Editor’s note: This is the fifth article since May 20, 2026 in an ongoing series by Dr. Andrew Maxwell, the Bergeron Chair in Technology Entrepreneurship in the Lassonde School of Engineering at York University. Every week – and occasionally every other week – we’ll present a new article by Maxwell, in a series whose wide-ranging and incisive themes encompass: Canada and innovation policy; productivity and industry; innovation frameworks; AI and higher education; research and intellectual property; technology adoption; entrepreneurship and commercialization; universities and higher education; entrepreneurship education; and AI and the future of work. 

When the federal Minister of Digital Innovation and AI invited ideas on how Canada can lead in this emerging field, the response across the country was predictable – calls for more investment, more startups and more research.
But if we want to turn AI into Canada’s next productivity revolution, we need to go further. We need to ask harder questions about how we adopt, learn and govern AI as a system of transformation – not just as another technology sector.

Canada’s world-class research base gives us a head-start, but the real challenge is deployment.
AI will not create value until it becomes woven into the everyday operations of our schools, hospitals, governments, and industries.
That shift – from research to results – will define whether AI becomes another Canadian discovery that others commercialize, or the foundation of our own national renewal.

From research to experimentation

Canada’s AI ecosystem – which includes the Vector Institute, Mila, Amii, and CIFAR – has established the country as global leaders in discovery. But as AI pioneer Geoffrey Hinton has long argued, the true value of AI lies in deployment, not discovery.

We need to move beyond research excellence toward a culture of experimentation.
Universities should evolve into living labs where researchers, policymakers and industry partners test and refine AI systems in real-world settings – from classrooms to city streets. Every research investment should include a deployment pathway, with funding that supports pilots, evaluation and diffusion.
In short: we need to learn by doing.

From talent to transformation

Canada produces extraordinary technical talent, but we don’t train enough people to work with AI, rather than merely study it.
AI literacy must become the new baseline of professional education. Just as the space race catalyzed STEM in the 1960s, the AI era demands national investment in AI fluency – not only for engineers and computer scientists, but for teachers, civil servants and entrepreneurs.

At York University, we’re already embedding AI into experiential learning. Students use generative tools to enhance design thinking, improve reflection and test ideas faster.

The next step is scaling this mindset is nationally empowering professionals to see AI as a creative partner in innovation, not a threat to it.

Adoption: The real policy gap

The single biggest barrier to Canada’s AI transformation is adoption.
Who funds it? Who owns it? Who ensures it benefits everyone?

We have structures for physical infrastructure but none for digital transformation.
Is AI adoption a federal responsibility through Innovation, Science and Economic Development Canada? A provincial mandate? Or an afterthought in departmental budgets?
Without a clear governance model, adoption stalls.

We need a National AI Adoption Mandate – a framework that funds demonstration projects, supports public-sector exemplars, and establishes regional AI Adoption Hubs modeled on Germany’s Fraunhofer institutes.
These hubs should help small and medium-sized enterprises implement proven solutions, share data and develop workforce skills.

Adoption also has consequences. AI will disrupt jobs and institutions just as industrial automation did. Canada must invest in transition frameworks – reskilling programs and micro-credentials that allow workers to shift into higher-value roles rather than be displaced by automation.

[Editor’s note: See the federal government’s new AI for All strategy, AI for All – released after this article by Maxwell – which includes a $500-million Canadian Tech Growth Fund, to provide growth capital, investment support and occasional federal equity investment in Canadian AI firms. The strategy also invests $130 million for commercialization programs across the three national AI institutes, and will establish a National AI Literacy Initiative that will offer entry-level AI training for all Canadians].

 The fiscal transformation ahead

AI will change not only what we do – but how governments spend.
In health care, AI diagnostics can expand capacity without expanding payrolls.
In education, adaptive learning can personalize teaching and reduce dropout rates.
In defence, AI can enhance readiness without proportional costs.

This is a paradigm shift: a government that learns to do more with less.
But realizing that potential requires a new fiscal mindset – one that treats AI-enabled productivity as an investment, not a cost-saving exercise to be clawed back.

As I’ve previously argued, productivity growth won’t come from generic enthusiasm for technology; it will come from embedding AI into the operational fabric of public life.

From commercialization to uptake

Commercialization has long been Canada’s weak link.
But in AI, the problem is not invention – it’s uptake.
We need to move from a culture of “owning IP” to one of “deploying intelligence.”

Government can help by:

• Using procurement as a catalyst – buying and testing Canadian AI solutions in public operations.
• Creating deployment finance tools – such as flow-through credits or productivity bonds that help firms invest in AI implementation.
• Measuring success by outcomes – productivity gains, equity, and resilience, not patent counts.

Commercialization should mean widespread benefit, not isolated success stories.

Governing AI as a learning discipline

AI policy should itself become an experimental science.
Governments launch countless digital initiatives but rarely evaluate what works.
We need to apply the same evidence-driven approach to policy that we apply to technology.

A national AI Systems and Policy Observatory could track adoption rates, share lessons and ensure accountability across ministries.
This would allow government to learn from failures instead of burying them – and to adapt faster than the technologies it governs.

As Rita McGrath, an expert on business strategy, innovation and leadership, reminded us, “snow melts from the edges.” Disruption begins quietly and becomes obvious only when it’s too late.
The edges are already melting in our public systems; the only question is whether we will see it in time to act.

Data sovereignty and trust

No AI strategy can succeed without data sovereignty.
Control over data means control over decisions.
If our critical datasets – health records, mobility patterns, energy systems – reside in foreign clouds or corporate silos, Canada loses both competitiveness and policy autonomy.

We need a national data-trust framework that balances innovation with privacy, and makes trusted data accessible for research, startups, and policy design alike.
Public trust in AI begins with confidence in who owns and governs our data.

The real mandate: Learning to .earn

AI will not just change our tools; it will change how we learn, adapt and lead.
The next decade of Canadian innovation will not be decided by how much AI we invent, but by how effectively we integrate it into our schools, governments and companies.

We must treat AI adoption as a form of national learning.
If we embed experimentation, measurement and reflection into everything we do, Canada can become a living laboratory for responsible, evidence-based innovation.
If we fail, we risk another lost decade of productivity and potential.

This is an opportunity to move past the rhetoric of innovation and start building systems that learn faster than the world changes.
That is the true challenge – and the true promise – of artificial intelligence.

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