Food and beverage manufacturers are still on the fence about artificial intelligence adoption. Many believe it isn’t useful or plain do not know how to use it. Yet on the other side, adopters can’t image their plants without it. 

According to research firm Mordor Intelligence, artificial intelligence in the food and beverage market was valued at U.S. $3.07 billion in 2020 and is expected to reach $29.94 billion by 2026 at a CAGR of over 45.77% during the forecast period, 2021-2026. 

The Association for Advancing Automation (A3) is made up of three principle daughter associations: robotics, machine vision and motion control/motor industries, where AI acts as a bridge across those technologies. One of the roles for Robert Huschka, vice president of Education Strategies, A3, is liaison to the strategic advisory committee on artificial intelligence. The committee helps guide members through their AI journey. 


Robert Huschka

Robert Huschka


Huschka and other industry leaders in both artificial intelligence and the equipment that uses it have a number of insights on how AI is being used, where it is used effectively and tips for execution.


What is artificial intelligence?

This is a tricky question. Artificial intelligence can be used in robotics and vision systems most basically, and to control certain parts of a machine. The data, however, is most important for any AI system to work as it is supposed to and to its highest potential.

“AI is used to augment controls in robotics. In Industrial manufacturing, it’s used for predictive and prescriptive technologies to determine when a system is going to break for downtime, etc.,” explains Huschka. Vision is deployed across a large swath of industrial environments—quality control, safety, inspection – and also in pick and place applications—usually as a piece of vision equipment, he adds.

Cory Knight, automation engineer in food and beverage segment at Festo, believes that AI is about data and digitalization. “You can code and predict and write algorithms all day long, but if you don’t have any data to analyze then it’s worthless.” Cory says that companies first need to get the data to the algorithms in order to start deterministic or programming to determine what you need to do or how to improve the process. 


Cory Knight

Cory Knight 


According to Dr. Irene Petrick, senior director of Industrial Innovation, Internet of Things Group, at Intel Corporation, “AI is the use of data that is processed thru algorithms that are generally trying to find some kind of pattern or assess data based on an already existing pattern. It’s simply using data to drive insights into the real world.” Intel works on artificial intelligence algorithms for the software itself and how to collect data at the source, how to pick it and move it to wherever it’s necessary to process, and how to store it.


Irene Petrick

Irene Petrick


Lawson believes that intelligence at a system level is more important than at the machine level. There is a lot of focus on building smarter machines, but what we really need are smarter systems at a higher level. The machine level problems don’t need AI or ML; they just need good algorithms and good engineering and software, and you can solve a lot of those problems.


Food and beverage applications

Use of AI in food and beverage manufacturing is limited at this stage. There are some companies behind it 100%, but most are still dipping their toes in the water.

One area that is taking off is crop harvesting, with Knight saying he has seen agricultural harvesting robots. One Festo customer has a solution that uses machine-guided vision for not only telling the robot where strawberries are on the plant, for example, but also grading strawberry ripeness and picking based on ripeness and where that strawberry is being transported. 

“So they’ve connected it to the supply chain,” says Knight. “They can pick it based upon transport time to each destination.” This allows the company to pick strawberries days before they’re ripe to make sure it is ripe when the shipment gets to its destination.

Huschka agrees that AI use in crops is working well. 

“AI is already leading in the global farming industry and weather prediction. When is the right time to plant crops? It’s making these macro decisions. What crops work best in the right time? And it’s providing insight at the beginning of the process. Farmers are attaching wearables to their cows that feed them data on how their cows are doing… whether or not they’re healthy.” Huschka thinks that this could lead to controlling greenhouse gas emission that cattle can cause. 

AI is also frequently used in inspection equipment, which is not new in food and beverage manufacturing. 

“What’s new is the way AI anticipates what’s coming, and the way AI can do it more quickly and do it even with a feedback into quality of future products coming down the line,” says Petrick. “AI comes in and makes visual inspection different than in the past. It plugs into difference pieces of the production process to reduce quality defects in future runs.” 

