Most B2B order tracking relies on disconnected systems and batch updates, leaving orders effectively invisible at crucial moments.
Buyers often see stale or conflicting information, which drives “where is my order” calls and tickets.
Manual reconciliation across ERP, WMS, and carrier systems consumes many hours each week for operations and support teams.
Scenario 1
Buyer asks in natural language, using a PO number, order number, project reference, or account. AI recognizes the context as an order inquiry and identifies which systems hold relevant data.
The assistant finds the order in ERP and commerce, then gathers WMS and carrier events. It reconciles statuses across systems into a single internal representation that reflects reality.
AI presents where each line stands, which shipments went out, and what is pending. It can suggest next actions such as expedite, partial ship, or notification preferences.
Significant reductions in “where is my order” tickets as buyers get accurate, instant answers.
Fewer manual reconciliations as AI highlights inconsistencies and provides a single version of status.
Better on time performance and customer trust as issues are detected and addressed earlier in the process.
Does it connect directly to ERP, WMS, and carrier systems rather than relying on manual exports?
Can it explain split shipments, partial fulfillment, and backorders at line level?
How does it handle identifiers such as POs, order numbers, and project references across systems?
Can AI for B2B order inquiries answer questions from chat and portals using the same ground truth?
Does it allow buyers to self serve while keeping sensitive data behind account level permissions?
What is the typical pilot and full rollout timeline across your ERP and logistics stack?
How will it support proactive alerts when orders are delayed or stuck in a status too long?