AI Governance in Manufacturing
Assurance Focus: Implementing the AIMS (ISO/IEC 42001) for safety-critical and predictive automation systems in industrial environments.
The Institute provides expert guidance to design and deploy an AI management framework, ensuring safety, traceability, and compliance with the EU AI Act, particularly for high-risk predictive maintenance and quality assurance models.
Project Overview
The core challenge in industrial AI is establishing continuous governance that integrates ISO/IEC 42001 requirements directly into the Operational Technology (OT) environment to manage high-risk predictive models used in production lines. Our framework focuses on enabling safe automation.
Focus on Industrial Risk & Assurance
In manufacturing, AI errors impact physical safety, process quality, and system uptime. We target these critical risk areas identified by the ISO/IEC 42001 standard.
Safety & Process Integrity
Mitigating risk of physical harm (C.2.9) caused by erroneous AI commands to robotics/machinery. Ensuring Maintainability (C.2.6) to quickly correct defects in the OT environment.
Robustness & Data Quality
Ensuring model Robustness (C.2.8) against sensor noise, hardware issues (C.3.5), and low Data Quality (C.3.4), which can lead to costly false positives in predictive maintenance.
Accountability & Traceability
Defining clear Accountability (C.2.1) for automated quality assurance decisions and guaranteeing full Transparency (C.2.11) of AI logs for regulatory inspection and troubleshooting.
Project Objectives: Integration of AI & Quality
ISO/IEC 42001 AIMS Establishment
To build a comprehensive Governance and Documentation System for all AI operations, ensuring full AIMS compliance with ISO/IEC 42001.
Quality & Safety Standards Alignment
To align AI system validation and performance with existing quality protocols (ISO 9001) and occupational safety requirements (ISO 45001).
Traceability in Predictive Automation
To ensure full traceability and transparency in predictive maintenance models, allowing human engineers to verify failure predictions before costly interventions or safety incidents occur.
Implementation Timeline (AIMS Framework)
1. Assessment
AI Risk & Scope (Cl. 6.1)
2. Design
Policy & Process Dev (Cl. 5, 8.1)
3. Implementation
Control Deployment (Annex A)
4. Audit & Review
Internal & Management Review (Cl. 9)
Projected Assurance Metrics
20%
reduction in AI-related system errors due to enhanced data quality and MLOps.
100%
Full documented readiness for ISO/IEC 42001 certification and regulatory submission.
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Transparent audit documentation secured, facilitating access to EU funding and grants.
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