assesses its value chain to identify digitalisation opportunities, making manufacturing processes more transparent and efficient from planning through quality assurance. At Audi México, management uses AI-supported Production Reports to display real-time key figures, enabling decisions based on current operating data from the San José Chiapa facility.
The carmaker has established clear governance frameworks for AI deployment. Its Code of Conduct and policy statement on artificial intelligence commit the company to respect, security and transparency in AI applications. A Data Sharing Code of Practice ensures data handling aligns with corporate values whilst enabling the cross-plant collaboration necessary for scaling digital innovations.
Walker emphasises the transformative potential of combining internal expertise with external partnerships. "Together with our partners, we are setting standards for the data-driven production of the future: decisively and responsibly."
