Beyond the hype: What AI really means on the farm

Ask a room full of farmers whether they’re using artificial intelligence (AI) and many would probably say “no.” Then ask who uses auto-steer, variable-rate fertilizer applications, robotic milking systems, yield maps or software that helps optimize finances and the answer changes.

The reality is that AI has been working behind the scenes on Canadian farms for years. Long before ChatGPT made AI part of everyday conversation, farmers were already using technology that could collect information, recognize patterns and support decision-making.

Technology is now evolving quickly, bringing new opportunities — and new questions — about where AI fits on the farm and whether or not farmers will be left behind if they don’t adopt these innovations.

“Many people think of AI as generative models like ChatGPT because that is the main tool they have been exposed to,” says Dr. Rozita Dara, professor in the School of Computer Science and director of the University of Guelph’s AI4Food initiative. “But, in reality, AI has been around for over 70 years.”

For Canadian farmers, the conversation isn’t really about adopting AI. It’s about deciding which technologies are worth investing in, which ones solve real problems and which are simply adding to the hype.

More common than you think

AI may feel like it has arrived overnight, but technology adoption rarely works that way.

Transformative technologies typically take time, requiring years of gradual adoption followed by rapid growth once the technology becomes easier to use and is proven to integrate with existing systems.

That’s exactly what Darrell Petras, CEO of the Canadian Agri-Food Automation and Intelligence Network (CAAIN), says he is seeing on Canadian farms. He believes that, in many cases, producers aren’t resisting technology; they’re waiting for evidence that it solves a real problem. He points to existing technology that was once novel, but that is now widely adopted, such as robotic milking systems that monitor animal health while collecting detailed production data. Other examples include GPS systems that help equipment stay on track, precision planting and harvesting tools that combine information from fields, and weather and machinery to support management decisions. These were novel technologies not that long ago.

“AI is impacting and being used on most Canadian farms to some degree,” says Petras. “Sometimes it’s apparent, and sometimes it’s happening behind the scenes.”

That doesn’t mean every sector in Canada’s agri-food system is moving at the same pace. Dara explains that environments such as dairy barns and greenhouses have adopted innovations like robotics more readily because the tasks are repetitive and the data is more structured. In these environments, sensors can continuously monitor animals or plants, feeding reliable information into AI systems that identify trends and help guide decisions.

Other sectors that are more variable or rely on uncontrollable elements, such as weather, disease, soil conditions and pests, may be slower to adopt AI because these technologies are still evolving to meet those complexities, require a higher level of investment and carry greater operational risk.

“Agriculture is a complex environment,” says Dara. She explains that complexity doesn’t mean certain sectors are being left behind, it simply means developing robust and reliable tools takes more time, and producers are more likely to adopt these technologies once they’ve proven their accuracy and return on investment under real farm conditions.

It all starts with data

Both Dara and Petras agree: AI is only as good as the information behind it.

Every yield map, production report, weather station, soil test and equipment sensor creates another data point that contributes to the overall picture. Individually, those data points don’t say much. Together, they can reveal patterns and trends that can help farmers make better decisions.

“Everything starts with data. When properly analyzed, that data tells a story about the farm,” says Dara, noting that, increasingly, AI is helping producers move beyond simply collecting information to actually using it. She points out that data is already helping producers identify disease risks earlier, fine-tune fertilizer recommendations, connect feeding programs to milk production, or help predict environmental conditions before they become problems.

But collecting data is only the first step. The next challenge is to make different systems work together. Today’s farms often use equipment and software from multiple manufacturers, each collecting similar information in different formats. Those systems don’t always communicate with one another, which makes it difficult to combine information into a complete picture of the operation.

Petras calls this interoperability — the ability for technologies to work seamlessly together — or the “holy grail” of technology adoption, he says. He believes that the more easily new tools fit into equipment and management systems producers already use, the easier they are to trust and to adopt.

