Every WMS vendor claims AI. The term appears without a consistent definition across the category. This article offers a practical framework for evaluating what AI in warehouse management means operationally. The three-level framework used here is Stackbox's own categorisation, developed to help operations teams compare vendor claims. It is not an established industry taxonomy.
Much of what is described as AI in legacy WMS platforms is rule-based logic: if zone A is over capacity, route to zone B. Rules can be effective and are a legitimate part of warehouse management, but they do not learn and do not adapt when underlying conditions change. The label AI for this category is primarily a marketing choice.
Some platforms apply genuine machine learning to specific tasks: slotting optimisation based on historical pick frequency, demand sensing for replenishment, or route optimisation for pick paths. These models do improve over time with operational data.
Based on publicly available vendor documentation, both SAP EWM and Infor WMS have Level 2 capabilities. SAP EWM has basic ML-based slotting and resource optimisation. Infor WMS has AI-powered inventory and order routing. Readers should verify current capabilities directly with each vendor.
The characteristic constraint of Level 2 is task isolation: models optimise individual tasks based on historical data but do not coordinate across resource types in real time based on current system state.
In Stackbox's framework, Level 3 describes a system that coordinates multiple resource types simultaneously, allocates tasks based on real-time system state rather than batch plans, and adapts continuously as conditions change. Stackbox describes its own platform as Level 3.
Multi-Agent Orchestrator. The SBX Multi-Agent Orchestrator manages task allocation across all resource types simultaneously based on a continuously updated model of warehouse state: zone occupancy, resource availability, order SLA status, and conveyor capacity. It does not work from a static task queue set at the start of a shift.
In the Fortune Top 50 FMCG deployment, the orchestrator achieved 100% system-guided task allocation across 29 Indian DCs, with no manual supervisor intervention required for routine task assignment. The full case study, including the 55-site global rollout currently in active pipeline, is covered in the FMCG case study article.
AI-Based Product Slotting. Stackbox's slotting engine is an AI-based model using three factors: pick frequency, order volume, and product affinity. The engine generates slotting recommendations based on those inputs. Those recommendations are applied through normal putaway and replenishment workflows. This means the system optimises where stock should be positioned over time, it does not move physical stock continuously on its own.
Computer Vision for QC. Stackbox includes high-speed image processing at packing stations to detect mis-picks and mis-sorts. Items are compared against expected profiles at pick time. The capability is described at product level; operational deployment scale is not publicly disclosed.
Real-Time SLA Prioritisation. In operations with mixed SLA profiles, Stackbox continuously recalculates SLA pressure across the order queue and adjusts task priority accordingly. This requires real-time queue management rather than batch planning and is part of the orchestration architecture.
A Level 2 platform in Stackbox's framework will have partial answers to most of these. A Level 3 platform will answer yes to all of them, and should be able to demonstrate the answers in a live environment rather than a prepared demo.
The orchestration architecture behind Level 3 is explained in our guide to the warehouse orchestration system. For how these capabilities compare across vendors, see the Stackbox vs SAP EWM vs Infor comparison, and for the returns automation delivers, warehouse automation ranked by ROI.
To see Stackbox's AI capabilities in a live warehouse environment, request a reference visit or scoped demonstration at stackbox.xyz.
