Why forward-thinking leaders are choosing to make compliance a strategic focus now
A future-proof data structure is essential to meet new EU regulations, implement scalable AI, and remain competitive.
The new reality for European manufacturing companies
Manufacturers face growing pressure for transparency, safety, traceability, and digital product information, driven by regulation, digitization, and new technologies. These demands affect all outputs at once, from documentation to cybersecurity and AI features in machines.
Because they influence every stage of the product lifecycle, data can no longer sit in separate teams, systems, or formats. Companies need more than small fixes to documents or tools. They need a new way to structure, manage, and access product and documentation data.
Today data is often scattered, while regulators, customers, and partners expect a consistent information base with high quality and traceability. Engineering needs version control. Service needs current, modular content. IT and Security must prove safe digital performance. Leaders want scalable AI.
It is now clear that data structure is not a technical detail but a strategic requirement for companies pursuing sustainable growth. Without a solid information foundation, compliance becomes costly, digital services slow down, and risks increase.
Market context: A structural shift toward data-driven compliance
The European Sustainability Products Regulation (ESPR) introduces a fundamentally different framework: virtually all products are subject to ecodesign and information requirements that go beyond energy efficiency. This includes traceability and circularity and mandates machine-readable product information delivered via the Digital Product Passport (DPP).
The DPP requires manufacturers to provide detailed, auditable, and traceable data on production, materials, sustainability, and lifecycle information. Manufacturers must also be able to provide batch and lot-level traceability and maintain data consistency throughout the supply chain.
The EU Data Act bolsters this movement by requiring companies to make data available in a machine-readable, accessible, and interoperable format. This obligation will also apply to product design ("data by design") starting in September 2026.
For the manufacturing industry, compliance is thus shifting from document management to organization-wide data management.
The invisible brake on progress that blocks compliance and innovation
Experience shows that in many manufacturing companies, critical product data is scattered across PDFs, legacy systems, local drives, and department-specific tools. Technical documentation, parts information, safety instructions, and compliance files often exist in multiple, conflicting versions.
The new EU regulations make this fragmentation unsustainable:
Without a uniform data structure, companies simply cannot meet these requirements. An additional drawback is that inconsistent and unstructured datasets make successful implementation of nearly all AI initiatives, such as predictive maintenance, automated documentation generation, or service optimization, impossible.
Impact and risks of fragmented and inconsistent data
Operational and financial
- Delayed product launches due to inadequate documentation or traceability.
- Recurring costs due to duplicate content, rework, and inefficient data flows.
- Fines for non-compliance—such as fines of 4–10% of revenue, depending on the type of data and member state for specific EU Data Act violations.
Strategic
- Inability to implement and scale AI projects due to poor data quality.
- Loss of competitive position as digital services, automation, and lifecycle services cannot be scaled.
Reputational risk
Inconsistent information directed toward customers, service partners, or regulators can lead to claims, audits, and reputational damage.
Our vision: compliance and AI require one integral information ecosystem
The common thread in all EU regulations is clear: product information must be structured, complete, standardized, and traceable.
To achieve this, companies need a uniform information and data model that:
- Works across engineering, service, quality, and product management;
- Centrally manages metadata, taxonomy, and version control;
- Prepares data for AI modeling and automation;
- Meets ESPR and DPP requirements for machine-readable, auditable data formats.
This is not a documentation project. This is a digital transformation of the information architecture.
Five essential steps for a future-proof transformation
- 1
Inventory all critical product and documentation data
Identify where data is located, in what formats, and how complete it is.
- 2
Define a uniform enterprise-wide information and data model
This prevents duplicate content, inconsistencies, and traceability issues.
- 3
Introduce governance: taxonomy, metadata, and quality rules
Essential for DPP requirements, Data Act interoperability, and AI modeling.
- 4
Automate documentation processes where possible
Using modular content, workflow automation, and version control.
- 5
Design documentation and data flows as a predictable operation
Not as a project, but as an ongoing part of product lifecycle management.
A solid information structure as the foundation for competitive advantage
Companies that invest now in a solid data structure create advantages on three fronts:
- Compliance: ESPR, Data Act, and Machinery Regulation become predictable and manageable.
- Operational excellence: Fewer delays, lower costs, and higher product quality.
- Innovation and growth: AI applications, digital services, and automation become scalable because the underlying data is reliable and structured.
The winners in the coming years will be the companies that understand that information is the new infrastructure.
Conclusion
European regulations are definitively changing the rules of the game. Compliance is no longer merely a documentation effort but a data issue of strategic importance. Companies that modernize their information foundation today minimize risks, manage costs, and create room for sustainable growth and AI-driven innovation.