Artificial intelligence has moved from recommending actions to executing them. A model that once flagged a demand anomaly for a planner to act on is now autonomously rerouting shipments, adjusting procurement volumes and rebalancing inventory without waiting for approval.

According to Gartner, 60% of supply chain disruptions will be resolved without human intervention by 2031. The same survey of 509 supply chain leaders found that 55% believe agentic AI will reduce their need for entry-level hires.

Adoption is outpacing the playbook for managing it. Smaller staffs overseeing faster, more autonomous systems make the governance question even more consequential. AI decisions often fall into an accountability vacuum shared by tech teams and business units. However, ownership should always mirror impact. If AI disrupts the supply chain, the head of supply chain is responsible. 

Closing this accountability gap is a matter of three specific disciplines:

Picking the right use case. Heads of supply chain can’t review every AI use case deployed across the organization. AI adoption is accelerating across planning, sourcing, transportation and warehouse operations, and trying to audit every model creates bottlenecks that defeat the purpose of autonomous execution.

The solution is a tiered approach. Classify use cases by the risk their failure poses to the business, such as supply chain disruption, lost revenue or increased cost driven by a flawed AI-executed decision.

High-priority use cases get direct oversight. A model for governing truckload versus less-than-truckload decisions every day for the entire distribution network has a material gross margin impact and warrants chief supply chain officer-level scrutiny. Sending a half-loaded trailer out of the yard is expensive; canceling the driver is expensive, and the failure mode is immediate and costly.

Lower-risk use cases can be democratized. A model selecting carton sizes for outbound orders might occasionally pick a carton that’s larger than optimal. It’s wasteful, but not mission-critical and easily corrected. Teams can deploy and iterate on those use cases without centralized review. The framework focuses scrutiny on where failure causes the greatest loss. Let everything else run.

Moving beyond explainability to traceability. Most organizations employ an AI model with some form of explainability. It can tell you that it adjusted an inventory allocation because it detected regional demand shifts, or that it rerouted a shipment due to a port disruption. That’s useful, but not enough.

Traceability goes further to capture specific data inputs and the business logic a model applies to arrive at a decision. A planner can go back weeks later and reconstruct exactly why the model made a given call.

For instance, what if an inventory allocation decision that looked sound at the time resulted in stockouts in one region and excess inventory in another four weeks later? Traceability allows humans to interrogate the original logic, understand what went wrong, and correct it.

Apply the approach in tiers, matched to use-case priority. For high-impact decisions, record AI decision logic in immutable audit trails. A 2025 study proposed a blockchain-based framework for exactly this, logging each inference’s key inputs, model ID, and output to a permissioned ledger. For lower-stakes decisions, lighter-touch traceability is sufficient.

The approach aligns with where regulators are heading. The EU AI Act, which began phasing in last year, requires risk management systems, technical documentation and human oversight for high-risk AI systems. Supply chains that cross into EU markets should evaluate whether their AI decision-making triggers compliance obligations.

Building guardrails that check the model against itself. The one to implement first is a variance trigger, an automated alert that flags outputs exceeding expected ranges. If a delivery scheduling model assigns a route that falls outside predefined thresholds, such as too many stops in a window or routing that bypasses a regional hub, it gets surfaced for human review before execution.

The next step is to throttle autonomy, because diving into the deep end has risks. You wouldn’t want to expand a model’s decision authority overnight. If it previously evaluated five variables and now can process 15, validate its outputs against actuals at that level before expanding to 30. Each stage should have a defined accuracy threshold the model must hit before it earns broader scope.

The third guardrail is running secondary models, potentially a previous-generation version, against the same data as the primary and configured to alert when the two diverge significantly. The practice, known as challenger testing, originated in financial services and machine-learning operations for validating predictive models in production. Triggering alerts when the two models diverge beyond an acceptable range acts as a built-in check against model drift, catching degradation before a flawed output drives a real-world routing, allocation or procurement decision.

The head of supply chain still owns every outcome, whether a human made the call or a model did. That hasn’t changed. But the volume and velocity of those decisions, and the fact that many of them now happen without a human in the loop at all, complicate that role. Closing the accountability gap makes ownership operational rather than theoretical. It just requires a system that can keep pace with the technology it’s meant to oversee.

Arun Kumar is global head of product and industry practice at Altimetrik.