AI for Food Manufacturers: Where It Pays Back First (and Where It Doesn't)

Most food manufacturers don't need an AI strategy. They need to know the two or three jobs where AI will pay for itself, and the ones to leave alone. This post covers both.

Where adoption really is

The headlines suggest everyone is already using AI. The official figures say something more modest.

• UK: about 35% of businesses with 10 or more employees now use at least one AI technology, up from about 12% in late 2023. But only 10% of those users say they use it extensively (ONS).
• UK manufacturing: manufacturers most often get their AI through free-to-use software, not through tools built into how they work (ONS, as above).
• Ireland: 20.2% of enterprises used AI in 2025 (CSO). In manufacturing (grouped with utilities), use was flat at 17.4%, against 17.6% the year before (CSO).

The most common reasons UK firms give for not adopting are difficulty identifying where to use it, cost, and a lack of expertise (ONS). That first one matters most. If you can't name the job, you can't measure whether it worked.

What the numbers tell us

Adoption is wide but shallow: most firms using AI are using one or two tools, often a free chatbot on someone's laptop. The gap between big and small firms is also large. In the UK, 49% of businesses with 250 or more employees use AI, against 28% of those with fewer than 10 (ONS).

For a food manufacturer turning over £5–25m, that gap is the opportunity. The firms that close it will be the ones that put AI into two or three jobs that matter and measure the result.

Where AI pays back first

In a food business, AI earns its place where people spend hours reading, sorting or re-typing information. It's weakest where the job is making a judgement call or producing a number someone will rely on.

A good test: AI should read, sort and draft. Your systems should produce the numbers. Your people should make the decisions. At Genesis Bakery, for example, AI reads supplier contract PDFs into drafts the procurement team checks, but every price and volume on screen comes straight from purchase records.

Where it doesn't pay back (yet)

• When the data is a mess. If your exports have duplicated weeks or wrong units, AI will give you confident wrong answers faster. Fix the data first.
• When it's asked to make the number. Costings, contract prices and production volumes should come from your systems, not from a model's best guess.
• When it replaces judgement it should support. Your planner's experience is the asset. AI should put better information in front of them, not overrule them.
• Safety-critical decisions without sign-off. Allergen and food safety decisions stay with a named person.
• An "AI module" bought before the problem is named. If you can't say which number should move, wait.

Some of the biggest wins aren't AI at all. Getting purchases, contracts and sales data into one place, checked automatically, often comes first.

Three questions before you spend

1. Which number will move, and by how much? Agree the measure before you start, and how you'll know.
2. Who owns it? Someone in the business has to use it every week. If nobody does, it won't stick.
3. Where does your data go? Ask who can see it, where it's stored, and whether it's used to train anyone else's model.

If a supplier can't answer all three plainly, keep looking.

Sources

• Artificial intelligence in UK businesses: 2023 to 2026, Office for National Statistics, July 2026
• Information Society Statistics – Enterprises 2025: Artificial Intelligence, Central Statistics Office
• Productivity in Ireland 2024–2025: Artificial Intelligence, Central Statistics Office