Manufacturing teams use AI across predictive maintenance, quality inspection, supply-chain planning, and automation. Compliance risk depends on where model outputs are applied and whether failures could affect safety, rights, or essential services.
Start by separating optimization tools from safety-adjacent systems. AI that influences operator safety, critical infrastructure behavior, or product conformity requires stronger governance than general forecasting.
Quality control models should be monitored for false negatives and drift. If systems miss defects, the downstream impact may be significant. Maintain validation records, escalation thresholds, and clear handoff protocols to human reviewers.
Predictive maintenance is valuable but should not operate without fallback logic. Document failure modes, confidence thresholds, and override processes. This is particularly important when downtime or malfunction can trigger broader operational or safety consequences.
For SMEs, compliance maturity can be built incrementally: inventory systems, assign risk owners, maintain logs, and standardize review cycles. A lean but disciplined approach supports both regulatory readiness and operational resilience.