Three ways food businesses are actually using automation and AI

AI has become one of the biggest talking points in business, but in food, some of the most useful applications are much more practical than the headlines suggest.

For many businesses, the bigger opportunity still lies in automation, despite the initial desire, or perhaps fear, to use AI to get ahead or simply keep up with the competition.

So, we thought we would share a few examples of how we are helping food businesses use automation now and prepare to use AI effectively.

1. One process for all orders

Food businesses are complex. That’s just a hard fact of life, like taxes and, apparently now, tropical weather in the UK.

One food supplier we work with handles several different types of order. Export orders need specific delivery notes and invoices, while smaller orders may need a delivery charge added. Each order must also be assigned to the correct delivery partner. Some deliveries attract tax and others don’t.

None of these decisions is particularly difficult on its own. Together, and at volume, they create a lot of admin.

Orders now enter Platter in one place, however they arrive. From there, the correct documents are generated and any delivery charge is added automatically. The delivery is also booked as soon as the order is placed.

Platter works out when the relevant tax applies too. At least, that’s what we tell the tax man.

By the time the warehouse team starts work, Platter has already organised what needs to be picked for each customer and how it is being sent. No email or Google Sheet needs to be created.

The customer is kept informed at each important stage too. Nobody on the team has to stop what they are doing to send another update email.

Since moving the process into Platter, the supplier told us it has almost halved the admin time involved in processing orders.

2. Giving the accountant everything they need

For another customer, a surprising amount of time was lost to back-and-forth between its office team and external accountant.

The accountant needed information from the business, so the team had to find it and send it across. If anything was missing, there would be another request and someone would have to go back into the records again.

That exchange has now largely disappeared.

The accountant is sent everything automatically and can access the data directly. The internal accounts team no longer needs to package up information or check that it has arrived.

This is not AI doing the accounts. It is good automation removing work that neither side should have been spending time on in the first place.

And yes, in case you were wondering, the tax man is still kept happy, but with a fraction of the hassle.

3. Knowing what’s actually happening in the business

Before we get to “magical” AI running the business for us, we first need to be confident about what is actually happening.

One customer expected the biggest benefit of Platter to be the time it saved on day-to-day admin. What surprised them was having a much clearer view of the business.

That visibility starts with reliable data. When orders and the commercial information behind them are held in one place, the business can see what is changing without first assembling another report.

This is where AI starts to become genuinely useful.

Take a buyer who normally orders every week but has gradually started ordering less. With the right data in place, AI can bring that change to someone’s attention before the account is lost.

Automation first, AI second

So, to end this with a good old-fashioned summary, here’s our take.

A lot of businesses are still typing the same information into different systems. People end up checking documents or, worse, chasing them. Reports take so long to assemble that errors become almost inevitable.

Fixing those things can sound unambitious next to the promises being made about AI. In practice, it is where businesses are already seeing the clearest results.

Once the order flow works and the figures can be trusted, AI has something useful to work with. Before that, it is simply being asked to make sense of the same mess that the people in the business are already struggling with.