Trust before technology

If technology exists, why isn’t everyone adopting it? According to Petras, the biggest barrier isn’t age or necessarily cost, it’s trust.

Producers want to know who owns their data, how it’s being used, whether systems are secure and, perhaps most importantly, whether the investment will actually pay for itself. He says producers typically approach new technology much like any other investment: they want to understand the risks, see proof that it works and know they’ll get a return on their investment.

That’s one reason CAAIN focuses on connecting technology developers with the end user. Through partnerships with a network of “smart farms” hosted by colleges, universities, and independent research organizations across Canada, CAAIN supports testing of new technologies under real-world conditions, giving developers valuable feedback while helping build producer confidence.

“Producers want proof. They want to hear from neighbours who have successfully implemented new systems, and they want to know there’s local service support if something breaks,” says Petras. Those factors all help build the trust and confidence needed before farmers are willing to invest.

According to Dara, transparency matters, too. Many AI systems operate as what she describes as a “black box,” offering recommendations without clearly explaining how those conclusions were reached. She believes that helping farmers understand how AI decisions are generated will be essential to build confidence.

Cybersecurity and data ownership are becoming equally important considerations. Farm data is an increasingly valuable asset, and producers are asking legitimate questions about who benefits when that information is shared.

“Until stronger business models and clearer governance emerge, hesitation is understandable,” Dara points out.

Solving problems that matter

“The most successful technologies won’t necessarily be the most complex. They will be the ones that solve the practical, everyday problems producers face,” says Dara, pointing to labour as the clearest example.

Whether it’s automated greenhouse climate controls, autonomous weeding equipment or sensor-guided sprayers, automation can reduce dependence on increasingly scarce skilled labour while collecting valuable management information. Dara notes that those data points can also improve sustainability, such as reducing unnecessary pesticide and fertilizer use, or AI models that can now predict disease outbreaks.

“The real value is giving producers precise information needed to make more informed, data-driven decisions,” says Dara.

When does waiting mean falling behind?

Every major technology reaches a tipping point.

Early on, the benefits are uncertain, and the costs are high. As more producers adopt the technology, costs come down, support improves and confidence grows.

That’s why Petras cautions against suggesting every producer needs to be the first to adopt — but he warns that there is an opportunity cost to waiting too long.

No matter the size of the operation, the longer farms postpone collecting quality data, the less information they have to support management decisions. Petras also points out that farms may also miss opportunities to improve efficiency, reduce risk exposure or expand their operations as technologies become increasingly integrated into agriculture.

“The other point to remember is that AI is constantly evolving,” adds Dara. She expects the next major shift to come from a more advanced level of AI — “agentic AI” — that will be capable of helping producers develop their own customized tools based on their specific data and needs, rather than relying solely on the technology provided by third-party tech providers.

For now, Dara expects robotics will continue expanding beyond dairy and greenhouse production into more complex field applications while advances in data sharing and computing promise even more sophisticated decision-support tools.

“AI isn’t about replacing farmers; it’s about empowering them,” she says. “In my opinion, AI will revolutionize the agricultural sector, but it cannot replace the knowledge and expertise of farmers.”

Human experience, intuition and judgement will remain essential, especially if there’s a data breach or breakdown. Dara points out that producers will still need to know how to perform tasks, process information and make decisions in the event that technology fails.

“The future of agriculture isn’t about handing decisions over to AI technology. It’s about empowering farmers with quality insights so that they can make informed decisions,” explains Dara.

As Petras puts it, the technologies that succeed will be the ones that earn producers’ trust by solving problems that matter. For Canadian farmers, that may be the most important lesson of all. AI isn’t another trend to chase. It’s another tool in the toolbox, and, like every other tool, its value depends on how, when and why it’s used.

Click here to see how farmers are using digital technology to stay competitive and reduce costs.

Click here to learn how digital technology can mirror your real-life farm to test decisions without risking real money.