AI Agents: The Ultimate Problem Solvers

Supply chain disruption is a significant challenge for businesses, costing around $184 billion in 2025, as reported by the J.S. Held Global Risk Report. Despite this staggering figure, the focus has mainly been on faster detection rather than faster action.

Traditionally, disruptions in the supply chain have been treated as inevitable occurrences, leading to costs that follow suit. However, viewing these disruptions as a product specification sheds light on the need for an operating model that can identify issues much earlier than before. Unfortunately, the process of addressing these issues often involves manual steps such as opening a ticket, organizing a call, and entering data into multiple systems.

Over the past decade, technologies such as visibility platforms, control towers, risk scores, digital twins, and exception dashboards have been instrumental in reducing the time between an event and awareness of it. Despite these advancements, there is still a significant lag between awareness and taking commercial action.

Chief supply chain officers have invested heavily in AI tools such as demand sensing, ETA prediction, supplier risk scoring, and inventory optimization, which have proven to be effective in reducing forecast errors and flagging potential delays. However, the real challenge lies in the decisions that need to be made after an issue is identified, such as whether to expedite an order, split it, retender a lane, or consolidate movements.

Surveys have highlighted that supply chain teams spend a substantial amount of time responding to disruptions, with a focus on investigating past events rather than proactively shaping future outcomes. While AI remains a strategic priority for many logistics executives, the actual financial impact of these investments is often limited.

Most current deployments in supply chain management are centered around the concept of a “ticket” – where recommendations are turned into alerts, which then become work items for human planners to address. This workflow, while efficient in providing insights, often results in delays in taking action due to the need for human intervention.

The next model for supply chain management involves pre-authorizing a set of actions that can be executed by agents when specific conditions are met. By delegating decision-making authority to these agents within predefined limits, companies can streamline their response to disruptions and reduce cycle times.

To achieve real change in supply chain management, decisions need to be documented as policies rather than relying on tribal knowledge. Execution systems must be able to accept machine-initiated transactions, and accountability must be tied to the actions taken. Companies that embrace bounded action and empower automated agents to make decisions within set parameters will gain a competitive advantage in responding to disruptions.

In conclusion, the key to addressing supply chain disruptions lies in enabling automated agents to take bounded actions without waiting for human intervention. By combining insights with the authority to act, companies can improve their response times, reduce costs, and enhance overall efficiency in their supply chain operations.