The situation
A fictional company’s planners combine stock, demand, purchase orders, and forecasts to decide which items need attention. The opportunity is to make the evidence easier to review and the next action clearer, within a bounded product or location scope.
Example assessment findings
Commerce and ERP records contain unmatched product identifiers. Stock refreshes nightly, while supplier lead times are maintained separately. Teams use different exception thresholds. A sponsor is available, but review duties and warehouse or finance escalations need named owners.
The decision
Start by reconciling the pilot item set and agreeing the exception rules. Use existing forecasts as inputs. Deterministic calculations identify exceptions; AI explains the context and drafts options for a planner. Automatic purchase orders, stock adjustments, prices, and delivery promises stay outside the pilot.
The outputs
The example work produces a validated workflow, data-source and freshness map, readiness gaps, owned preparation backlog, bounded workbench blueprint, evaluation set, and operating plan. The opportunity portfolio separates exception review from later forecasting or replenishment proposals.
The proposed roadmap
Prepare the identifiers, data access, owners, rules, and baseline. Prove read-only calculations and source references using historical and missing-data cases. Pilot with planners after quality and control gates pass. Enable training, support, monitoring, and cost review before deciding whether to expand.
What would be measured
Compare exception accuracy, planner review time, source traceability, decision quality, failure handling, and running cost against the starting point. A change in stock availability would need its own definition and evaluation.