How AI Agents Accelerate Response Times in Slow-Acting Supply Chains
Supply chain disruptions cost businesses around $184 billion in 2025, according to the J.S. Held Global Risk Report. Despite improvements in detection speeds, the problem remains that organizations often struggle to act quickly on the issues identified.
Traditionally, tools such as visibility platforms, control towers, risk scores, and digital twins have revolutionized the way supply chain problems are reported, allowing companies to notice delays and other issues much faster than before. However, this speed in detection has not translated effectively into rapid mitigation actions. The process is still bogged down by the need for human intervention, which leads to delays in decision-making even when the problem is clear.
When executives are asked about AI spending in supply chain management, they often cite spending on demand sensing and ETA predictions among others. While these tools effectively reduce forecasting errors and improve tracking of shipments, they do not address the time lost between identifying an issue and taking necessary actions—this gap is what contributes to the financial loss reported.
Surveys show that supply chain teams often spend a significant portion of their time—28% according to a 2026 Knosc survey—addressing disruptions, predominantly investigating past events instead of planning immediate corrective actions. This points to a persistent inefficiency in supply chain operations, where many organizations lack a formal AI strategy or sufficient authority delegated to AI tools to take immediate actions.
Most systems are designed around a ticket-based workflow where AI generates alerts that wait for human approval before any real work can be done. This often results in lost opportunities, as timely actions become obsolete while waiting for human planners, who are already occupied with other tasks.
To address this, companies must shift their focus towards allowing AI agents to take bounded actions. This involves granting pre-approved authority for certain types of decisions, allowing for rapid responses to common issues within established limits. For instance, if a carrier’s expected time of arrival (ETA) is missed, the system could automatically seek alternative carriers that fall within predefined budgetary constraints.
For this change to be effective, organizations need to formalize decision-making policies beyond anecdotal knowledge. Clear directives must be documented so AI agents can act according to these rules without human intervention. Furthermore, there must be system integration that allows AI to engage in machine-initiated transactions, holding both agents and human stakeholders accountable for the decisions made.
In a competitive landscape, the differentiation will arise not from merely deploying AI but from optimizing the speed and efficiency of decision-making processes. Companies capable of enabling AI to independently make certain calls will be able to respond to disruptions far more effectively than those still reliant on human gatekeeping, effectively turning detection into action faster than ever before.
For more information, refer to JD.com’s expansion into logistics with AI.
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