The Fields AI Forgot

AI has become the default answer in every boardroom on earth. We talk about it endlessly in finance, healthcare, logistics, and consumer tech. Yet there is a whole category of industries the conversation has quietly walked past, the ones that feed and sustain the world, where the technology gap is wide and the value waiting to be unlocked is enormous.

Agriculture sits right at the top of that list.

The farmers running large-scale operations in Egypt, Indonesia, or India are not sitting around debating machine learning pipelines. Plenty of them still work from intuition and tradition passed down over generations. I have seen this up close in my own work across Southeast Asian markets, where so much of the value chain still runs on instinct rather than data. I say that without a shred of judgement; it simply reflects how far the technology conversation has reached, and how far it still has to go.

The applications here are genuinely transformative. Pair soil health monitoring with AI and a farmer can know exactly when and what to plant based on the composition of the ground, swapping calendar-based habits for data-led decisions. Irrigation is another goldmine. Overwater a crop and you damage it while wasting a precious resource; underwater it and you do the same kind of harm. Systems that track soil moisture continuously and cross-reference it against weather data keep irrigation finely calibrated, cutting waste and lifting yield at the same time.

Then comes forecasting, my favourite part. Combine soil health data, irrigation records, and weather prediction, and AI can hand a farmer a reliable estimate of harvest quantity and quality before a single crop leaves the ground. For operations running on forward contracts and supply chain commitments, that kind of foresight carries serious commercial weight.

Companies like Cropin and Agrivi are already deep in this space. Cropin has built AI-powered platforms for agricultural intelligence across Asia and Africa, while Agrivi pulls weather data, pest risk analysis, and operational planning into one farm management system. The largest agricultural projects in high-output countries would do well to treat these players as genuine strategic partners rather than line-item vendors.

The barriers are real, of course: connectivity, literacy, infrastructure, cost. The return on clearing them is just as real. AI has already reshaped the industries that were ready and waiting for it. Agriculture is the one sitting furthest behind the curve, and it is precisely where catching up would be felt most directly, on plates and in livelihoods around the world.