The Problem Isn't AI. It's Data.

Artificial intelligence is only as powerful as the data behind it. For our food economy, without better data, AI is just an expensive calculator.

Ottawa’s new national artificial intelligence strategy has generated considerable excitement. The federal government promises stronger productivity, economic growth, new jobs, and a more competitive Canada. These are worthy objectives, but Canadians should ask a simple question: what problem are we actually trying to solve?

In agriculture and food, the challenge is not a lack of AI. Farmers have been using predictive analytics, precision agriculture, machine learning, and automated equipment for years. Food manufacturers rely on artificial intelligence to improve efficiency and reduce waste. Retailers use advanced algorithms to forecast demand, manage inventory, and optimize logistics. Across the food value chain, AI is already deeply embedded in decision-making.

What the sector lacks is data.

Canada continues to struggle with fragmented food-system information, limited supply-chain visibility, inconsistent reporting, and a lack of real-time intelligence. Valuable information remains trapped in silos, making it difficult for businesses, governments, and consumers to make informed decisions. Artificial intelligence cannot compensate for poor-quality data. If anything, it magnifies the consequences of bad information.

The examples are everywhere. Canada still lacks a national, real-time food inventory monitoring system capable of tracking supply levels across major commodities. During the pandemic, governments and industry often struggled to assess shortages because no comprehensive data infrastructure existed. Food waste remains another blind spot. Canada wastes billions of dollars worth of food annually, yet we still cannot accurately measure where most of that waste occurs throughout the supply chain. Even food affordability is difficult to assess in real time. Statistics Canada publishes valuable information, but much of it is retrospective, leaving policymakers and businesses reacting to events rather than anticipating them.

Supply chains present another challenge. Canada is one of the world’s largest agricultural exporters, yet we lack a modern national platform capable of providing real-time visibility into transportation bottlenecks, port congestion, rail performance, inventory movements, and disruptions affecting food shipments. When rail strikes, port disputes, floods, or extreme weather events occur, decision-makers are often operating with incomplete information.

Traceability is another missed opportunity. While countries around the world are investing heavily in digital food traceability systems, Canada remains a patchwork of disconnected databases and reporting systems. In an era when consumers increasingly demand transparency about food origin, sustainability, and safety, this fragmented approach limits both competitiveness and innovation.

That is why Ottawa’s new strategy feels incomplete. While billions are being directed toward computing infrastructure, research, and adoption, much less attention is being paid to the data ecosystem that makes artificial intelligence useful in the first place. Canada does not need more algorithms nearly as much as it needs better information.

The broader economic lesson is equally important. Canada has never lacked innovation. The country has produced world-class researchers and breakthrough technologies, including many of the advances that underpin today’s AI revolution. Yet productivity growth remains weak, business investment lags behind key competitors, and GDP per capita continues to trail that of the United States. The issue has rarely been invention. It has been commercialization, execution, and productivity.

Canadians should not fear artificial intelligence. Nor should they assume it is a silver bullet. The success of Ottawa’s strategy will not be measured by the number of AI startups funded or computing centres built. It will be measured by whether businesses become more productive, whether wages rise, whether industries become more competitive, and whether taxpayers see a return on their investment.

Artificial intelligence may well become one of the defining technologies of our generation. But technology alone does not create prosperity. In food and agriculture, and arguably across much of the Canadian economy, the real opportunity lies not in making machines smarter. It lies in building smarter systems fueled by better data. Until that happens, Canada’s AI strategy risks treating the symptom rather than the disease.