AI is also used for helping to monitor things like pumps. “In food manufacturing, pumps play a major role in moving any kind of liquid or semi-liquid through things. So you would see AI monitor vibrations, heat temperature compared to ambient temperature around the pumps. You would also see this type of sensing in any kind of packaging machine,” she adds.

Wes Garrett, account manager, Authorized System Integrator Network at FANUC, sees mostly 2D vision being used in food and beverage manufacturing. “As far as food processing, there isn’t much going on. E-commerce, logistics and fulfillment—that’s where there is more demand for it.”


Wes Garrett

Wes Garrett 


Lawson’s company—whose clients include McCain Foods, Mars Inc., Tyson and General Mills among others—uses AI to identify causes and effects as to what happens in a manufacturing supply chain process. “When we see variations or deviations in an important metric, we go back and look for correlations upstream that occurred earlier in the data chain that have the same characteristics of the variants that we’re seeing. In order to do this, however, you must sift through enormous amounts of data to find these correlations.” Using machine language, big data and AI can rapidly find these correlations and provide customers with the insights needed to solve the issue. 

Knight explains the advantage of Festo’s MSE6-E2M family of modules: When you look at industrial equipment, like form/fill/seal packaging lines, every piece of equipment has a point of use air prep. And the compressed air line that goes through the facility branches off and supplies air to each subsystem in an assembly line. “And that is what every plant manager is concerned about, because they want to know how much air that machine needs, how much does it use? Because that’s dollars. That’s affecting their utility bills significantly.” 

The food and beverage market has been lagging in AI compared to automotive, says Petrick. However, she says that food and beverage manufacturing has a lot of opportunities to apply this and they are beginning to look at how to do that more efficiently.

“What makes food manufacturing so interesting is that if you’re dealing with organic materials, by their nature they have some variation in the incoming raw materials. AI is helping companies do what we call ‘tunable’ manufacturing. In other words, if I understand what the properties of the incoming materials are, I can increase the ultimate yield by changing the processing parameters of the various stages of the production process.” 

In fact, AI is being used among vineyards, as moisture and sunlight are important parts of what creates the sugar necessary to certain levels of wine, with an appreciation of doneness. “So we’re seeing a lot of AI being used around moisture content,” Petrick adds. 

“Although we haven’t seen a massive impact in the industry just yet, we have seen AI being used to acquire raw materials more intelligently, and seeing hidden causes and effects that have been hidden for decades that we are now uncovering,” says Doug Lawson, CEO of ThinkIQ, a pioneer of Digital Manufacturing Transformation.


Doug Lawson

Doug Lawson 

Ambition-Execution Gap 

Last year, A3 surveyed its members about their companies’ adoption of AI. More than 76% of respondents felt that AI will be important to their companies in the next three years. However, just 3.3% of those surveyed said that AI was being widely applied in their organizations. This has been called the Ambition-Execution Gap.

“Even if you get started, we have seen companies who struggle to move from the labs so to speak into real world production. Scalability has been a challenge in some cases. And making it work outside of a more controlled environment,” Huschka cautions.

“I would say AI, machine learning, digital twinning… it hasn’t been fully adopted yet. It’s rare you see it,” says Knight. “You need to know who’s in the game and forward thinking, because it’s an investment. You have to figure out how to apply it to your process.” He adds that once applied, it offers the flexibility and modularity to take control of your process.

So, is AI a useful tool or is it hype? 

Most agree that it’s somewhere in between. “It’s not a magic wand; it’s not something that will solve all of your problems. But at the same time there are companies that are making use of – whatever you want to call it AI, data analytics, smart automation -- to make a real difference in their businesses,” says Huschka. He adds that it takes the vision, know-how and finding the right strategic partners to make that dream a reality for your individual business.

