Robots detect anomalies, adjust parameters, stop production lines. Decisions that were always human. How far does autonomy extend? Who is accountable when a system decision causes an incident?
Industrial autonomous systems make real-time decisions: stopping a production line, modifying a critical parameter, redirecting a logistics flow. These decisions have direct financial, operational and sometimes regulatory consequences.
In most deployments, this boundary was never explicitly defined at executive level. It formed by default during technical parameterisation. Leadership does not know precisely what it has delegated to its systems.
We intervene to explicitly define the boundary between automated and human decisions, structure the accountability chain at executive level and anticipate EU AI Act obligations. Not a technical compliance exercise. A decisional governance framework.
Autonomous AI systems governance refers to the framework by which an organisation defines the limits of decisional autonomy of its robotic or AI systems, structures the accountability chain in case of incident, and ensures compliance with applicable regulatory requirements.
In an industrial environment, autonomous systems capable of making real-time decisions : line stops, parameter adjustments, anomaly detection : pose a fundamental question of decisional delegation. The boundary between what the system decides alone and what requires human validation must be explicitly defined, documented and revisable. Without this definition, accountability in case of incident is ambiguous and potentially unmanageable.
The EU AI Act classifies some autonomous systems in critical industrial environments as high-risk AI systems, subject to mandatory human oversight, technical documentation and audit trail obligations from August 2026. Industrial operators must formalise their governance framework before large-scale deployment of autonomous decision systems.
The answer to this question should be precise and documented. We intervene to make it so.
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