Artificial intelligence
Data governance and AI
Publiée le October 29, 2025
Publiée le October 29, 2025
AI, whether used for predictive maintenance, part recognition or intelligent planning, relies on reliable, well-governed data. Without a coherent, structured database, algorithms produce erroneous results. The major challenge highlighted by Datategy is data quality and availability: many organizations use obsolete or incomplete inventories, making searches imprecise and maintenance predictions unreliable. In addition, the integration of multiple sources (scanned documents, external suppliers, legacy systems) represents a technical challenge.
To take advantage of AI, it is necessary to integrate and unify data:
Standardize parts catalogs and bills of materials: each part must be uniquely identified, with complete metadata (dimensions, material, compatibility). Duplications and variants must be eliminated.
Create APIs and middleware to connect AI to ERP, inventory systems, HRIS and financial systems. The aim is to obtain a global view of operations and enable a fluid flow of information.
Ensure traceability: all interventions, data modifications and model updates must be logged to guarantee transparency and compliance (e.g. ISO 55000 standard for asset management).
Beyond the technical aspects, the success of an AI project depends onuser adoption. The people involved (technicians, planners, managers) need to understand the benefits and feel involved. Studies show that adoption depends on :
Ergonomic interfaces: solutions must be intuitive, with screens adapted to the context (tablets in the field, touch interfaces, schedule display). The application must minimize interruptions. VirtoSoftware’s study explains that tasks must be assigned without disturbing the agent, via simple interfaces and clear notifications.
Ongoing training: technicians need to learn how to interpret predictive data, use the parts search tool and adjust schedules. Training can include case studies and simulations based on the digital twin.
A participative approach: involve teams in defining use cases and scheduling rules. This promotes ownership and reduces resistance. For example, when interviewing maintainers, it’s a good idea to understand their irritants and document their best practices.
Support for change: AI may give rise to fears of substitution. It is important to communicate its role as a support tool, and to make the most of human skills (diagnosis, arbitration, field expertise). MIT Sloan’s article on maintenance highlights the importance of resolving data quality issues, integrating legacy systems and overcoming cultural resistance to take advantage of AI.
Implementing AI in an industrial context also requires compliance with an ethical and regulatory framework:
Data protection: information collected (sensor data, HR data) must be stored and processed in compliance with the RGPD and local regulations. It is imperative to implement confidentiality and security policies.
Algorithm transparency: the models used (for maintenance or scheduling) must be documented to explain their decisions. In the event of a dispute (for example, if an algorithm proposes an intervention that was not planned), traceability must enable the logic to be understood.
Fairness and non-discrimination: when AI assigns tasks, it must not introduce any bias. Assignment rules must be based on objective criteria (skills, availability) and validated by HR.
To succeed in transforming 4.0 rail workshops, it’s not enough to buy AI tools. You need to invest in data quality, standardization, integration and ergonomics. Data governance is the foundation on which predictive maintenance, part recognition, dynamic scheduling and intelligent planning with digital twin are built. By structuring these initiatives around a governance strategy, and by getting teams involved, we can take full advantage of the AI startups and technologies discussed in these articles.