Canada’s AI Strategy Needs Governance Capacity, Not Just AI Adoption

Canada’s renewed National Artificial Intelligence Strategy, AI for All, marks an important shift in Canadian science and innovation policy. It recognizes that Canada’s AI challenge is no longer only about research excellence. It is also about adoption, trust, sovereignty, skills, public value, and democratic resilience. The strategy’s six pillars, namely protecting Canadians, empowering Canadians, powering AI adoption, building sovereign AI foundations, scaling Canadian champions, and building trusted partnerships, in my opinion rightly reflect the breadth of this challenge. Yet the success of “AI for All” will depend on more than ambitious targets, new programs, or stronger legislative tools. It will depend on whether Canada can build the institutional governance capacity required to coordinate these pillars as a coherent national project.

The strategy sets important goals: increasing business AI adoption from roughly 12 percent to 60 percent by 2034, creating up to 250,000 new AI-related jobs by 2031, offering AI-related work opportunities for young Canadians, expanding AI literacy, strengthening privacy and safety protections, investing in sovereign compute, and supporting Canadian AI companies. These are significant commitments. But they also reveal a deeper governance problem: AI policy is no longer contained within a single department, sector, or regulatory framework. It now cuts across privacy, cybersecurity, labour markets, education, procurement, industrial policy, research infrastructure, democratic institutions, health care, and international standards.

From a Science and Technology Studies perspective, AI governance should therefore be understood as a sociotechnical system rather than a narrow compliance exercise. AI systems do not enter society as isolated technical tools. They are embedded in organizations, data infrastructures, professional routines, markets, public institutions, and political choices. Their effects are shaped not only by model performance, but also by how institutions define risk, assign responsibility, interpret evidence, and respond to harm.

This matters directly for the first pillar: protecting Canadians and safeguarding democracy. Stronger privacy laws, online safety protections, transparency measures, watermarking tools, and AI safety evaluations are necessary. But rules alone will not secure public trust if Canadians experience AI governance as fragmented, reactive, or opaque. Trust requires visible accountability. It requires clear lines of responsibility across regulators, public agencies, developers, deployers, and users. It also requires mechanisms for affected communities to contest decisions, not merely receive technical explanations after systems are already deployed.

The second pillar, empowering Canadians, is equally important. AI literacy should not be treated only as workforce training or digital upskilling. It should also be civic capacity-building. Canadians need to understand not only how to use AI tools, but how AI systems classify people, allocate opportunities, generate misinformation, shape public services, and affect democratic participation. A truly inclusive AI literacy agenda should prepare Canadians to participate in governance, not only in adoption.

The third pillar, powering AI adoption, raises another crucial issue. Canada’s adoption gap is real, especially among small and medium-sized enterprises. But rapid adoption without governance maturity could reproduce the very harms the strategy seeks to avoid. SMEs, public agencies, and non-profits often lack the internal expertise to evaluate AI risks, manage data governance, monitor bias, assess vendor claims, or document system performance. Adoption supports should therefore include practical governance supports: model evaluation templates, procurement guidance, audit tools, sector-specific risk frameworks, and access to independent expertise.

The fourth pillar, building sovereign AI foundations, is perhaps the most strategically significant. Sovereignty is not simply a matter of owning compute infrastructure or keeping data within Canadian jurisdiction. It is also the capacity to govern AI on Canadian terms. Sovereign AI requires institutions that can evaluate systems, shape standards, coordinate public procurement, protect rights, and negotiate with powerful global technology providers. Without governance capacity, sovereignty risks becoming an infrastructure ambition without democratic substance.

The fifth pillar, scaling Canadian champions, must also be connected to responsible governance. Canadian firms should be supported to grow, retain intellectual property, and compete globally. But public support and procurement should be tied to clear expectations around safety, transparency, accountability, accessibility, and public value. Canada has an opportunity to make responsible AI governance a competitive advantage, not a regulatory burden.

Finally, the sixth pillar, trusted partnerships, recognizes that Canada cannot govern AI alone. International cooperation on standards, safety, compute, research, and democratic resilience is essential. But Canada should enter these partnerships with a clear governance identity. It should not merely align with larger jurisdictions. It should contribute a distinct model rooted in democratic accountability, institutional coordination, public trust, and inclusive innovation.

The central challenge for AI for All is therefore not whether Canada can promote AI. It is whether Canada can govern AI adoption at scale while preserving trust, opportunity, and sovereignty. Meeting that challenge requires moving beyond a policy model focused on individual programs and toward a governance model that connects institutions, standards, public participation, technical evaluation, and democratic accountability.

Canada has already shown that it can lead in AI research. The next test is whether it can lead in AI governance. If AI for All is to deliver on its promise, governance capacity must become the connective tissue of the strategy. Without it, the six pillars risk becoming parallel initiatives. With it, Canada can build an AI future that is not only innovative, but legitimate, resilient, and genuinely public-serving.