How to validate an AI inventory use case before buying software

1. Choose one inventory decision

Select a concrete action such as changing a reorder point, prioritising a cycle count, reallocating stock or escalating a shortage.

2. Define the decision window

Establish when the recommendation must arrive to remain actionable.

3. Audit the transaction data

Test on-hand balances, locations, units of measure, lead times and open-order status before testing a model.

4. Build a non-AI baseline

Compare the use case with the current rule, report or experienced operator—not with doing nothing.

5. Measure consequence, not prediction accuracy alone

Track stockouts, excess inventory, labour effort, service and the cost of incorrect actions.

6. Inject operational failure cases

Include stale inventory, delayed receipts, substitutions, missing scans and sudden demand changes.

7. Set automation boundaries

Define which outputs remain advisory and which can trigger a workflow or inventory transaction.

8. Run a controlled shadow period

Let the model make recommendations without executing them, then compare decisions and outcomes.

9. Decide whether to scale, redesign or stop

Scale only when the decision outcome improves after accounting for data-maintenance and exception-handling costs.