AI Order Automation Blueprint for a Primary Producer and Wholesaler

The organisation handled seasonal demand through a mix of phone calls, free-form email orders, spreadsheets, manual confirmations, and pick-list preparation. That created pressure around re-keying, order accuracy, staff load, procurement visibility, and fulfilment speed.

Because the products were perishable and demand was seasonal, small delays or data errors could affect margin, stock availability, and customer service. The first decision was not to build AI immediately, but to define the operating workflow clearly enough to choose the right automation and system path.

ExIQ scoped a Stage 1 investigation covering stakeholder workshops, current-state process review, technology and MRP/ERP assessment, conversation-flow design, integration options, and a Product Requirements Document.

The target design considered three connected capabilities: an email parser for unstructured orders, a voice order assistant for guided capture and confirmations, and image-rich pick lists that could reduce selection errors during packing.

The discovery also tested whether a modern MRP/ERP should become the source of truth so AI could write into operational records rather than creating another layer of email, spreadsheet, or manual reconciliation.

The proposed Stage 1 output was a clear implementation blueprint: functional requirements, non-functional requirements, integration assumptions, test criteria, rollout steps, and a costed path for later delivery.

The value of the work was decision clarity. Leadership could compare a narrow AI order-capture release with a broader AI plus MRP path, understand the controls required, and choose a staged implementation without committing to a large build before the workflow was proven.