Powering the food industry with AI

But despite this promise, industry adoption still lags. Data-sharing stays limited and corporations across the worth 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 maintain pace with the technological wave. 

All the identical, progress is being made and the potential for AI within the food sector is large. 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 latest 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 hundreds of conditions, configurations, and genetic variations until they crack the best 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 information into actionable intelligence. Notably, large language models (LLMs) and chatbots can function digital interpreters, democratizing access to data evaluation for farmers and growers, and enabling more informed, strategic decisions by food corporations. 

Partnerships are crucial for maximizing respective strengths. While large agricultural corporations lead in AI implementation, promising breakthroughs often emerge from strategic collaborations that leverage complementary strengths with academic institutions and startups. Large corporations 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. 

Download the total report.