AI in Agriculture: You’re Already Using it- Here’s How to Use It Well

Written by Samantha Bourke

When people hear the term artificial intelligence, it can feel intimidating or even a little futuristic. But one of the most important takeaways from our recent AI in Agriculture & Communications session is this, “You’re already using AI, even if you don’t realize it”. From voice assistants and predictive text to search tools and content recommendations, AI has quietly become part of our everyday lives. The real question isn’t whether AI will affect agriculture but how do we use it responsibly and in ways to strengthen our communities?

Our session was led virtually by Loren King, Supervisor of AI and Insights at Morgan Myers, who was raised on his family farm, participated in 4-H, FFA, and Agriculture Future of America. Loren’s perspective of agriculture is important for his role in AI today. AI is not just a tech conversation; it is a people conversation. It is about using new tools while staying rooted in values like stewardship, trust and service.

To get everyone up to speed and to understand AI, here is a good overall summary of AI. At its core, artificial intelligence refers to computer systems that can analyze information, recognize patterns, and generate responses that are meant to feel human like. Prompts, models, agents and training data are what powers AI. It is a tool and should be treated as a tool. AI has an underlying system that makes it sound human such as using contractions, can misspell and will try to give you an answer to please use. Who doesn’t enjoy being told they have good ideas? AI is trained on half of the internet. So, as it does have a lot of information to share with you, it can also lack in the accuracy of some information as well. What you put in is what you will get out. Here is an example of how a crop farmer might use AI. You want to find out when you should start planting so you would want to give AI several sources of information such as, historical field yield data, soil type and moisture readings, weather forecasts, and temperature of the soil. This isn’t futuristic farming; it is an extension of how farmers already think. AI just speeds up the observation and improves the quality of information.

It is good to know that not all AI tools are the same. Loren discussed the differences between Public AI and Private AI. Public AI tools are widely available and easy to use. They are great for brainstorming and exploring different scenarios. But they are not great for sensitive information as they are not connected to your internal systems. Examples of Public AI tools are ChatGPT, Google Gemini, and Meta AI. Private AI is designed inside an organization, company, or system with controls over data access, storage and training. Examples of Private AI tools include Microsoft Copilot, Claude, and internal AI bots, such as the new IALF practice bot. The IALF practice bot was designed as another resource to

help us within our leadership journey. It can simulate interviews or stakeholder questions and draft responses in our organization voice, for example. Loren described in best when describing the differences between Private and Public. “Public is like a bus; it is free and you can be seen by others on the bus or looking in. Private is like a car; expensive and you cannot be seen by others outside your car.”

When we talk about artificial intelligence in agriculture, it is helpful to remember that farmers have been using a form of “AI” long before we ever called it that. One of the earliest and most powerful examples is artificial insemination. Artificial insemination was a breakthrough that used data, genetics, and predictive decision making to improve herd health and productivity. Farmers used this tool to help make better decisions for their operations. Over time, this innovation transformed livestock production and became a trusted practice.

AI is already influencing agriculture and food systems in meaningful ways. It is supporting genetics and agronomy research, answering complex farm and policy questions, monitoring food systems and health trends, and helping consumers decide what to cook, buy and grow. What we see coming next for agriculture is voice assistants built specifically for agriculture, greater automation and robotics, smart sensors and real-time monitoring, and personalized tools that learn and adapt over time.

There are challenges with AI that can’t be ignored either. It is powerful but it is not perfect. Some of the biggest challenges include bias toward certain groups or perspectives, “hallucinations” where AI provides confident but incorrect information, increased energy and resource use, data privacy and ownership concerns, and the risk of AI reflecting or reinforcing our own beliefs. That is why human judgement still matters. AI should support decision-making, not replace it.

Rather than fearing AI, this session also encouraged us to choose how we they think about AI. We need to treat AI as an idea partner, not the authority calling all the shots. Ask AI creative and even impossible questions. Use AI to expand your learning capacity but don’t let it outsource your thinking. A good practice Loren told us to use to practice the AI tool is to write down a few pain points or challenges you face, ask AI three different ways to approach each one, test those ideas using at least three AI tools, and finally, reflect on what worked, what didn’t and what you’d change. From summarizing meetings and scanning documents to drafting communications and monitoring trends, many everyday tasks are already well-suited for AI support.

AI is not a replacement for leadership experience or human connections. But when used intentionally, it can help us work smarter, communicate more clearly, and lead more

effectively in agriculture and beyond. As this technology continues to evolve, the most important thing to remember is that people who care about the future of food, farming and their communities will always remain the same.