New vehicle architectures are changing the aftermarket
AI-supported product intelligence in the automotive aftermarket
Modern solutions such as AI-based product intelligence and digital twins enable more efficient working methods and provide clear competitive advantages for workshops and parts wholesalers – the new technologies speed up processes, minimize sources of error and make data useful.
Today’s vehicles consist of an interplay of complex mechanical, electronic and software systems. Traditional diagnostic devices are reaching their limits. Digital solutions provide a remedy. The increasing diversity of variants and tight timeframes in workshop operations require tools that work in a networked, intelligent and scalable way.
Example: The Spread platform integrates product data from various sources, structures it semantically and makes it available as required. This makes diagnosis, repair and parts identification much easier.
Product intelligence: structured data is the key
Modern workshop processes require more than just access to data. Their quality and structure are crucial. Product Intelligence bundles and interprets information from a wide variety of sources:
- Diagnostic systems
- CAD models
- Repair guides
- Sensor and telemetry data
The goal: a functional digital twin that not only recognizes individual components, but also understands their interactions.
Digital twins: faster fault diagnosis, lower risk
The Spread platform shows how digital twins are transforming everyday workshop practice. Distributed data merges into an intelligent vehicle model that shows cause-and-effect relationships.
Concrete advantages for the aftermarket:
- Up to 80 % faster troubleshooting
- Lower costs due to fewer returns
- Context-sensitive support directly on the vehicle
Practical example: A mechanic uses a voice command to ask a question about a vehicle camera. The AI recognizes the request, searches relevant data sources and provides visual recommendations for action in real time – in the desired language.
Data structure as the foundation for AI benefits
Data quality determines the success of AI-supported systems. Unstructured raw data brings little added value. Spread uses a semantic data architecture that recognizes and correctly interprets terms such as DTC, ECU or software container.
- Advantages for workshops and wholesalers:
- Standardized data structure across system boundaries
- Direct access to relevant information
- Reduction of media disruptions and duplication of work
The platform remains flexible and adapts dynamically to new vehicle models and technology standards.
People and technology: practical relevance as a success factor
Despite a high degree of automation, humans remain crucial. Successful AI integration can only be achieved with user-friendly solutions.
Important factors for implementation:
- Intuitive user interfaces
- Practical use cases
- Training and promoting acceptance in the team
Small and medium-sized companies in particular benefit from a modular approach. Instead of selective pilot projects, the focus should be on long-term scalability.
Conclusion: Future-proofing through networked intelligence. AI-based product intelligence is no longer a vision, but already a reality in the aftermarket. Platforms such as Spread create real added value – from faster diagnostic processes to precise error prevention. Companies that invest in structured data models and AI applications at an early stage gain a clear competitive advantage. Because the more complex the vehicles, the more important immediate access to relevant knowledge becomes – across systems, in multiple languages and available at any time.
