When AI takes on the order flow, the real work begins.

What happened?

Choco, a food-distribution platform, has built AI agents that receive orders around the clock. Orders often arrive as free-form messages, photographs or documents, making them difficult to enter correctly in conventional systems. The agent interprets the content, uses the customer’s order history and product catalogue as context, then processes the order through the normal workflow.

According to Choco’s April 2026 case study, the solution processes more than 8.8 million orders annually. The company reports up to a 50% reduction in manual order entry, doubled sales-team productivity without added headcount, and configurable thresholds for when a case is automated or handed over. Those are the company’s own figures — but the implementation pattern is the important part.

This is more than automation.

Conventional automation requires information to arrive in the right format. This kind of AI can handle the variation that usually stops automation: a customer writes “the same as last week, but twice as much of the small one,” or sends a photo of a handwritten list. The AI makes a proposal; the business rules determine what may run automatically.

That does not mean giving a model free rein. Quite the opposite: a robust setup is bounded. Spending limits, approved product lists, clear logging and human review when confidence is low make the solution useful without making it careless.

Where could the same idea work?

Many smaller businesses encounter the same friction well beyond food logistics: quote requests arriving by email, free-text booking requests, service cases with images, supplier invoices, and customer follow-ups that fall between inboxes and spreadsheets.

A sensible start is not “implement AI everywhere.” Choose one recurring process involving lots of reading, copying and sorting. Let AI create a structured proposal, retain an approval step, and measure how often the proposal is right. Once it works, automate the next step safely.