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Artificial intelligence is advancing at an astonishing pace—improvements measured in five-thousand-fold increases from week to week according to AI researchers at Singularity University. Yet in agriculture, most farmers remain largely disconnected from these advances, using AI only at the most basic level for administrative tasks like finance, report writing, and form filling. This widening gap threatens to leave the agricultural sector further and further behind as the world accelerates into what might be called the "bullet train moment" of AI adoption.
Today we are joined by Aidan Connolly, CEO of Agritech Capital, who has spent the past years advising agricultural organizations on AI readiness. Aidan joins Ash Cloud to discuss how to restructure agricultural businesses for AI-driven decision-making without waiting for others to lead the way.
The big challenge is that AI systems can only work with the data available to them. In agriculture, that data is fundamentally flawed. Most farm information is still captured by humans in notebooks, then transcribed into databases. Each step introduces errors, misinterpretation, and gaps that make datasets unsuitable for the sophisticated decision-making AI promises to deliver. Meanwhile, the major AI companies racing forward at incomprehensible speeds won't wait for agriculture to catch up.
When you consider that farmers are constantly gathering sensory information through sight, sound, and observation while working in the field, data that never enters any database, the informational chasm becomes even more apparent. Yet animal production systems offer unique opportunities. Dairy cows milked three times daily provide recurring windows to assess milk quality, animal health, fertility, and nutrition. Hens laying eggs daily, pigs weighed multiple times, animals with collars and sensors, these continuous data streams contain vastly more untapped value than the sector has realized.
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