Before AI Places the Order, Someone Must Be Able to Explain It
An AI recommendation to replenish inventory can look straightforward: buy a specified quantity, from a particular supplier, by a certain date. Approving it commits working capital and may affect warehouse capacity, production availability and customer service. The operational question is whether the planner can inspect the assumptions behind that recommendation before it becomes an order, and whether the business can reconstruct what happened afterward.
That question becomes more consequential when AI moves beyond advice and begins executing connected tasks. A recommendation can be challenged before approval. An automated process may already have investigated an exception, selected a response and passed instructions into another system. Comments provided to The Supply Chainer by executives at Blue Ridge Global and TraceLink identify two related requirements: evidence that makes an individual decision reviewable, and operating measures that show whether automation improves the process around it.
The Recommendation Needs an Evidence Trail
For Ken Carpenter, Chief Product Officer at Blue Ridge Global, the planner’s ability to interrogate a recommendation is a basic requirement. “A planner should be able to understand and question any recommendation before approving it. That is their right as a planner,” Carpenter said. He argues that an inventory recommendation should expose its forecast, lead time, safety stock and intended service level. Planners should also be able to see how factors such as seasonality, pricing and promotions contributed to the forecast.
Those details matter because the same proposed order can reflect very different operating assumptions. A higher inventory target might follow a change in demand, a longer replenishment lead time or a decision to protect a higher service level. Without that distinction, an approver can see the quantity while remaining unable to assess the reason for buying it.
Carpenter said adjustments and automated actions should remain traceable and explainable. His comments establish a standard for evaluating a system; they do not, by themselves, demonstrate how consistently that standard is met in customer deployments.
Data Quality Comes Before Automated Orders
Carpenter places the first implementation challenge upstream of the planning screen. ERP records and other existing data must form a validated foundation for forecasts, lead times, order recommendations and operating measures. “Data quality checks resolve gaps, such as missing lead times, before anything is forecast or any order is generated,” he said.
This makes data validation part of the decision control. An explanation may describe an order convincingly while relying on an incorrect input. Reviewing the reasoning therefore also requires access to the information on which it depends.
Service targets introduce another layer of accountability. “Finance belongs in that conversation from day one, because a service level target is a working capital decision,” Carpenter said. A planning team cannot assess that trade-off solely through forecast performance. Finance, operations and planning need an agreed basis for deciding how much inventory the business is prepared to carry to support its delivery commitments.
Measure What Happens After Approval
The adoption backdrop makes those controls increasingly relevant. McKinsey’s August 2026 State of AI survey found that 40% of respondents from organizations with annual revenue above $1 billion reported scaling AI agents in at least one business function. That is an enterprise-wide finding, rather than a measure specifically of supply chain deployment.
For Paul Cianciolo, Chief Commercial Officer at TraceLink, evaluation should focus on the interval between implementation and useful operational results. “Two important early indicators are time-to-value—from implementation kickoff to productive use—and reductions in the time and manual effort required to resolve operational exceptions,” Cianciolo said.
His response followed questions about TraceLink’s establishment of dedicated teams for its compliance and agentic business process activities. He said the company works with customers to establish baselines covering workforce productivity, exception-resolution times, service levels, working capital and operational risk.
The distinction between a baseline and a claimed improvement is essential. Fewer manual interventions may indicate a more efficient process, but managers also need to establish whether exceptions were resolved correctly and whether service or risk deteriorated elsewhere. TraceLink’s response identified intended measures without supplying quantified customer results for the organizational changes.
Shared Systems Still Need Named Accountability
Cianciolo said TraceLink’s two businesses operate on a common platform and network foundation, supported by coordinated customer engagement and executive governance. “This shared architecture enables customers to leverage their existing network connections, compliance investments, and operational information as they expand into new Agentic Business Processes,” he said.
A shared platform can support consistent records and controls. The management test remains whether responsibility is clear when a process crosses functions: who approves an exception, who can stop an action, and who investigates an unexpected result.
That concern also appeared in previous Supply Chainer coverage. In their opinion column on AI compliance, Prof. Shlomit Yanisky-Ravid wrote: “Human oversight requires more than an approval button.” They argued that employees need information, escalation procedures and authority to challenge consequential actions.
For supply chain leaders, the practical purchasing test follows the decision from beginning to end. Can the planner inspect the inputs? Can an authorized employee challenge the proposed action? Does the record show what was approved and executed? And can the business compare the outcome with a credible operational baseline? An automated order may take seconds to issue. Its justification, ownership and consequences need to remain accessible long after that moment.
