The numbers don't lie: what AI is actually delivering for food manufacturers

49% of food manufacturers are using AI according to our research*. We take a stat-led look at what that means for the sector and how it will affect production efficiency, quality control and investment choices.  

Food manufacturers are no strangers to pressure. Tight margins, complex supply chains, mounting compliance obligations, and a workforce in flux are all part of the day to day. So, when a technology comes along promising to fix all of it, scepticism is understandable. 

But what happens when the tech starts to improve the outcomes? That's the question we asked when we surveyed 500+ senior leaders across food manufacturing, processing, distribution, retail and foodservice in the UK and US.

Here's what the numbers say about where AI is heading – and why manufacturers in particular can't afford to stand still.

1. Manufacturers are already leading on adoption

49% of food manufacturers are actively using AI and machine learning technologies – the highest adoption rate of any sub-sector. That compares to 36% in food retail.

This isn't entirely surprising. Manufacturers have been running early iterations of AI for years — predictive maintenance, process optimisation, quality inspection on the line. The infrastructure and the appetite were already there. What's changed is the pace and the scope.

2. AI investment has moved out of the innovation budget

Early AI in food manufacturing lived in pilot programmes. That phase is ending.

89% of agri-food businesses now have a dedicated budget for AI implementation — with 44% folding it directly into overall IT budgets. That signals a shift from "let's test this" to "this is core infrastructure."

For manufacturers, that means the competitive question is no longer whether to invest. It's whether your investment is targeted at the right problems.

3. Quality and production efficiency are the headline wins

When we asked businesses what benefits they're actually seeing from AI, the top three were:

- Improved product quality

- Increased production efficiency

- Reduced operational costs

In fact, 55% of respondents cited improved product quality and increased production efficiency as core benefits of AI adoption. And critically, only 1% believe AI will deliver zero benefits — a figure that says a lot about how quickly sentiment has shifted.

For manufacturers navigating tight margins and high volumes, these aren't abstract gains. Better quality control means fewer rejects, less rework, reduced waste. Greater production efficiency means more output from the same assets.

4. The use cases with traction are grounded in operations

The AI mechanisms getting the broadest uptake right now are those that plug directly into quality and process control systems — image recognition for defect detection, automation of process steps and tools that complement existing QA infrastructure.

Inventory control is also well established. And leading manufacturers are moving into demand forecasting and capacity planning — using AI to model short, medium and long-term demand rather than relying on manual commercial judgement.

Nestlé has deployed AI-enabled Intelligent Process Automation at scale for demand forecasting and product distribution. That's not a pilot. That's the new operating model.

5. Scepticism is highest where adoption is highest – and that's telling

Here's a finding worth pausing on: 22% of food manufacturers don't believe AI will have a positive impact, or aren't sure. That's a higher rate of scepticism than the sector average — despite manufacturers leading on adoption.

The likely explanation? When you've actually scaled AI into operations, you see where it falls short. The hype bumps into reality. Integrations are harder than expected. ROI is slower to materialise than originally suggested.

This isn't a reason to stop investing. It's a reason to invest differently — with a clear ROI framework in place before committing and realistic expectations for the timeline to value.

6. The data problem is real and it's slowing you down

28% of agri-food businesses are only partially digitised, or not at all. For this group, AI isn't the first problem to solve — data infrastructure is.

Even among businesses that are well-digitised, the challenge of getting data into a usable state is significant. Siloed systems, inconsistent formats, legacy platforms that weren't built to feed AI models — these are the unglamorous blockers that slow progress.

The ROI from AI in manufacturing is directly proportional to the quality and accessibility of the data behind it. Businesses that haven't addressed this yet will find themselves paying twice: once to fix the data foundation, once to build the AI on top of it.

7. The skills gap is a bigger risk than most manufacturers realise

A fifth of agri-food businesses currently have no dedicated digital transformation team — rising to 23% in the US.

And it's not just specialist roles. The World Economic Forum found that 58% of employees expect their job skills to change significantly in the next five years owing to AI and big data. For manufacturers, that touches production operatives, planners, schedulers, quality teams — not just the IT function.

Businesses that treat AI as a technology procurement decision, rather than a workforce transformation, will underutilise what they buy.

8. The investment wave is coming — and it's coming fast

96% of agri-food businesses anticipate making further investment in AI within the next five years. More pressingly, 63% expect that investment to happen within the next 1-2 years.

The manufacturers who've spent that time building clean data foundations, developing internal expertise, and running focused pilots will be positioned to scale. Those still waiting for the technology to mature will be playing catch-up against competitors who didn't.

What this means for manufacturers right now

The evidence is clear: AI is delivering real benefits in manufacturing, and investment is accelerating across the sector. But the businesses seeing the strongest returns aren't those who moved fastest — they're those who moved with the most focus.

The priorities that come out of the data are consistent: clean your data, close the skills gap, and build an ROI framework before you commit. Don't pursue AI in multiple directions at once without a strategic anchor.

The technology is ready. The question is whether your organisation is.

Foods Connected now has a native Model Context Protocol (MCP) connector, meaning AI assistants like Claude and ChatGPT can connect directly to your supply chain and compliance data. This opens the door to faster, more contextual insights — without switching between platforms or manually exporting data.

Find out how other leading food manufacturers are using AI and Foods Connected to move from manual QA to Connected Compliance in our upcoming Webinar on 30 June. 

*This blog draws on research from the Foods Connected report: AI & Agri-Food: Attitudes, Adoption and Ambitions.*