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Manufacturers Urged to Build AI Governance Frameworks as Agentic Systems Expand
Manufacturers are scrambling to keep pace with a wave of autonomous AI agents that are reshaping factories and supply chains. On June 24 2026, Foley & Lardner LLP issued a practical guide that maps out five concrete steps for firms to establish robust AI governance.
The push comes after a PwC 2026 survey revealed that only 37 % of operations leaders feel comfortable delegating end‑to‑end processes to AI agents, and a mere 27 % have fully embedded an AI strategy across business units. The gap between rapid deployment and regulatory oversight is creating legal, operational, and compliance headaches.
A telling illustration is a Tier 1 automotive supplier that deployed an AI‑driven demand‑forecasting agent to autonomously cut steel purchase orders. The faulty forecast triggered a contractual minimum‑volume dispute, higher freight costs, and an OEM penalty. Because the manufacturer lacked a documented governance framework, it could not prove oversight or assign responsibility.
Step 1: Form a cross‑functional AI governance committee – Operations, procurement, legal, compliance, data science, and IT representatives should collaborate to supervise AI deployments and assess risk.
Step 2: Classify AI systems by risk and apply tiered controls – The guide proposes three tiers: - Tier 1 (advisory): AI recommends; humans decide. - Tier 2 (semi‑autonomous): AI recommends; humans approve. - Tier 3 (fully autonomous): AI decides and acts. Each tier demands specific safeguards—model validation, sandbox testing, human‑in‑the‑loop gates, continuous monitoring, audit trails, and emergency shutdowns—mirroring the EU AI Act’s risk‑based framework and the NIST AI RMF MAP function.
Step 3: Implement documented human oversight and emergency protocols – For high‑volume, time‑critical operations, the recommended model is “humans on the loop, not in the loop.” Emergency shutdown mechanisms are mandatory for safety‑critical environments.
Step 4: Demand vendor transparency and strengthen contractual protections – Contracts should require disclosure of training data sources, model weightings, and testing protocols; establish annual or quarterly assessment cadences; negotiate liability structures, indemnification, audit rights, and incident notification timelines; and develop contingency plans for vendor failure or model degradation.
Step 5: Design a scalable governance architecture – Foley & Lardner suggest an automation maturity path: Crawl (inventory AI assets), Walk (automate governance steps like drift detection), Run (embed governance into every workflow). Embedding controls upstream ensures new AI capabilities inherit governance by default. Digital twin and sandbox environments can support pre‑deployment testing, while building AI literacy is emphasized, as the EU AI Act requires operators to interpret AI outputs critically.
AI governance in manufacturing is an evolving operational function that must keep pace with technology. Manufacturers that adopt the five steps now will be better positioned to reap operational benefits while mitigating legal risk as autonomous capabilities expand. Foley & Lardner’s Manufacturing, Supply Chain, and Artificial Intelligence teams offer tailored assistance in designing and implementing AI governance programs. The guidance also notes that regulatory frameworks such as the EU AI Act and NIST AI RMF will increasingly mandate documented governance for high‑risk AI systems.
The push comes after a PwC 2026 survey revealed that only 37 % of operations leaders feel comfortable delegating end‑to‑end processes to AI agents, and a mere 27 % have fully embedded an AI strategy across business units. The gap between rapid deployment and regulatory oversight is creating legal, operational, and compliance headaches.
A telling illustration is a Tier 1 automotive supplier that deployed an AI‑driven demand‑forecasting agent to autonomously cut steel purchase orders. The faulty forecast triggered a contractual minimum‑volume dispute, higher freight costs, and an OEM penalty. Because the manufacturer lacked a documented governance framework, it could not prove oversight or assign responsibility.
Step 1: Form a cross‑functional AI governance committee – Operations, procurement, legal, compliance, data science, and IT representatives should collaborate to supervise AI deployments and assess risk.
Step 2: Classify AI systems by risk and apply tiered controls – The guide proposes three tiers: - Tier 1 (advisory): AI recommends; humans decide. - Tier 2 (semi‑autonomous): AI recommends; humans approve. - Tier 3 (fully autonomous): AI decides and acts. Each tier demands specific safeguards—model validation, sandbox testing, human‑in‑the‑loop gates, continuous monitoring, audit trails, and emergency shutdowns—mirroring the EU AI Act’s risk‑based framework and the NIST AI RMF MAP function.
Step 3: Implement documented human oversight and emergency protocols – For high‑volume, time‑critical operations, the recommended model is “humans on the loop, not in the loop.” Emergency shutdown mechanisms are mandatory for safety‑critical environments.
Step 4: Demand vendor transparency and strengthen contractual protections – Contracts should require disclosure of training data sources, model weightings, and testing protocols; establish annual or quarterly assessment cadences; negotiate liability structures, indemnification, audit rights, and incident notification timelines; and develop contingency plans for vendor failure or model degradation.
Step 5: Design a scalable governance architecture – Foley & Lardner suggest an automation maturity path: Crawl (inventory AI assets), Walk (automate governance steps like drift detection), Run (embed governance into every workflow). Embedding controls upstream ensures new AI capabilities inherit governance by default. Digital twin and sandbox environments can support pre‑deployment testing, while building AI literacy is emphasized, as the EU AI Act requires operators to interpret AI outputs critically.
AI governance in manufacturing is an evolving operational function that must keep pace with technology. Manufacturers that adopt the five steps now will be better positioned to reap operational benefits while mitigating legal risk as autonomous capabilities expand. Foley & Lardner’s Manufacturing, Supply Chain, and Artificial Intelligence teams offer tailored assistance in designing and implementing AI governance programs. The guidance also notes that regulatory frameworks such as the EU AI Act and NIST AI RMF will increasingly mandate documented governance for high‑risk AI systems.
