AI in the Food Industry

How to Harness the Power of AI in Food Manufacturing

7 mins

17 Mar, 2025

Ian Scott-Mance, Digital Transformation Manager, METTLER TOLEDO Product Inspection

We hear the term ‘’Artificial Intelligence” or “AI” a lot, particularly in manufacturing – but what part does AI play in food production? And is it really AI, or is it Machine Learning? In this article, learn the differences between AI and Machine Learning and how they are already integrating into food safety processes and quality control in the food industry.

AI is already part of everyday life, though we may not realize how widespread it has become. Ethan Mollick, Associate Professor at the Wharton School of the University of Pennsylvania, is a leading AI expert who widely shares his insights into how AI is impacting the worlds of work and education. He is the author of “Co-Intelligence: Living and Working with AI” and states the following in this work:

“Many things that once seemed exclusively human will be able to be done by AI. So, by embracing this principle, you can view AI’s limitations as transient, and remaining open to new developments will help you to adapt to change… and remain competitive in a fast-paced business world.”
Ethan Mollick, Co-Intelligence: Living and Working with AI (2024)

Managing change and remaining competitive are top priorities for food manufacturers, but how can they harness the power of AI and not be left behind? AI is having a considerable effect on all industries, including the food industry. But what does that look like – and why should we care?

First, let’s take a look at definitions. Artificial Intelligence (or "AI") and Machine Learning are often used interchangeably, but they are technically not the same thing.

AI refers to computers or machines that have the ability to mimic cognitive functions associated with human intelligence, such as being able to see, understand, and respond to spoken or written language, analyze data, make recommendations, and more. AI uses algorithms, statistical models, and computer programs to analyze data, make predictions, and automate processes.

Machine Learning, however, is a subset of AI that automatically enables a machine or system to learn and improve from experience. Instead of explicit programming, Machine Learning uses algorithms to analyze large amounts of data, learn from the insights, and then make informed decisions.

AI tends to be a more overarching term, whereas Machine Learning is a specific application of AI. So, when you hear “AI” applied in the food industry, it’s likely that it’s actually Machine Learning.

The Appeal of AI

Why is AI so appealing to food manufacturers? Put simply, it's speed. In a world where time is money and efficiency is key, manufacturers need results quickly. A task that could take hours when conducted by a human is now instantaneous with AI’s subset Machine Learning – this benefit alone is a game changer.

Machine Learning has the potential to transform many processes, from quality control to distribution. For example, it can be used to identify foodborne pathogens and contaminants, detect defects and anomalies in products, and optimize supply chain logistics.

It doesn’t stop there; Machine Learning can be used to analyze product inspection data in order to predict failures before they happen. Information gathered using sophisticated data management software can be analyzed in advance of any issues occurring. This allows manufacturers to optimize their maintenance schedules to avoid unplanned downtime caused by equipment breakdown. In an industry where preventing line stoppages is essential, knowing when to carry out preventive maintenance is invaluable.

Benefits for Product Inspection

By automating processes and analyzing data in real-time, Machine Learning can help companies make more informed decisions to elevate their food safety and quality control processes. Dynamic checkweighers from METTLER TOLEDO, for example, are enabled with a number of software tools to help improve efficiency, such as Smart Documentation Services.

By using advanced algorithms and machine learning techniques, AI systems can make highly accurate predictions and decisions based on vast amounts of data. This can be particularly important in food safety, where even small errors or oversights can have serious consequences. For example, an AI-enabled system can quickly detect and isolate contaminated food products to prevent them from reaching consumers.

Potential Pitfalls

While Machine Learning and AI offer many advantages to food manufacturers, there are some possible disadvantages too, such as concerns around job displacement.

As AI and Machine Learning systems become more advanced and widespread, they may replace human workers in some tasks, such as manual labor or data entry. However, job displacement is not inevitable; these workers could be redeployed elsewhere in the business to perform more value-added tasks.

AI systems also require vast amounts of high-quality data to function effectively. If the data used to train the algorithms is biased, incomplete or unrepresentative of real-world scenarios, the performance of the AI system could be compromised.

Consumer safety is vital in the food industry. Manufacturers utilizing any new AI-powered technology must make sure that it robustly supports regulatory compliance. Manufacturers must check whether additional validation or certification processes are required and act accordingly.

These are not the only issues associated with implementing AI-powered systems in a food production environment. However, it’s important to note that while AI technology often adds value, it is not the ‘’magic’’ solution to production challenges.

How to Leverage the Power of AI in Food Manufacturing

AI-enabled technology may sound great in theory, but can you put it into practice? Here are some steps that food manufacturers should take if they want to incorporate AI into their production environments:

Conduct a readiness assessment

Evaluate your current technological capabilities and identify any gaps in infrastructure.

Start Small

Choose a specific area within your manufacturing process to run a pilot AI project - this enables you to measure its effectiveness with little upfront investment.

Invest in Training

Upskill your workforce and prepare them to adapt to working alongside AI-powered tools.

Collaborate

Work with AI technology providers with specialist knowledge of the food industry to guide you through implementing tailored AI solutions.

Leverage Data Analytics

If you don’t already, start collecting and analyzing your production data, as high-quality data is essential for effective AI models.

Think Compliance

Prioritize food safety compliance when implementing AI-based solutions.

Monitor and Evaluate

After implementing any AI solution, continuously monitor its performance, and use KPIs to assess the solution’s impact on efficiency, cost reduction and product quality.

AI and Machine Learning are powerful technologies that are already having a considerable impact on the food industry. They can help companies save time, resources, and improve the quality and consistency of their products.

It’s crucial that food manufacturers think seriously about how AI-enabled technologies can best be deployed to support their aims of enhancing product quality and safety.

Finally, harnessing the power of this technology is not without its challenges. It is an increasing part of our world, and now is the time to embrace it.

About the Author

Ian Scott-Mance, Digital Transformation Manager, METTLER TOLEDO Product Inspection

Ian Scott-Mance has over 16 years of experience in marketing and project management to drive advancements in data connectivity and industrial AI applications. Based in Royston, UK, Ian has a proven track record of developing and executing innovative marketing strategies that enhance product offerings in the rapidly evolving landscape of industrial technology. His expertise in digital transformation and connectivity solutions enables him to bridge the gap between complex technical concepts and market needs.

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AI in the Food Industry

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Potential Pitfalls