Canada’s ambitions in artificial intelligence are often framed around software innovation, computing power and digital services. Policy debates focus on generative AI, productivity tools and automated decision-making in offices, schools and government.
These discussions matter. But they overlook an emerging frontier of artificial intelligence that is beginning to transform how societies manage living systems.
The next frontier of artificial intelligence will not only be digital. It will be biological.
Across agriculture, food production and environmental monitoring, a new class of systems is beginning to emerge: biological digital twins. These systems create dynamic computational models of living environments by integrating continuous data from animals, infrastructure, climate conditions and operational activities.
Unlike traditional monitoring tools, digital twins do not simply report what is happening. They simulate how a living system is likely to respond before decisions are made.
In sectors such as aerospace, advanced manufacturing and energy, digital twins have already transformed how complex infrastructure is managed. Engineers can test changes in virtual models before applying them to physical systems. Equipment failures can be predicted earlier and performance can be optimized continuously.
A similar transformation is beginning to take shape in food production and that creates an opportunity for Canada.
If the country engages early, it can help shape both the technological and institutional architecture surrounding biological digital twins. If it waits until these systems are standardized globally, Canadian producers and policymakers may instead find themselves adapting to frameworks designed elsewhere.
There are three practical steps that Ottawa can take now: recognize biological digital twins as emerging strategic infrastructure rather than simply another category of agricultural technology; invest in real-world demonstration environments; and develop governance frameworks alongside technological innovation.
A fast-changing agri-food world
Modern farms already generate large volumes of data. Sensors monitor animal behaviour and health. Cameras track movement and feeding patterns. Environmental systems measure air quality, humidity and temperature. Automated equipment records production levels and operational conditions.
Yet these technologies often operate as separate tools. One system monitors barn climate. Another tracks feeding patterns. Another detects potential health problems. Farmers receive alerts from multiple platforms but rarely see how decisions affect the biological system as a whole.
Biological digital twins change that.
A digital twin creates a continuously updated model of a farm as an integrated living environment. It connects animal welfare, barn climate, energy consumption, productivity and emissions indicators within a single analytical system. Farmers and managers can evaluate management strategies virtually before implementing them.
Feed adjustments can be assessed for their potential impact on methane intensity and milk production. Ventilation strategies can be evaluated against both animal comfort and electricity use. Early disease signals may appear through combined analysis of behavioural, acoustic and environmental data.
In effect, the digital twin becomes an operating system for biological production environments.
Leadership needed
Canada should recognize that this emerging technology represents more than another agricultural innovation. It signals the development of a new layer of infrastructure at the intersection of artificial intelligence, climate accountability and food-system governance.
Globally, the race to lead in AI is often framed around large language models, semiconductor supply chains and consumer software platforms. Those areas are important, but they are also crowded. Many countries are competing for leadership in the same technological spaces.
Canada’s strategic opportunity may lie elsewhere: in applied artificial intelligence for complex real-world systems.
Biological digital twins sit precisely at the intersection of several Canadian strengths. The country has a globally significant agri-food sector, strong research capacity in artificial intelligence and environmental science, and a tradition that emphasizes responsible technology governance.
Food systems are also entering a period of rapid transformation. Producers face increasing pressure to document environmental impacts, demonstrate animal-welfare standards and provide traceability across supply chains. Markets and regulators are demanding more reliable data about how food is produced and how production systems interact with climate and ecosystems.
In this environment, the ability to model and manage biological systems with intelligent infrastructure will become increasingly valuable.
However, the emergence of biological digital twins also raises important policy questions that existing AI strategies rarely address.
Who governs the data generated by living production systems? Who owns predictive models derived from animal behaviour, environmental signals and operational performance? What reliability standards should apply when AI systems operate continuously in biological environments rather than controlled digital settings?
Governments will also need to consider how these systems interact with environmental reporting, food certification programs and animal-welfare oversight. If digital infrastructure becomes necessary for market participation, policymakers must ensure that smaller producers are not structurally disadvantaged.
These questions are beginning to surface internationally, but governance frameworks remain largely undeveloped.
Three practical steps that Canada can take
First, recognize biological digital twins as emerging strategic infrastructure rather than simply another category of agricultural technology. These systems connect artificial intelligence with climate monitoring, environmental verification and food-production management.
Second, invest in real-world demonstration environments. Canada needs living laboratories where researchers, producers and regulators can evaluate digital-twin technologies under realistic biological and climatic conditions.
Third, develop governance frameworks alongside technological innovation. Standards for data stewardship, interoperability and system reliability should evolve in parallel with technological development rather than after widespread adoption.
Public trust will depend on demonstrating that intelligent systems embedded in food production operate transparently, responsibly and in the public interest.
The significance of biological digital twins extends well beyond agriculture.
Climate change, biodiversity pressures and rising global food demand are increasing the complexity of managing living systems. Governments and industries will increasingly rely on predictive tools to balance productivity, environmental sustainability and risk management.
Artificial intelligence capable of modelling living environments responsibly will become a critical capability.
Canada often describes itself as a leader in responsible artificial intelligence. Achieving that ambition will require looking beyond software platforms and urban applications.
Some of the most consequential AI systems of the coming decades will operate in farms, forests, supply chains and environmental monitoring networks where digital intelligence interacts directly with living systems.
Biological digital twins represent one of the earliest manifestations of that shift.
Canada has the research capacity, agricultural diversity and governance credibility to help define this emerging field. Doing so would not only strengthen domestic innovation. It would position the country at the forefront of the emerging intersection of artificial intelligence, climate adaptation and food security.
If Canada wants to lead where AI, climate and living systems converge, biological digital twins are a frontier worth claiming.
