Will Artificial Intelligence Take Our Jobs in Agriculture? Or Will It Drive Better Outcomes and Productivity?
June 26, 2024 Editor’s note: In a recent issue of Upstream Ag Professional, agribusiness analyst Shane Thomas addresses the buzz surrounding conversations about the future of jobs in agribusiness and AI. Here’s a summary of that article:
This week, discussions focused on the future of jobs in agribusiness and AI, addressing the fear of job loss and the need for re-skilling. Historical context, like the Luddites’ resistance to mechanization in the 19th century, highlights the longstanding apprehension towards technological advancements. The Luddites feared losing their skilled jobs to mechanized looms, leading them to destructively protest against these innovations. However, mechanization was inevitable, driving economic progress and societal advancement, much like today’s AI technologies in agriculture.
The Lump of Labor Fallacy — the misconception that there is a fixed amount of work — explains why automation doesn’t reduce jobs overall. As automation makes production cheaper and more efficient, it creates new demands and, consequently, new jobs. This phenomenon, illustrated by Jevon’s Paradox, shows that gains in efficiency lead to increased demand and new economic opportunities.
To navigate these changes, the Job to Action Framework helps understand the transformation of job roles. For example, an agronomist’s job involves various tasks and actions, some of which can be automated. Automation tends to eliminate tedious tasks, allowing professionals to focus on more valuable responsibilities. For instance, with automated field scouting by technologies like Solinftec’s Solix, agronomists can devote more time to data analysis and strategic planning.
AI, particularly in agribusiness, offers significant benefits by improving data quality and reducing errors from user input. As Matthew Pryor of Tenacious Ventures suggests, AI can overcome the “garbage-in, garbage-out” challenge by enhancing data accuracy and utility. Similarly, Patrick Walther emphasizes AI’s potential to analyze unstructured data, providing insights from customer interactions and feedback.
Furthermore, AI can improve interoperability between different agtech systems, as highlighted by the ability of Large Language Models (LLMs) to develop APIs quickly, enhancing collaboration and data synthesis.
Overall, while AI may change the nature of certain jobs, it will likely eliminate tedious tasks and empower professionals to achieve more. AI will enable agribusiness professionals to leverage their capabilities more effectively, creating a more seamless and productive user experience. Thus, AI is more likely to transform and enhance jobs rather than eliminate them.
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