Five Steps Every Manufacturer and Supply Chain Manager Should Take to Build a Scalable AI Governance Program

Key Takeaways:

• AI governance in manufacturing is an operational and regulatory imperative. With a rapidly shifting technological and regulatory landscape, manufacturers deploying AI systems without formal governance programs face compounding legal, operational, and compliance challenges.
• Governance should be calibrated to risk and autonomy. Not all AI systems require the same level of oversight. A predictive maintenance alert poses different risks than an autonomous procurement agent executing purchase orders. Organizations need tiered control frameworks that scale governance proportionally to the risks attendant to each AI system.
• Stay flexible, scale responsibly. Organizations that build governance programs designed for adaptability, rather than today’s capabilities alone, will be positioned to scale AI responsibly as agentic systems, world models, and multi-agent ecosystems reshape manufacturing operations.

The rapid deployment of agentic artificial intelligence (AI) across manufacturing and supply chain operations is creating a widening gap between system capability and organizational oversight. AI agents are now or soon could be autonomously negotiating procurement terms, executing purchase orders, adjusting production schedules, and making quality control decisions, often with limited or no human intervention. Yet a PricewaterhouseCoopers 2026 survey found that only 37% of operations leaders are comfortable assigning AI agents to execute full end-to-end processes, and only 27% have fully embedded an AI strategy across business units.

The consequences of this governance vacuum are not hypothetical. Consider a Tier 1 automotive supplier that deploys an AI-driven demand forecasting agent across its stamping operations. The agent autonomously reduces purchase orders to a steel supplier based on a flawed demand signal, triggering a contractual minimum-volume dispute, expedited freight costs to cover the resulting shortage, and/or an OEM penalty for missed delivery windows. Without documented governance structures, the manufacturer may not be able to demonstrate what oversight was in place, reconstruct why the agent made the decision, or establish whether the vendor or the deployer bears responsibility.

This article, part of Foley’s 2026 AI in Manufacturing & Supply Chain Series, outlines five steps manufacturers and supply chain managers should take now to build governance programs that manage today’s risk exposure while scaling alongside rapidly advancing AI capabilities.

Step 1: Establish a Cross-Functional AI Governance Committee

Manufacturers should establish a formal AI governance committee, with executive sponsorship and representation from operations, IT, legal, compliance, safety, and procurement. The committee should define its charter, decision-making authority, meeting cadence, and reporting obligations to the board.

To be clear, this is not a technology committee. It is a risk governance body. AI-specific risks cut across cybersecurity, operational, regulatory, and reputational categories and should be formally incorporated into the organization’s enterprise risk management framework rather than siloed within IT. The committee should also maintain a comprehensive AI inventory documenting every AI system in use, including its purpose, data access, autonomy level, and risk classification.

Step 2: Classify AI Systems by Risk and Apply Tiered Controls

Not every AI deployment demands the same oversight. A simple anomaly detection tool monitoring equipment vibration patterns requires different governance than an autonomous agent executing procurement transactions. Manufacturers should implement a tiered control framework calibrated to autonomy and consequence severity. For example:

• Tier 1 (Advisory): AI provides recommendations; humans decide. Standard model validation and periodic review are sufficient.
• Tier 2 (Semi-Autonomous): AI recommends and humans approve. Pre-deployment sandbox testing, human-in-the-loop approval gates, and regular drift monitoring are required.
• Tier 3 (Fully Autonomous): AI decides and acts without human intervention. Continuous monitoring, governance agents, emergency shutdown mechanisms, full audit trails, and agent-to-agent monitoring protocols are essential.

This or similar classification should be documented for every AI system before deployment, a requirement that aligns with emerging frameworks, such as the EU AI Act’s risk-based approach and the NIST AI RMF’s MAP function.

Step 3: Implement Documented Human Oversight and Emergency Protocols

Organizations must develop and document a human oversight structure before deploying AI systems that make or influence consequential decisions. This includes designating who has authority to override AI decisions, defining what triggers escalation, and establishing how override events are logged.

For high-volume, time-critical manufacturing operations, the appropriate model is often “humans on the loop, not in the loop,” where agents act and humans monitor and override when necessary, rather than per-decision approval that would negate the efficiency gains AI provides. Emergency shutdown mechanisms are non-negotiable for AI systems operating in safety-critical environments. For a deeper analysis of how autonomous AI decisions create liability exposure, see our prior article on agentic AI liability in supply chain operations.

Step 4: Require Vendor Transparency and Strengthen Contractual Protections

Most manufacturers do not build their AI systems in-house. Each third-party dependency introduces risk that standard SOC 2 reports and generalized questionnaires are not designed to assess. AI vendors update models, change data sources, and modify training methodologies, often without explicit notification.

Manufacturers should where practical:

• Contractually require vendors to disclose data sources used in model training, the factors and weightings influencing outputs, and testing protocols.
• Establish recurring assessment cadences (at minimum annually, quarterly for critical systems) and never rely on outdated assessments as evidence of ongoing compliance.
• Negotiate liability structures that reflect actual operational risk, including indemnification provisions, audit rights, and incident notification requirements with defined response timelines.
• Develop contingency plans for vendor failure or model degradation, including manual fallback procedures.

Step 5: Design Governance Architecture for Scalability

Crucially, the governance challenge is accelerating. World foundation models are enabling manufacturers to simulate entire factory environments. Multi-agent systems are multiplying non-human identities across enterprise workflows. Organizations that build governance only for today’s capabilities will find themselves perpetually behind their own risk exposure.

To account for the scalability needs of AI governance systems, manufacturers should:

• Follow an automation maturity path, such as: “Crawl” (inventory AI and data assets) → “Walk” (automate governance steps like drift detection and compliance checks) → “Run” (embed governance into every workflow).
• Embed controls upstream so new AI capabilities inherit governance by default.
• Leverage digital twin and sandbox environments for pre-deployment governance testing.
• Build organizational AI literacy systematically. The EU AI Act requires it, and operators must be equipped to critically interpret AI outputs rather than deferring uncritically.

Building Governance That Keeps Pace

AI governance in manufacturing is not a one-time compliance exercise. It should instead become a permanent operational function that evolves alongside the technology it oversees. Manufacturers that establish scalable governance programs now will be positioned to capture AI’s operational benefits while maintaining the oversight, documentation, and contractual architecture necessary to manage legal risk as autonomous capabilities expand.

Foley & Lardner’s Manufacturing, Supply Chain, and Artificial Intelligence teams are available to help organizations design and implement AI governance programs tailored to manufacturing and supply chain operations. We welcome the opportunity to discuss how these issues may affect your company’s operations.

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