AI Governance in Manufacturing: A Practical Guide for 2026

AI governance in manufacturing extends conventional AI oversight beyond data and software to include operational technology, industrial control systems, machinery, safety processes, and the physical consequences of incorrect model decisions, such as equipment damage, worker injury, or product recalls. It calls for inventories spanning IT and OT environments, risk classification based on physical impact, strategic-to-plant-level oversight, thorough audit records, and integration with established quality systems such as ISO 9001 and ISO/IEC 42001. Relevant regulatory pressures include the EU AI Act and the EU Machinery Regulation 2023/1230, which will require AI-enabled machinery to meet safety requirements for CE marking from January 2027. The text identifies limited visibility into AI embedded in sensors and control systems, along with unsanctioned “shadow AI” use by employees, as major governance gaps, while noting that insurers increasingly evaluate AI controls during cyber-insurance underwriting. It recommends beginning with an OT-focused inventory, mapping applicable rules early, assigning ownership across governance tiers, and treating AI decision logging with the rigor used for safety incidents; it also presents Superblocks as a platform for governing AI-built internal applications through role-based access controls and audit capabilities.

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