But despite this promise, industry adoption still lags. Data-sharing remains limited and companies across the value chain have vastly different needs and capabilities. There are also few standards and data governance protocols in place, and more talent and skills are needed to keep pace with the technological wave.
All the same, progress is being made and the potential for AI in the food sector is huge. Key findings from the report are as follows:
Predictive analytics are accelerating R&D cycles in crop and food science. AI reduces the time and resources needed to experiment with new food products and turns traditional trial-and-error cycles into more efficient data-driven discoveries. Advanced models and simulations enable scientists to explore natural ingredients and processes by simulating thousands of conditions, configurations, and genetic variations until they crack the right combination.
AI is bringing data-driven insights to a fragmented supply chain. AI can revolutionize the food industry’s complex value chain by breaking operational silos and translating vast streams of data into actionable intelligence. Notably, large language models (LLMs) and chatbots can serve as digital interpreters, democratizing access to data analysis for farmers and growers, and enabling more informed, strategic decisions by food companies.
Partnerships are crucial for maximizing respective strengths. While large agricultural companies lead in AI implementation, promising breakthroughs often emerge from strategic collaborations that leverage complementary strengths with academic institutions and startups. Large companies contribute extensive datasets and industry experience, while startups bring innovation, creativity, and a clean data slate. Combining expertise in a collaborative approach can increase the uptake of AI.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
