How can AI help solve the world’s mounting issue with food waste?

Food waste is a significant challenge globally, with an estimated 1.3 billion tonnes lost or wasted each year. Could Artificial Intelligence (AI) provide the solutions to an issue plaguing governments and businesses across the world?
February 10, 2023

The mounting issues and concerns around food waste

Food waste is a major issue that is not only growing but is increasingly front of mind given food security fears relating to climate change. According to the UNEP’s most recent Food Waste Index Report conducted in 2021, around 931 million tonnes of food waste was generated in 2019[i]. Of this, 61% came from households, 26% from food service and 13% from retail. Overall, the UNEP suggest that almost a fifth (17%) of total global food production may ultimately be wasted[ii]. When looking at food loss and waste, the World Bank estimates that a staggering 30% of global food may be wasted- equivalent to 1.3 billion tonnes per year[iii].

At Zero Carbon Academy, we previously reported on a study by Keenan Recycling which uncovered significant business costs associated with food waste[iv]. Their 2022 survey found that UK businesses spend an average of £50,862 a year sending food waste to landfill[v]. Keenan Recycling estimates that by recycling food waste, companies could save £7,000 a year. Concerningly, however, the study revealed that 38% of businesses surveyed felt recycling food waste was not a core priority, and 27% stated that their company was not recycling any food at all. Further results revealed that 62% of businesses were currently working to understand food waste issues, whilst 58% were concerned about the carbon emissions from sending food waste to landfills. At the time, Keenan Recycling’s Managing Director, Grant Keenan, said:

“With pending legislative change and pressure for organisations to make public their own net-zero plans, food recycling will be key to how commercial operations function sustainably in the future, so the best time to become involved with it is now.”[vi]

How AI could provide solutions

To tackle food waste issues, AI (Artificial Intelligence) is increasingly being touted as a potential technological tool which can provide solutions. There are several use cases for AI to address food waste, for example:

  • Predictive analytics
  • Planning food use
  • AI shopper

We explore these in more detail below.

1. Predictive analytics

Predictive analytics is the use of big data and its subsequent analysis, it is a branch of advanced analytics which “makes predictions about future outcomes using historical data combined with statistical modelling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities.[vii]

Studies, such as that by McKinsey, identify significant financial benefits associated with the technology, with the consultant estimating that AI could unlock a $127 billion opportunity by 2030, should it be used to tackle food waste[viii]. Their research, carried out in partnership with the Ellen MacArthur Foundation and Google, argues that ‘AI can create, rather than extract value, and even protect and regenerate biological systems’[ix]. Google itself has a strong interest in this space, with their agtech company Mineral recently emerging from stealth. Mineral, which has already mapped and analysed 10% of the world’s farmland, hopes to provide actionable data and analytics for companies across the food, agriculture, and technology[x].

Another example is Afresh, a software company using AI to streamline forecasting and inventory management at food retailers, thus reducing wastage. So far, this has reportedly helped Afresh save more than 30 million pounds of food. Forbes quoted CEO Matt Schwarz as saying: “Food waste is often driven by an invisible factor: poor decisions. People decide to buy too much, retailers and distributors order too much, and growers grow too much. AI optimizes decision making—so when applied to food, it holds the potential to prevent billions of pounds of food waste annually.”[xi]

Further use cases of predictive analytics relate to sensors and machine learning. By placing sensors on farmland, crops can be monitored to control growth conditions- for example, inform changes in water supply and identify the best time for harvesting. Further, environmental factors can also be added, with forecasts used to predict crop yields as weather patterns evolve. Sensors also have a role to play post-harvest, allowing monitoring of food as it is stored to provide a more accurate assessment of shelf life, thus reducing waste.

2. Planning food use

AI holds the potential to create unique food profiles for consumers. Knowing an individual's tastes and preferences, AI could be used to build out a meal planner. This would include meal and food suggestions which are more environmentally conscious, for example, swapping beef for chicken or encouraging the consumption of more vegetables. AI would be central to this given its ability to analyse feedback and adjust its recommendations to better suit each individual user. There are benefits too for retailers, where AI can inform them of each customers individual needs and enable them to target offers and promotions more accurately.

3. AI shopper

With estimates that 13% of food waste occurs from retail[xii], there is much more that can be done to combat this. Much waste in retail stems from overstocking, where consumer behaviour is often hard to predict and leads to over-purchasing by retailers. One alternative could be to reverse the traditional system, that awaits consumers to make choices and dictate demand- either in-store or online. Instead, retailers could utilise AI to make purchases on behalf of consumers, essentially a personal shopper who operates based upon a detailed consumer profile. The AI shopper would be aware of stock levels and use by dates, enabling it to promote the best food items for each consumer; it could also make any necessary substitutions which, based upon consumer profiles, would still be satisfactory but would prevent spoilage. An AI shopper could also help reduce food waste by the consumer, encouraging the correct portion size or quantity of produce. Coupled with AI planning for food use discussed above, consumers would also benefit from education on how to cook and utilise food products.


[i] UNEP Food Waste Index Report 2021 | UNEP - UN Environment Programme

[ii] Ibid

[iii] Global Food Loss and Waste (

[iv] WRAP’s new ‘123 pledge’ launched to coincide with COP27, aiming to combat the growing issue of food loss and waste across the globe | Zero Carbon Academy

[v] Food recycling could save businesses £7,000 a year | MRW

[vi] Ibid

[vii] What is predictive analytics? | IBM

[viii] How AI can unlock a $127B opportunity by reducing food waste | McKinsey & Company

[ix] Ibid

[x] Five Food Technologies To Curb Climate Change (

[xi] Ibid

[xii] UNEP Food Waste Index Report 2021 | UNEP - UN Environment Programme

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Lauren Foye
Head of Reports

Lauren has extensive experience as an analyst and market researcher in the digital technology and travel sectors. She has a background in researching and forecasting emerging technologies, with a particular passion for the Videogames and eSports industries. She joined the Critical Information Group as Head of Reports and Market Research at GRC World Forums, and leads the content and data research team at the Zero Carbon Academy. “What drew me to the academy is the opportunity to add content and commentary around sustainability across a wealth of industries and sectors.”

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