As artificial intelligence reshapes global supply chains, the challenge is no longer simply adopting the technology. It is making sure AI is used in ways that protect the business while improving procurement, logistics, trade compliance, and supplier management.
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Supply chains underpin the global economy, and AI is rapidly changing how companies manage them. From supplier screening and procurement to inventory planning and logistics, AI is helping organizations make faster decisions and respond more quickly to disruptions. But as AI moves beyond supporting decisions to making them, companies face greater operational, financial, and legal risks if those systems are not properly governed.
Today's supply chains already operate in an increasingly complex environment. Disruptions from natural disasters, pandemics, cyberattacks, shifting tariffs, and expanding economic sanctions all require companies to make informed decisions quickly. Suppliers must be evaluated for potential ties to terrorist organizations, cybercrime, forced labor, corruption, and other regulatory concerns, as well as traditional measures such as quality, reliability, and cost. AI can help navigate this complexity, but companies remain responsible for the decisions it makes on their behalf.
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The scale of the risk is already becoming clear. Industry reporting shows that more than 70% of organizations experienced at least one material cybersecurity incident in the past year, according to SecurityScorecard. Kaspersky reports that 31% of enterprises experienced a supply chain attack in the past 12 months, while 30% reported exposure to AI-powered attacks.
While AI adoption has accelerated, governance has not always kept pace. That gap is creating a new risk landscape as AI systems increasingly perform tasks that once required direct human oversight.
The liability problem
One of the biggest changes is the rise of AI agents that can independently perform tasks such as selecting suppliers, approving shipments, placing purchase orders, or rerouting freight. These systems move beyond recommending actions and instead make decisions on behalf of the business.
That raises an important question: who is responsible when AI gets it wrong?
“The question is no longer whether AI can improve supply chains, but whether organizations can show they are governing those systems responsibly.”
Consider a retailer whose AI system autonomously selects a supplier that fails to meet regulatory requirements. Even if no employee approved the decision, the retailer may still face enforcement actions, penalties, or contractual disputes resulting from that choice.
Regulators increasingly expect companies to demonstrate that AI operates under meaningful human oversight with documented policies, testing, and audit trails. The question is no longer whether AI can improve supply chains, but whether organizations can show they are governing those systems responsibly.
Regulators are focusing on companies using AI
The regulatory landscape suggests responsibility is increasingly falling on the organizations deploying AI rather than the companies that developed it.
The European Union's AI Act adopts a risk-based framework that places obligations on both developers and deployers of AI systems. Under that approach, companies using AI in their supply chains are responsible for ensuring those systems comply with applicable requirements.
The United States has taken a different path, but the practical outcome is similar. Rather than creating a single AI law, agencies such as the Federal Trade Commission have made clear that existing consumer protection laws still apply to AI-driven business decisions. Companies remain responsible for exercising reasonable diligence when selecting, testing, and overseeing AI systems, including those provided by third-party vendors.
The message from regulators is straightforward: using AI does not reduce an organization's compliance responsibilities.
Building better AI governance
Organizations should begin by understanding exactly where AI is being used across the supply chain. Many companies have deployed AI in multiple departments without maintaining a complete inventory of those systems. Without that visibility, it becomes difficult to monitor performance, identify failures, or demonstrate appropriate oversight.
Companies should also establish clear guardrails around AI-driven decisions. High-risk activities such as approving suppliers, executing contracts, or making significant purchasing decisions should include defined approval thresholds and human review. Organizations should also regularly test AI outputs to identify model drift, bias, or declining performance before problems spread through the supply chain.
“Companies that pair AI with thoughtful governance, meaningful human oversight, and strong documentation will be better positioned to reduce risk while capturing the benefits of greater automation.”
Equally important is preparing for failures. Incident response plans should address situations in which AI contributes to a supply chain disruption, a compliance violation, or a cybersecurity event. Organizations should also define contractual responsibilities with AI vendors so liability is clearly understood if problems occur.
Documentation matters
Strong documentation remains one of the most effective ways to reduce regulatory and litigation risk.
Companies should maintain records of human reviews, approval thresholds, model testing, vendor due diligence, and any decisions that override AI recommendations. If regulators, customers, or business partners later question an AI-driven decision, those records can help demonstrate that appropriate governance procedures were followed.
AI will continue to play a larger role in global supply chains, but accountability will remain with the businesses that use it. Companies that pair AI with thoughtful governance, meaningful human oversight, and strong documentation will be better positioned to reduce risk while capturing the benefits of greater automation.
Originally published by Supply Chain 24/7.
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