The use of AI in the UK food system

What is the issue?

For the purposes of this study, the food system refers to the stages of food supply, food production, food processing, distribution, consumption and food waste/disposal.

In recent years, artificial intelligence (AI) has gained increasing prominence and utilisation in multiple sectors, including in that of food. AI offers many benefits and opportunities for the food system – increasing the efficiency and effectiveness of tasks across the food system stages, providing economic, social and sustainability benefits, and helping to solve current challenges like the rising demand for food necessitated by the growing world population, climate change, labour shortages and other economic pressures. However, it also raises concerns around potential failures in transparency around the algorithms in use, and around data access and its use infringing privacy or exposing trade secrets.

With increasing use of AI across the agriculture sector, it is important to stay abreast of what gaps the technology is filling, where technology can be further optimised, and what barriers and challenges may be encountered or may emerge as a result of use of technology. This research can help provide a more rounded view of the promises and risks that accompany the use of AI tools in the food sector.

How did we help?

The Food Standards Agency (FSA) commissioned RAND Europe to undertake research to better understand the use of AI in the food system, specifically in the UK context. This research aimed to help the FSA to understand how AI is spanning multiple use cases across the food system; what risks and challenges it may present in terms of food safety; what steps the FSA could take to ensure that it supports and stays abreast of innovation in the food system; and for the FSA to understand where further research is warranted. The study focused on the following topics:

• The current focus of research on AI in the food system.
• The areas and the purposes for which AI is likely to have the greatest applicability in the future (next 10 years) in the food system.
• Where AI tools are currently being used within the food system.

This project utilised a rapid evidence assessment, horizon scanning and scientometric analysis to develop a high-level overview of AI research and use cases across the UK food system.

What did we find?

Levels of research globally into AI have been consistently high over the last six years, however, research within UK academia appears to be concentrated at the early stages of research, focusing on testing theory or developing new ideas, rather than on developing products for immediate application in industry or researching the impact of the implementation of new tools. Industry sources however unearthed multiple use cases of AI within the UK food industry. This suggests there may be a lack of systematic implementation of AI within the UK, with the use cases of AI being under-evaluated.

While AI use cases within the UK were present at each of the six stages of the food system: supply, production, processing, distribution, consumption and waste, the majority of published sources that we included in our analysis, highlighted that AI use was concentrated either at the beginning (food supply and production) or end (food waste) of the food system chain. The potential for scaling tools was identified at the food waste and food supply stages in particular, although specific barriers to scaling were also noted. Given the limitation of this rapid evidence assessment, it is possible that some UK-specific AI use cases might have been missed, and reliance on systematic reviews meant that UK-focused research was often underrepresented and thus excluded from scope. Grey literature also likely provides an underrepresented view of developments in industry due to commercial sensitivities, indicating a need for more comprehensive, systematic research. There was more limited evidence of use within the areas of food processing, distribution and consumption, although evidence from the grey literature searching and horizon scanning suggests there is growing interest in tools for individual consumers.

However, as a rapid evidence assessment, the methodology faced limitations due to its non-systematic nature. Some UK-specific AI use cases might have been missed, and reliance on systematic reviews often overlooked UK-focused research. Grey literature analysis was limited, with industry perspectives underrepresented due to commercial sensitivities, indicating a need for more comprehensive, systematic research.

What can be done?

A number of key areas for future research are suggested by this study, as well as wider recommendations which are applicable to multiple stakeholders operating across the varied facets of the food and agriculture ecosystem.

Recommendations:

1. Government institutions may consider proposing guidelines or codes of conduct, akin to the US executive order, to generate more transparency in the use of AI and underpinning algorithms in the food industry.
2. There is a need for more planning and investment in capacity building to support adoption of AI tools and technologies across the food supply chain.
3. More cross-disciplinary efforts are needed to assess AI utility where food systems interact with other areas.
4. More efforts demonstrating economic and social benefits of technology adoption are needed to engage with the public, and the food industry at large to drive technology acceptability underpinned by robust evidence outlining both risks and benefits.

Areas for further research

1. More systematic research into the use of AI in the UK food system is needed.
2. More research on industry practices and challenges on AI use and scalability is needed.
3. Gap analysis and stakeholder engagement could identify opportunities for tool development.
4. Global capabilities assessment could identify opportunities for the UK.
5. Developing rules of engagement for the use of AI tools in the food system could create transparency in the use of AI in the food sector.

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