The future of AI in the food industry

Artificial intelligence is one of the most exciting technological developments within the food and beverage industry. Learn more about its potential. 

The rise of artificial intelligence (AI) and machine learning (ML) are impacting almost every industry, and the food industry is no exception. Manufacturers and produce growers are using AI to streamline activities and 31% of food retailers in the US are already improving food safety with AI. In this article, we dive deeper into the subject of AI in the food industry and what innovations are on the horizon.  

What areas of the food industry would benefit from AI? 

Artificial intelligence has the potential for benefits across the entire food supply chain. The following examples indicate how this is working and what may be to come.   

Cultivation

Farming has always required a combination of scientific and local knowledge, data analysis and educated guesswork. For example, food producers must decide which sowing and harvesting times will offer them the best return on their investments. But timing isn’t the only concern.  

Farmers choose crops and farming methods that allow them to target optimal market prices. They must also consider which fertilisers and crop protection agents should be applied, and when this should occur. In animal production, achieving good yields requires data analysis, market forecasting, and decisions like feed choices, nutritional supplements, and veterinary intervention.  

AI can help farmers succeed by providing data-driven insights into crops, farming methods and timing, so they can optimise yields and achieve higher levels of profitability. 

Food processing & manufacturing

It has always been possible to sort and grade food products using analog machinery and robotics, but AI is likely to bring enhancements. This usually entails multiple automations to grade produce according to different criteria. AI-driven automation, however, could integrate all of these processes into a single step - as well as monitor and coordinate production lines from grading through to packaging. Over time, AI can deliver insights to improve efficiency on the factory floor. 

AI can also be used to predict buyer needs more accurately based on consumption data and market trends. Waitrose, for instance, began using AI to gauge flavour preferences and adjust recipes accordingly. These lucrative insights will benefit suppliers, manufacturers and retailers, while also reducing waste by focusing on products that are more likely to sell.

Food distribution & retail

What do customers want, and what are they willing to pay to get it? How much will they buy? How frequently should stock be refreshed and at what volumes? These are questions that food distributors and retailers previously answered using historical data, but can now be addressed by using AI to forecast consumer behaviour. 

Current events and trends might influence demand, and historical data can’t reflect that. AI is already able to develop effective market demand projections that account for a range of factors. 

Even restaurants and fast-food outlets can benefit from AI-generated insights and labour-saving advances. For example, they can research suppliers and order stock based on an AI-calculated market demand figure. Big chains like Domino’s and Starbucks are already doing this. They can also use AI for customer service - with fast food chains such as Wendy’s using AI to make taking orders more efficient. 

How can specific aspects of AI be used in the food industry? 

Automation

Automation can expedite workflows and ensure consistency across various supply chain processes. For example, while different machines might have been needed to grade produce according to size, weight, and colour, AI-driven systems have the potential to perform all three processes at once. AI can also be used to automate product sourcing by allowing manufacturers to monitor the price, quality and availability of produce. By automating repetitive processes, human resource can be redirected to focus on more value-enhancing and strategic operations. 

Machine learning

So what is machine learning and how does it fit within the bigger AI picture? This sub-sector focuses on how humans learn, imitating the processes by applying a mixture of data and algorithms. The aim here is to create more accurate processes.  

So, machine learning allows machines to “learn” just like humans do. Simply “train” them using the datasets you want your AI to implement, and they’ll apply it. For example, you might find that changing trends affect the produce you offer for sale. This is information that AI can monitor and learn from, helping you to anticipate changes and strategise accordingly.  

As an added benefit, machine learning will can make production lines more efficient. For example, AI-driven automation with machine learning capabilities can reduce the need to invest in manually adjusting your production line equipment. And, with the right AI, you might even get data-driven suggestions on how you should package, distribute and retail products for widespread market appeal.  

Natural language processing

This branch of AI is designed to help a computer understand - and process - human language in the same way that we would. It gives the machine the power to interpret and manipulate language as it encounters it.  

Natural language processing (NLP) can help businesses understand how customers talk about their products and services. This allows for nuanced insights into consumers that don’t rely on historical data, and therefore may better reflect changing needs and preferences. This can be used in a multitude of ways, from recipe formulation and NPD to branding and packaging.  

Robotics 

 “Thinking” machines will have a far greater range of capabilities than their predecessors. Whereas you always had to determine desired outcomes yourself and monitor production systems, AI will allow automation to run more smoothly and become increasingly self-monitoring. 

Intelligent robots will be capable of performing more functions simultaneously, reducing the amount of mechanisation needed for an efficient production line. And, should an issue occur, AI could detect it early and may even be able to fix it with minimal intervention. 

Tips for implementing AI in the food industry 

Identify key operations 

Start by identifying where AI could be most effective. Are there common bottlenecks or pain points that could benefit from being streamlined or automated? This could include inventory management, order fulfilment, quality control, or customer service. Based on how AI improves these operations, you could then make a compelling business case for wider implementation. 

Start collecting data  

AI thrives on data - so once you've identified the operations you'd like to enhance, gather as much relevant information as possible - it can be sales data, customer feedback or production statistics. Then the next step is to make sure it is all digitised, so it can be analysed. You can then use this for benchmarking purposes, so you can effectively evaluate the impact of new AI systems. 

Evaluate your current technology suite  

Take the time to thoroughly assess your existing technology infrastructure. Consider whether it has the necessary capabilities to seamlessly support AI integration. If it falls short, identify the specific areas that require improvement and determine what changes are necessary to bridge the gap. 

Invest in training & onboarding 

The success of AI relies on the expertise of the team behind it. It is crucial that employees fully comprehend the purpose of the AI system and possess the necessary skills to utilise it effectively. Making sure that staff are fully trained will optimise returns on investment and can also help identify new opportunities for where AI can be put to use. 

Monitor insights regularly  

Once your AI system is up and running, it should be monitored regularly. Use the resulting insights to make data-driven decisions and improve operations. By regularly evaluating the system's performance, you can ensure that it continues to deliver sustainable long-term value. 

Of course, to ensure that this all runs effectively, you must also have AI governance in place. This will ensure that your AI system is used responsibly – and transparently - and mitigate against the risk of issues such as privacy law contravention, fraud and even data leakage.  

Set up AI model documentation and auditing pipelines. This will allow you to monitor lifecycle behaviours, flag any issues and assess any potential risks before products go into production.  

Foods Connected can help food manufacturers, suppliers and retailers get the most out of digitisation, from digitised workflows and cost and yield management to food safety, livestock procurement and processing. See for yourself how we can transform your operations, or request a demo of our award-winning platform to find out more.