Spend AI on stock interpretation and planning, not on moving every order.

1. 1

   Prioritizes stock review

   A model evaluates the physical-count and system context, ranks discrepancies and proposes an adjustment, reconciliation or recount. The category or inventory owner receives the relevant count and movement evidence together.

2. 2

   Builds demand proposals

   A forecasting model uses the approved history and drivers to propose demand for the planning horizon. The planner can accept or override the proposal with a recorded reason and retain both versions.

3. 3

   Interprets delivery exceptions

   A model reads the available fulfillment information and proposes a category with the supporting context. Routing rules then carry the case to the owner responsible for that type of resolution.

4. 4

   Matches products across channels

   A model compares listing descriptions and attributes to propose links to ERP items. Exact unit, pack-size and variant checks complement the semantic match before the catalog owner approves the mapping.

Moving an order, calculating a quantity or applying an established SKU mapping should not add another model charge. Those steps run as deterministic operations; model capacity goes to discrepancy interpretation, predictive planning and product matching where descriptions differ. Choosing the path per step keeps AI spending aligned with the work it improves as transaction volume grows. The deterministic commerce workflows below are the operational foundation, not a reason to add inference to every transaction.

How governed AI automation worksRules first, model proposal second, policy check, human review, captured decision.