Introduction

Agricultural supply chains have traditionally relied on intuition, experience, and traditional methods. However, in a world with expanding global markets, unpredictable weather, and fluctuating commodity prices, these old approaches are no longer enough. These systems face increasing pressure to become more efficient, transparent, and adaptable.

Even with data-focused strategies and automation, critical supply chain choices in agriculture still take hours or days of human analysis, simulation, and manual updates. Now, a shift is happening, fueled by large language models (LLMs) and generative AI. These models transform labor-intensive workflows into intelligent conversations, drastically reducing planning times from weeks to minutes.

This article examines how LLMs are transforming agricultural supply chains by using a fictional yet illustrative Agri-tech company as a case study. Their pilot deployment of AI across farming operations, processing facilities, and distribution centers shows both the potential and the hurdles of this technology.

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