Industrial and manufacturing
Protection of intellectual property, designs and supply-chain data.
Context
For manufacturers, designs, process knowledge and supplier data are the business itself. AI creates value across engineering and operations when that knowledge stays within the company.
Key regulation
| Framework | Relevance |
|---|---|
| EU Data Act | Rules on access to and sharing of industrial and product data, applicable since 12 September 2025. |
| EU AI Act | AI embedded in regulated products such as machinery is high-risk, with obligations from 2 August 2028. |
| NIS2 | Manufacturers of machinery, electronics, vehicles and medical devices can fall within scope. |
| Trade secrets | Protection depends on reasonable measures to keep know-how confidential. |
Challenges and opportunities
Challenges
- Intellectual property Designs and process knowledge must not leave the company's control.
- Supplier data Contracts with suppliers often restrict how shared data may be processed.
- Legacy systems Engineering data sits across many older systems and formats.
Opportunities
- Engineering assistants Search and drafting across specifications, standards and test reports.
- Quality management Faster analysis of deviations, complaints and quality records.
- Supply-chain analysis Structured insight into supplier documentation and risks.
How Lindstead helps
- Infrastructure and cost Quantify the total cost of ownership of in-house AI against current API expenditure, including hardware, energy and staffing.
- Deployment Deliver a production-grade first use case on your own infrastructure, with the security and quality controls auditors expect.
- Model stewardship Continuously assess new model releases and regulatory developments against your established baseline, and advise on upgrades.
Discuss the priorities in industrial and manufacturing.
Schedule an introductory meeting with our team.