Labour shortage in the food industry: automation and AI as a growth enabler in a tight labour market

Why the labour market is now the brake on growth

The tight labour market in the food industry is not a temporary peak. Production work in food is physical, involves irregular hours and competes for the same people as logistics and other manufacturing. On top of that comes the brake on labour migration, while part of the production and processing capacity relies precisely on that group. The result is that vacancies stay open longer and that a line that runs well is more likely to stop because of understaffing than because of a technical fault.

For many companies this means that growth can no longer be staffed as a matter of course. An extra order or a new customer requires people who are not there. That brings automation and AI into the picture, not to replace people you can find, but to deploy the people you do have on the work where they make the difference, and to lighten repetitive manual work and administration.

Wage costs: every percent is a hard ROI case

Where labour is scarce, labour becomes more expensive. ABN AMRO notes a wage increase for the food industry of about five percent in 2025 and around four percent in 2026. In a sector with thin margins and ongoing consolidation, that is not a detail, but a structurally higher cost base that cannot simply be passed on in the selling price.

That makes the sums on automation more tangible than a vague efficiency story. If wage costs rise year after year, that shifts the break-even point at which an investment in smarter planning, record-keeping or quality control pays for itself. It makes the business case real, but it is not a free pass to take a vendor's claim at face value: your own wage costs, your own process and your own volumes determine the payback period, not an average from a brochure.

Where automation and AI realistically come into play

The gain rarely lies in one spectacular robot, and almost always in existing processes that become less error-prone and more predictable with fewer person-hours. In the food industry there are four places where that is currently most tangible:

• Quality control: image recognition that flags defects in product or packaging on the line, as an extra pair of eyes alongside the employee, so that checks become less dependent on the hands available on a busy day.
• Record-keeping and administration: automatically processing packing slips, orders and batch and quality records that are now retyped by hand, with a human check on the exceptions.
• Production planning: working through planning scenarios faster when staffing, raw material supply and order peaks vary, so that a planner can still put together a feasible roster with fewer people.
• Demand forecasting: better forecasts for each product or customer, so that production and purchasing match actual demand more closely and there is less waste and fewer lost sales.

Be critical of savings claims

Impressive percentages circulate around automation in food: less waste, fewer rejects or hours saved. Treat them with healthy scepticism. The effect varies greatly by company, by line, by product and by how complete and reliable your data already is. A generic savings figure says little about your own situation and can justify the wrong investment decision.

The sensible approach is to ask a vendor for a trial on one process with your own data and your own figures, rather than a marketing figure. Record beforehand how things stand now: how many hours it takes, how much waste or how many rejects there are, and who decides on what. Only with that baseline measurement can you assess afterwards whether AI support has measurably saved time or money there.

Fitting it into quality assurance, and the human decides and signs

In the food industry, no process stands apart from food safety. An AI signal from quality control or an automated record must fit within existing frameworks such as HACCP and FSSC 22000: who decides, on the basis of what information, and how that is recorded. A model provides an additional source of information, not a replacement for the existing structure of responsibility or the duty to document.

At AIBA, our starting point is that the human decides and signs. A system can flag a deviation or propose a plan, but the employee responsible keeps the final decision, certainly where food safety is at stake. In addition, Article 4 of the EU AI Act (Regulation (EU) 2024/1689) has applied since 2 February 2025. Since the amendment by Regulation (EU) 2026/1744, in force from 27 July 2026, it has asked organisations to take measures that support the AI literacy of employees who work with AI systems. They do not have to guarantee a specific level. For operators, quality officers and planners, what matters in practice is that they understand what a system can and cannot do, where the assumptions and limits lie, and when a proposal has to be adjusted manually.

Where to start in practice

Do not start with a factory-wide automation programme, but with one process where the shortage or the wage costs currently bite hardest. Often that is a place with a lot of repetitive manual work or a lot of manual record-keeping, or an inspection step that stops as soon as someone is off sick. Establish how things stand now and what it costs, and only then assess whether AI support is the right first step there or whether another part of the process delivers more.

Our free Practice Scan helps you choose that first process and sharpen the baseline measurement, so that any next step rests on real figures from your own operation and not on a promise.