Artificial intelligence (AI) is fundamentally reshaping the contours of life as we know it. In agriculture, the world market for AI is expected to reach almost US$47 billion by 2034. AI enables higher farm yields with fewer inputs, an outcome that matters deeply in an era of climate uncertainty and resource scarcity.
In Canada, agricultural policymakers and industry leaders are gradually waking up to the promise of AI. However, as Canada’s new AI for All strategy recognizes, technology alone will not deliver the much-desired transformation while there is an “adoption gap.”
Canada lags behind other G7 countries in system-wide transformation of the agricultural sector. The problem is not a lack of sophisticated tools. It is a lack of systems that help farmers understand, integrate and trust these technologies.
I led my research team at Brock University in a two-year study of agricultural automation and robotics in Ontario. We found that while many technologies were technically sound and commercially available, adoption was constrained by broader structural factors. Our findings apply to AI-enabled agricultural technologies Canada-wide.
The promise for farmers
In agriculture, tools such as Farmer Chat, AgPal and Root AI are transforming farmers’ lives globally with real-time, data-based advice.
Smart sensors monitor soil moisture, nutrient levels and pH. Drones and satellites capture high-resolution field imagery. AI systems synthesize these data to identify where crops are under stress and, in milliseconds, determine which interventions are needed, sometimes at the precise scale of a few square metres.
AI-based early detection of diseases and pests allows producers to intervene before problems become visible. Computer vision systems can identify conditions such as yellow rust or blight days or weeks earlier than manual scouting, reducing crop losses and pesticide use. Irrigation platforms such as CropX dynamically adjust water application based on soil and weather data, sometimes reducing water use by up to 50 per cent.
We are seeing similar trends in livestock production. Farmers use sensors, cameras and machine learning models to monitor animal health, detect lameness and identify early signs of diseases such as mastitis before outbreaks spread across herds.
(AP Photo/Piyush Nagpal)
Why adoption keeps stalling
Our research reveals three barriers to AI adoption. First, many farmers remain unaware of which AI tools exist and which ones are relevant to their operations. I call this the information gap syndrome.
Second, others struggle to integrate new systems with existing equipment, data platforms and workflows. I call this the mismatch syndrome.
Third, innovation system networks are often unco-ordinated, with universities, technology firms, extension services and producers working in silos rather than collaboratively. This, I call the fragmentation syndrome.
The combined effect of these challenges is that support structures are weak, limiting opportunities for shared learning and co-ordinated uptake of new technologies.
(AP Photo/Peter Dejong)
Systems matter more than tools
To unlock AI’s potential and curb its hazards, Canadian agricultural policy needs to be grounded in what my research team calls an agricultural innovation systems approach.
This perspective treats innovation as a networked process involving researchers, farmers, agri-entrepreneurs, policymakers and intermediary organizations that connect them.
A key tenet of this approach is the importance of regional context in a country as geographically vast as Canada. What works for intensive dairy systems in Quebéc may not for grain producers in Saskatchewan or horticultural operations in British Columbia and Ontario. Canada’s geographic scale and production diversity mean that well-intentioned national solutions often fall short.
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In my book, I explained the key elements of innovation systems as governance architecture. Seen through this lens, AI can serve as a powerful, transformative tool for boosting productivity and ecological stewardship in Canadian agriculture.
When properly governed within a calibrated, regionally grounded innovation ecosystem, AI can support shared learning, improve knowledge exchange and help bridge gaps between technology developers and end users.
When poorly deployed, it can amplify misinformation and reproduce bias embedded in training data. It can narrow rather than expand farmers’ decision-making capacity, sabotage data privacy and ownership, and ultimately undermine trust in AI-enabled tools.
Moving from promise to practice
Turning AI’s promise into durable change requires nothing less than fundamental, system-level change toward more co-ordinated action. Given Canada’s vast geography, policy must also strengthen regional innovation systems rather than relying on one-size-fits-all programs.
Intergovernmental efforts — an approach I refer to as multi-level governance — grounded in regional innovation support ecosystems, can close knowledge gaps through training programs for farmers that focus on integration and use, rather than simply promoting technology.
AI will not transform Canadian agriculture on its own. However, when embedded within an effective innovation systems governance architecture, it can make a radical contribution to a more competitive, sustainable and resilient agrifood system.
