Industry: Automotive Manufacturing
Organization: Large German OEM
Function: Engineering & Product Development
Challenge
Fragmented Engineering Knowledge at Scale
Situation
Engineering teams operated across a complex landscape of PLM systems, CAD tools, requirements platforms, test repositories, and supplier data sources.
Critical knowledge existed but it was siloed, duplicated, and difficult to reconcile.
Trigger
As engineering complexity increased and AI initiatives moved from experimentation to production, the organization hit a hard limit:
AI systems could not operate reliably because engineering context was fragmented and inconsistent.
Barrier
Traditional approaches failed:
- Document repositories lacked structure
- Search tools returned files, not context
- Data warehouses optimized reporting, not reasoning
- AI pilots broke down due to missing relationships, lineage, and ownership
The problem was not data availability.
It was the absence of connected, machine-understandable context.
Solution
A Connected Fabric of Engineering Knowledge
The organization implemented a linked data layer that unified engineering knowledge across systems into a single contextual fabric.
At the core was a Knowledge Graph that explicitly modelled:
- Engineering entities and artifacts
- System and component relationships
- Dependencies, requirements, and change history
This architecture allowed engineering knowledge to be:
- Navigable for humans
- Queryable for systems
- Reasonable for AI
Architecture & Trust
- Integrated, not replaced existing engineering tools
- Read-only ingestion where required to meet compliance constraints
- Governed access aligned with enterprise security and GDPR expectations
Human-in-the-Loop by Design
AI was deployed as an engineering copilot, not an autonomous decision-maker:
- Engineers retained control
- AI assisted with discovery, analysis, and impact assessment
- Decisions remained auditable and explainable
Impact
Measurable Efficiency and AI-Ready Engineering
Results
- 60–70% reduction in search and rework time
- €2.2–3.3M/year returned to value-creating engineering work
- Engineering teams became structurally ready for AI, not dependent on fragile prompts or ad-hoc retrieval
Executive Perspective
“The breakthrough was not AI itself it was finally giving AI the same structured understanding of our engineering systems that our best engineers already had.”
Key Takeaway
AI does not fail in engineering because models are weak.
It fails because context is fragmented.
By connecting engineering knowledge into a governed, linked fabric, this German OEM achieved immediate efficiency gains while laying a durable foundation for AI-driven engineering workflows.