Opinion: AI Compliance - The Missing Link in Your Supply Chain
A shipment disappears from the schedule. A trusted supplier suddenly receives a poor risk score. Asked why, the AI agent running the operation explains: “This is the shortest route to the goal.” This hypothetical scenario raises a practical question: whose goal, and at what cost? Artificial intelligence promises faster, smarter supply chains, with agents forecasting demand, evaluating suppliers, adjusting inventories, and coordinating deliveries. Yet an instruction to cut costs says little about which risks are acceptable. When companies delegate decisions without defining boundaries and accountability, efficiency can become a fast lane to costly mistakes.
An effective AI corporate compliance program must therefore stand at the center of supply chain strategy. Its purpose is to establish how automated decisions are assessed, supervised, and corrected. Those responsibilities become more consequential as AI systems gain the ability to act across interconnected operations.
How One AI Error Can Travel Across the Supply Chain
Imagine a procurement system trained on historical purchasing data that repeatedly favors established vendors, overlooking smaller suppliers with limited digital records. A logistics tool might select cheaper routes without adequately considering safety. A generative AI assistant could produce an inaccurate contract summary that employees accept without checking.
One flawed output can trigger a chain reaction across purchasing, production, and distribution, reaching customers before anyone identifies the original error. The consequences may extend beyond the company operating the system to suppliers, logistics partners, and workers. Supply chains connect businesses, and AI increasingly connects their decisions, making clear responsibilities essential.
Start With Risk Assessment and an AI Inventory
The foundation of an effective corporate compliance program is a risk assessment. Companies should ask what could go wrong, who could be harmed, and how far the damage could spread. That assessment should consider operational disruption, privacy, cybersecurity, discrimination, contractual obligations, and worker safety, prioritizing risks by their likelihood and potential severity.
Companies must also map where AI operates, including tools embedded in vendor software. They should identify each system’s data sources, users, permissions, and influence over decisions. A system that recommends a supplier presents a different level of exposure from one that can place an order, change delivery instructions, or commit company funds.
Assign Owners and Strengthen Contracts
Every consequential AI application needs a designated owner with the expertise, resources, and authority to intervene. Responsibility should be clear across procurement, operations, legal, compliance, and technology teams. Saying “our vendor handles that” leaves unanswered questions about who can investigate a failure, stop an action, or coordinate a response.
Contracts should address data use, confidentiality, security, performance testing, incident reporting, and cooperation when problems arise. They should also establish what happens when a model changes, a service fails, or an output causes harm. Resolving these questions before a crisis gives companies a clearer basis for managing disruption and working with their providers.
Give Human Oversight Real Authority
Human oversight requires more than an approval button. Employees need training, relevant information, and clear escalation procedures to assess recommendations and recognize warning signs. They must also have the authority to challenge outputs and pause consequential actions when circumstances warrant intervention.
Oversight should reflect the system’s powers and the potential consequences of its decisions. A low-impact recommendation may require different controls from an automated decision affecting worker safety or a critical supplier. Companies should define these boundaries in policies and procedures so employees understand when review is required and who can authorize an exception.
Measure Errors and Resilience Alongside Savings
Performance measurement should extend beyond speed and cost reductions. Companies should track errors, supplier complaints, security incidents, and effects on workers, alongside operational gains. Testing systems against disruption can help reveal weaknesses that routine performance measures overlook.
Yesterday’s accuracy cannot guarantee tomorrow’s resilience. Changes in suppliers, data, operating conditions, or the AI system itself can alter the risks. Auditing and monitoring should therefore inform updates to controls, training, and oversight throughout the system’s use.
Make Compliance an Ongoing Management Responsibility
Boards and executives should ask whether they could reconstruct a damaging AI decision, identify the responsible parties, and respond effectively. Answering that question requires preparation across policies, contracts, records, and escalation procedures. It also requires leadership to treat AI compliance as part of running the business.
An effective program begins with risk assessment and continues through policies, procedures, and controls; designated responsibility; due diligence; training; auditing and monitoring; discipline and incentives; and regular updates. Companies should revisit these elements as applicable regulations, emerging standards, and AI capabilities evolve. The competitive advantage will belong to businesses that combine intelligent automation with accountable leadership, protecting the trust on which their supply chains depend.
Disclosure: This opinion column was written by Shlomit Yanisky-Ravid and Carole Basri, co-authors of A Guide to Creating an Effective AI Corporate Compliance Program. Yanisky-Ravid has been a Visiting Professor at Fordham Law since 2012 and affiliated with Yale Law School’s Information Society Project (ISP) since 2011. Her research focuses on the legal and ethical aspects of artificial intelligence and intellectual property. The views expressed are those of both authors and do not necessarily reflect the views of The Supply Chainer.
