AI agents for operations teams in retail

A core line is down to two sizes by Thursday, and nobody saw it because the reorder check means exporting till sales, comparing them with stock, and scanning hundreds of lines by hand while the weekend rush approaches.

→An agent reads sell-through against stock and open purchase orders each morning, flags lines with a broken size run or short cover, and drafts the top-up order for the planner to approve.

A supplier delivery note says 120 units, the PO says 144, and the difference only surfaces when the stockroom cannot find the missing cartons weeks later, long after the supplier will accept a claim.

→An agent reads each delivery note against its purchase order the day the goods arrive, logs every short, over, or substituted line, and queues the supplier query for approval with the evidence attached.

Cycle counts get sent to stores, sheets come back late, and someone spends a Tuesday matching them against the system to find which variances are real losses and which are mislabelled bays.

→An agent builds the count sheets, compares each returned sheet with system stock, and ranks the variances by value so the stockroom rechecks the costly lines first.

A reset goes out as a long email, thirty stores read it thirty ways, and the area manager only learns a bay is wrong when a photo turns up days after the deadline.

→An agent turns each planogram change into a store specific task list, chases the checklist, and compares the photos against the plan so the exceptions reach the merchandiser, not the whole chain.