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Agriculture Intelligence (AgI): Lessons from “Old School” Artificial Intelligence (AI)

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03-26-2026 12:51 PM
NickShort1
by Esri Contributor
Esri Contributor
2 0 147
‎03-26-2026 12:51 PM

This article reflects on the cyclical hype and setbacks experienced in the history of Artificial Intelligence, drawing parallels between the AI boom of the 1980s and current developments. It highlights how early AI efforts, particularly expert systems, failed due to inadequate data management infrastructure and the brittleness of knowledge representation. The discussion transitions to lessons for the agriculture industry, emphasizing the importance of robust, geospatially-driven data management as a foundation for future AI applications. The article notes the fragmented nature of agriculture and the challenges of achieving economies of scale, especially as the industry faces increasing pressure from global prices, climate change, and supply chain uncertainties. Ultimately, it argues that agriculture must learn from past AI failures by building data systems tailored to its unique characteristics to realize the potential of intelligent technologies.

The article:  Agriculture Intelligence (AgI): Lessons from “Old School” Artificial Intelligence (AI)

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Contributors
About the Author
Nick Short has dedicated his career to integrating Esri’s GIS technology into the agricultural sector, including a seven-year tenure working with Esri’s USDA account team. With over four decades of IT experience, he has specialized in AI, business intelligence, advanced analytics, GIS, and data management. His professional journey includes senior management roles at Gartner, SAP, SAS, and several Silicon Valley start-ups. Additionally, he spent a decade at NASA Goddard, where he focused on remote sensing, high performance computing, and AI within the Earth sciences and agriculture sector.