There are a lot of buzzwords being thrown around regarding AI. “We have algorithms that make decisions, but they’re simplistic decisions. So is that AI? Yes, but it’s very basic. Because of the way models have evolved and the amount of data that’s now available, the AI software models and algorithms are so much more complex than ‘is this a defect or not a defect, yes or no?’” explains Petrick. Today, AI has gone far beyond that as it’s using a lot more data, visual, audio, text, numerics and combining them in ways—much like the human mind will do—and putting it together to answer much more complex questions.

Knight sees AI as a valuable tool – especially in food. “The big reason it’s highly important in food is the because of the variability and irregularity of food products. They are the most challenging product to process in the world.” That’s where Knight sees AI really making a huge impact.

Garrett thinks it’s both. “A couple years ago it was a lot of hype; everyone was excited and thought it was going to take care of a lot of things. And now, it’s not what everyone thought it would be. It’s got limitation,” he says. He believes it may come in 10 years, but not in the short foreseeable future. 

Lawson agrees with Garrett. “While there are practical success stories out there, there’s also an extraordinary level of hype around AI and how it will revolutionize the manufacturing industry. This hype poses an issue in that it gets in the way of those providing real innovative technology to their customers.”

Questions to ask before adopting AI

For any company looking for an AI solutions, you need to establish your business case and verify that you have the data you need to deal with the problem you want solved, explains Huschka. Look for a trusted partner, because companies don’t have someone in house. So at least in the early days, look for a system integrator or tech partner who might be able to help on the journey and get to the problem you are trying to solve. Look around your industry, where are others making use of AI.

Some questions to consider on your journey into AI, Huschka adds, are: 

  • What is your business need?
  • Is your data house in order?
  • How are you currently collecting data? Can it be accessed?
  • Do you know the challenges of bias and explainability?
  • Is your company committed?
  • Do you have the skills you need?

Part of AI is the learning process and if you’re not going to write thousands of lines of code, and using sensor feedback, you have to get the feedback some way. “The learning is as only as good as you give it,” Knight advises. 

AI Application in a Nutshell

According to an A3 whitepaper, there are six applications where AI is making a difference in manufacturing and automation:

1. Manipulation and Grasping: Machine learning is now being applied to help robots with material handling tasks, such as moving large objects onto a pallet. This is also known as pick and place.

2. Defect Analysis/Machine Vision: One of the most common applications using AI and machine learning is for inspections and defect analysis. A3 says that of their members, 82% are using vision/inspection apps in the workplace.

3. Asset/Process Optimization: Some of the best use cases for AI involve making good use of the data: predictive and prescriptive maintenance. Many sensors are now collecting and cataloging information and data from the factory floor.

4. Cybersecurity: AI cybersecurity solutions are emerging to protect systems like those that control factory automation or industrial robots from hacking and cyberattacks.

5. Autonomous Mobile Systems: Autonomous robots are already working wonders in e-commerce fulfillment centers. Mobile robots have been replacing forklifts, and now they are putting together production processes by moving products from one conveyor belt to another. 

6. Safety: Applying AI in machine safety technologies is challenging. AI-based machine vision systems might be used to determine if workers are appropriately dressed for the factory floor, or if objects are in unintended positions.

AI & the workforce

One of the big challenges in the global food industry has always been workforce. So how can you add more automation into, say the ag field or meat production? “AI is making robotics more sophisticated but the ability to pick a berry—not just picking it correctly so you don’t damage it, but deciding when to pick it, is it the right time to harvest—using AI vision applications,” says Huschka on labor issues in the food space.

“AI really helps with taking workers off the floor who aren’t essential and support the workers who are essential, and that’s about data,” adds Petrick. “When we’ve asked companies, what is the impact of the pandemic on your operations? About 80% tell us it’s had a negative impact from a safety perspective, or a production perspective or an output perspective. But 20% of the companies we’ve talked to say productivity is actually getting better as they remove non-essential workers from the factory floor and use data more effectively to do remote monitoring and troubleshooting. And AI underpins that.”

For more information:

A3, Association for Advancing Automation: