When Supply Chains Cannot Move, Decisions Must Move Faster

Alex Saric is the chief marketing officer at Ivalua.

getty​As conflicts reroute shipping lanes and tariffs change overnight, disruption thousands of miles away now reaches production lines within days.

What separates the organizations that successfully manage supply chain disruption from those that lose ground is how quickly they can see what’s coming. Those that work out which products are about to run short, and have a contingency plan in place, hold on to their customers. Those still piecing the picture together days after the disruption are deciding a week late, and their customers go elsewhere.

With difficult geopolitical conditions showing no sign of easing, organizations are turning to agentic AI to get ahead of the next disruption. But these tools are only as good as the data underpinning them, which needs to be unified, accurate, current and accessible. For many, the reality of their data shortcomings arrives too late.

Some sectors face more of this risk than others. Manufacturing, energy and technology depend on components and raw materials produced in a handful of locations or, in some cases, a single location. One example is China, which produces around 80% of the global supply of battery cells. Suppliers in other markets such as Korea and Japan can take on that work, but shifting production to them takes time.

Data shows that 70% of organizations have had between one and five suppliers fail in the past 12 months, and four in 10 believe just one more issue would tip them into crisis. The obvious fix, finding another supplier, often takes longer than the disruption lasts. Approving a new supplier for a precision component means sample testing, audits and often customer sign-off, which can run for months, so a closure lasting weeks can stop production outright.

Procurement teams are also being asked to cover more. More suppliers, more countries and more regulations, without a matching increase in the people watching them. Checking a supplier’s financial health and compliance status remains time-consuming, while the number of suppliers continues to grow.

The challenge is accurately working out what a disruption will do. When a shipping route closes, most organizations will find out quickly. What takes far longer is establishing which suppliers sit behind that route, which contracts depend on them and which customer orders are now at risk.

This is an area where agentic AI can add significant value. Agents can model supply chain scenarios before anything goes wrong, so alternative sources are identified and qualified while there is no pressure to decide. When disruption hits, agents can then analyze the orders exposed to the delay, identify the materials that will run short and point to alternatives that have already cleared approval. That analysis takes minutes rather than days.

Agents can also trace exposure through the tiers of a supply base that organizations cannot usually see into. If a carmaker’s supplier buys steel from a region now facing tariffs, an agent can surface the exposure while there is still time to act. The response might be approving another source or working with engineers to redesign a product’s specifications.

However, agentic AI does not work without clean and consistent records. An agent reading from multiple systems that use a different name for the same vendor may not accurately determine if it is looking at one supplier or three.

The data challenge is a reality for many organizations, as half admit their supplier data is not ready for AI. Consolidating supplier, contract and spend data into a single source of truth turns an agent from a promising tool into a usable one. Combining this with company policies and purchase history provides the remaining context that enables agents to work effectively.

Given this context, agentic AI changes the speed at which that judgment can be made. Whether to switch suppliers, absorb a higher cost or tell a customer their order has moved remains a decision for the organization, but one that can now be made in hours rather than days.

Supply chain disruption has stopped being an unusual event and become the everyday climate organizations operate in. High-risk sectors cannot move the plants or shipping lanes they depend on, so the advantage lies in how quickly an organization understands what a closure costs and what to do about it.

The long-term damage is often decided within the first few days. Orders that ship late, customers who go elsewhere and working capital locked into the wrong inventory are the losses that outlast the disruption itself.

Agentic AI can close the gap between disruption happening and an organization understanding the impact. Ultimately, at a time when speed is of the essence, organizations cannot afford to wait until the disruption hits; otherwise, they will still be searching for answers long after it has passed.

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