Hunton Retail Law Resource

Retailers continue to deploy AI throughout their enterprises, with particular emphasis on enhancing operational productivity. One area where AI can improve operational efficiency is predictive inventory and logistics planning. Yet the technology is not without risk, and businesses should understand both the challenges associated with deploying AI and the ways they can mitigate supply chain and logistics disruptions through insurance and other forms of risk transfer when the technology fails to perform.

The idea of using technology to monitor sales patterns, seasonality, and logistics data has been around for decades. What has changed is the ability of retailers to use AI to evaluate that information against real-time trends and behavioral patterns, allowing them to forecast supply and demand more proactively and optimize product availability, space utilization, and labor planning. In that sense, AI offers clear advantages to retailers using predictive inventory models: it can improve forecasting accuracy, identify patterns that traditional analyses may miss, and automate large portions of the process. Unsurprisingly, many retailers have already begun employing AI in predictive inventory and supply chain operations, while others are actively considering it.

At the same time, the growing use of AI-driven inventory and logistics tools introduces a new category of risk that calls for updated mitigation strategies. When hundreds of thousands of decisions are being made daily, often in real time, the potential for a cascading impact is significant. Because those decisions are increasingly made at a granular level, the opportunity to catch a “judgment” error before it results in major disruption becomes much smaller. That does not mean the technology is inherently problematic. Rather, even well-designed systems can produce harmful outcomes with real business consequences if issues are not identified early.

These failures can take several forms. Forecasting errors, for example, can lead to stockouts or overstock backlogs when AI misreads data or draws faulty conclusions, causing retailers to lose product, waste resources, or miss customer demand. The consequences can extend beyond immediate sales losses to market share erosion and damage to brand perception. In addition, predictive supply chains depend heavily on connected systems, making them vulnerable to cyberattacks, model failures, and service provider outages that can trigger operational losses or liability exposure. Integration failures create another point of weakness, because interconnected systems must communicate accurately and consistently; when they do not, retailers may face distorted inventory visibility, mistimed shipments, and broader supply chain disruption. Reliance on third-party infrastructure adds still more uncertainty, particularly where cloud-service outages or vendor-side failures can disrupt systems far beyond the retailer’s direct control. And perhaps most concerning, overreliance on automation without adequate redundancy can magnify losses, leaving companies with few practical alternatives when a systemic failure occurs.

As those operational risks increase, the need for targeted risk mitigation becomes more urgent. Insurance programs must evolve alongside the technology they are meant to protect. Insurers are increasingly scrutinizing whether legacy property, business interruption, D&O, E&O, cyber, or crime policies actually respond to AI-related losses. A property insurer may argue that no physical loss occurred. A cyber insurer may contend that the failure was internal rather than caused by a malicious actor. D&O and E&O insurers may dispute whether there was an insured wrongful act at all. Complicating matters further, insurers may invoke one of the many new so-called AI exclusions now being added to traditional policies.

For that reason, general counsel and others responsible for risk mitigation should assume that coverage may be uncertain even when the business impact is obvious. Insurance is only one layer of protection. Contractual risk transfer can be equally important, particularly through indemnification and insurance provisions in vendor, supplier, software, and technology agreements that allocate risk to third parties. Retailers can further reduce vendor-related AI exposure by requiring governance standards around AI use and ensuring that indemnifying parties maintain adequate protection against AI-related risks.

Ultimately, as business systems and assets evolve, risk mitigation strategies must evolve with them. Retailers should understand where coverage currently exists, where gaps remain, and how those gaps can be addressed going forward. In practical terms, that means mapping potential loss scenarios tied to predictive inventory and automated logistics systems, engaging brokers and relevant stakeholders across the organization, assessing whether current policies are likely to respond, considering manuscript coverage or other risk-transfer solutions such as indemnification agreements, and working with experienced coverage counsel to evaluate insurance gaps. Taken together, these steps can help retailers capture the operational benefits of AI while better preparing for the disruptions that may follow when the technology falls short.