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How Might Generative AI Affect Food System Jobs?
‘How might AI affect jobs in the food system?’ It was an intriguing question that came up in a recent office conversation — and one that is directly relevant to GAIN’s work that supports equitable and high-quality employment in food systems.
‘How might AI affect jobs in the food system?’ It was an intriguing question that came up in a recent office conversation — and one that is directly relevant to GAIN’s work that supports equitable and high-quality employment in food systems.
The rise of generative Artificial Intelligence (genAI) in recent years has led to excitement about the potential for higher productivity and new possibilities for what humans, helped by machines, can do – but also to anxiety about job loss, displacement, and inequitable outcomes for workers. This is no less acute in the food system, which employs about 1.2 billion people globally but already faces challenges with retaining workers, recruiting younger generations, and providing not just work but high-quality livelihoods. Bringing genAI into the mix could complicate an already challenging puzzle
The data
Moving beyond speculation
Fortunately, beyond just coffee break speculation, there’s data we can bring to answer this question. The International Labour Organization (ILO) and NASK, Poland’s National Research Institute, have developed a dataset that scores hundreds of occupations for their exposure to genAI automation.
This is based on a database of tasks associated with standard occupations — a global one, ISCO-08, containing 3,265 tasks for 427 occupations, and a Polish one containing nearly 30,000 tasks for about 2,500 occupations.
Using this, ILO/NASK surveyed Polish workers on the automatability of tasks associated with their own jobs; labour market experts then reviewed a subset of these task scores; where expert and survey scores diverged, two LLMs served as arbiters to produce an adjusted task score; and a predictive model was used to expand the scores to the full database of thousands of tasks. These task-level scores were then aggregated to the level of an occupation, considering both the mean ‘automatability’ of tasks and the standard deviation in that—representing the variability of tasks within the occupation.These were then classified into six categories: from not exposed (very low automation potential) to
high exposure across tasks, with low task variability.
But the ILO dataset covers occupations across the whole economy, not just food. To classify those related to the food system, we used Claude (Sonnet 5) to flag occupations in the food system. The author reviewed these and reclassified as needed. Where gaps were identified, occupations were added using the nearest proxy in the ISCO-08 data (e.g., ‘Messengers, Package Deliverers and Luggage Porters’ as a proxy for food delivery drivers)
The results are intriguing
As shown in the interactive figure above, most occupations, like fishers, food processing plant workers, and farmers, are considered ‘not exposed’, with a handful having minimal or low exposure, for example waiters, food lorry drivers, and only a few, retail and wholesale food trade managers, grocery shopkeepers, food technologists, agronomists and crop scientists, having moderate exposure. Only one has significant exposure, food systems researchers, proxied by ‘economists’, and none have high exposure.
This is in stark contrast to the ILO results for the economy as a whole, which show considerably larger shares of occupations in the moderate, significant, and high exposure categories, for roles like typists, accountants, and medical records technicians.