From bytes to bushels: How gen AI can shape the future of agriculture
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Complementing analytical AI with generative AI can create new value on and off the acre. A number of exciting use cases show how the industry can evolve.
Global demand for nutrition continues to increase, creating new economic pressures—and opportunities—for farmers. At the same time, the agriculture industry must contend with the push toward more sustainable practices.
The emergence of rapidly evolving technologies, such as AI, offers agriculture players another powerful tool to meet these challenges head on and unlock greater efficiency and effectiveness throughout their businesses. Generative AI (gen AI) in particular has captured the imaginations of many leaders in agriculture and beyond and could be the impetus to create significant change.
It has also brought to light the application of many other, long-existing approaches, such as analytical AI, with proven use cases and still relatively low levels of adoption.
When combined, analytical AI and gen AI have the potential to unlock value across the value chain and across business operations. This article explains how companies in the $4 trillion global food production industry can comprehensively strengthen their AI efforts by leveraging gen AI. Doing so can create economic value in two key areas: first, on the acre by improving on-farm economics such as labor and input costs and yields, and second, for the enterprise through increased sales growth, productivity, and operational efficiencies. Overall, our analysis shows that AI can create $100 billion in the former area and $150 billion in the latter.
Applying gen AI in agriculture
Generally speaking, “gen AI” refers to applications that process large and varied sets of unstructured data, including geospatial and weather data, and perform more than one task. In this way, gen AI can generate new ideas by identifying patterns in large unstructured data sets, particularly when it comes to complex tasks such as molecular research, marketing or agronomy, and code generation. By contrast, analytical AI typically solves specific tasks by making predictions based on well-structured data sets and predefined rules. Examples here include forecasting sales, segmenting customers, and conducting sentiment analysis.
Agriculture is particularly well suited for disruption by AI and gen AI because of its high volumes of unstructured data, significant reliance on labor, complex supply chain logistics, and long R&D cycles, as well as the sheer number of farmers who value customized offers and low-cost services. As an example, gen AI can develop testing scenarios by synthesizing millions of data points on weather, soil conditions, and pest and disease pressure, and analytical AI models can then simulate those scenarios. Using both technologies in tandem has the potential to increase efficiencies, lower costs, and improve environmental impact for all agricultural players.
The significant value at stakeAI can create significant value for agriculture in two key areas: 1) on the acre, which refers to crop and livestock production, and 2) for the enterprise, which refers to business functions.
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