Packaged food companies and agricultural manufacturers have applied AI to increase crop yields and develop more desirable food formulations for consumers. Advanced large language models allow food companies to integrate disparate information such as global tariff alerts, forecasts of fungal outbreaks that may require pesticides, and forecasts of strong winds that could affect moisture levels, enabling more informed decisions about growing and purchasing key ingredients. The global food production industry, valued around $4 trillion, could generate roughly $250 billion annually from AI-driven productivity gains. Those gains could offset rising global food commodity prices. Implementation challenges include recruiting technical talent and standardizing data across diverse supply-chain participants, from small family farms to large national retailers. Major food companies are pursuing these technologies despite challenges.
For years, packaged food companies and the agricultural manufacturers they work with have used AI to help increase crop yields and create more desirable food formulations for consumers. Now, the food-manufacturing industry has a new AI tool for boosting productivity on farms and in factories: advanced large language models. With this type of generative AI, food companies can pull together disparate pieces of information - like alerts for global tariffs,
The global food production industry, which is worth an estimated $4 trillion, has the potential to generate $250 billion in annual profits from AI's productivity potential, as per a 2024 McKinsey & Company report. These potential savings from more targeted labor and greater operational efficiencies in manufacturing come at a critical time, when global food commodity prices have increased to their highest level in two years in July, as per the United Nations' Food and Agriculture Organization.
For effective implementation, leaders must contend with how to apply AI to complex agricultural systems. Challenges include recruiting engineers, software developers, and data experts and organizing data uniformly across the supply chain - from small family-owned farms, which may have limited resources, to agricultural producers and large national retail chains, as per the McKinsey & Company report. Despite these challenges, big food manufacturers - like Land'O Lakes, PepsiCo, and the global agricultural
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