Maple Syrup, Hockey and AI: Economy-Wide AI Adoption as a Nation-Building Project

By Joël Blit, Danielle Goldfarb, Paul Samson and Stephen Tapp

June 26, 2025

Canada faces a productivity and economic growth crisis. Over the past 50 years, our output per hour worked has grown more slowly than every other G7 country.1 Most recently, we have fallen behind not just relative to others, but in absolute terms as well, with GDP per capita down 2.5% from its peak in the second quarter of 2022.2 The trade crisis further worsens the outlook.

Artificial intelligence (AI) presents a historic opportunity to change Canada’s trajectory. Broad-based AI adoption is Canada’s best way to reverse the productivity decline, raise living standards and strengthen the country’s economic competitiveness. Rapid AI advances now let non-experts use AI across almost any domain or application using natural language, making it a general purpose tool to drive productivity gains across all sectors of the economy.

Adoption will drive economic gains. Most of the economic value generated by AI will come not from building new models, but from integrating AI across the economy. Thus far, Canada has spent far more effort on research leadership, computing infrastructure, and AI risk than on driving adoption. By contrast, many peer countries have made AI adoption a key pillar of their economic strategy.

Canadians are key AI inventors, but slow to use AI. Two of the three “godfathers” of AI and many of the latest AI innovators did their pioneering work here in Canada, but other countries are leading in commercialization and uptake. Our business adoption rates lag peers, and Canadians remain among the most guarded and cautious about AI according to survey data.3 If we fail to address these deficits, Canada will not only miss a historic opportunity, but we will also fall further behind.

Canada needs an economy-wide AI adoption strategy. The appointment of Canada’s first minister of artificial intelligence and digital innovation, the prime minister’s mandate letter, and the G7 Statement on AI, including the new G7 AI Adoption Roadmap and the AI Adoption Blueprint, show that Ottawa recognizes the adoption imperative.

We are launching the Canadian AI Adoption Initiative. Our goal is to advise government on the best metrics to track and the best policy levers to pull, to enhance Canada’s AI adoption strategy. Our first action was to bring together a small group of independent Canadian experts with deep AI adoption and public policy expertise to map out the most important and urgent policy issues.

Here are the 10 consensus priorities that emerged from our discussions:

  1. Frame AI adoption as a nation-building mission. The prime minister and Cabinet should frame AI adoption as a nation-building mission that spreads the benefits of AI widely across the economy. We need to demonstrate AI’s public value through targeted projects and results. Ottawa should demonstrate through words, public investment and actions how AI can advance the public interest. Public trust will grow when Canadians see the value of AI in action, understand how it works and believe they have a role to play in shaping its future. We should also continue to build on its successful investments in AI research as they form a key part of Canada’s advantage.
  2. Set ambitious, measurable adoption targets. The prime minister and the minister of digital innovation and AI should declare Canada’s goal to achieve the highest rate of AI adoption in the G7, focused on real productivity gains. A national target communicates urgency, ambition and a commitment to track and evaluate results.
  3. Launch an AI adoption research and data hub. Canada currently lacks a robust set of metrics that measure AI adoption and allow policy makers to adapt their strategies in real time. This research and data hub should curate a public open data set of metrics to measure the pace, breadth and intensity of adoption; real and perceived barriers to adoption; outcomes; and, in time, productivity impacts. This hub should leverage traditional Statistics Canada business survey data, along with various digital indicators (such as AI platform usage), and measures of adoption by workers — now that virtually anyone across any company can use AI tools. These agile, real-time measures will be vital to provide early warnings of where adoption is trailing to allow policy makers to quickly adjust their approach, if needed.
  4. Provide SMEs with AI starter packs. Most Canadian firms are small and face real adoption constraints. Governments should, therefore, provide starter packs that include pre-vetted tools, curated training data and use cases specific to their needs, including clear playbooks. These packs should focus on simple, high-impact tasks that improve productivity and reduce costs, freeing workers to focus on higher-value-added tasks. This aligns with the scope of the new G7 AI Adoption Blueprint commitment.
  5. Provide regulatory clarity. Because it is hard to predict where the biggest gains from AI will occur, the focus should be on creating a clear regulatory environment conducive to AI investments. This means regulation that is flexible enough to foster ongoing innovation. Governments should also establish national AI regulatory sandboxes to enable companies to test new applications. In order to move more quickly to provide clarity, Ottawa should separate AI regulation from legislation on online harms.
  6. Invest in dual-use AI that advances national priorities. Government should launch an AI Champions Fund for high‑impact projects in areas such as Arctic surveillance and monitoring, supply‑chain intelligence, and agricultural technology, with open access to public data and subsidized computing power. Ensure appropriate integration of frameworks related to new defence spending.
  7. Launch a national AI skills and training initiative. Fast-track broad digital-AI skills training and communicate the economic upside to shift the public narrative from fear or skepticism to opportunity. Governments need to launch a national public awareness campaign focused on practical AI literacy, across all levels of education — including continuing education — and across all types of businesses and governments. AI literacy and a culture of change are the foundation for widespread AI adoption. This could include educating managers (one reason for Canada’s literacy gap), an education tax credit for workers in priority sectors and a variety of courses, such as massive open online courses and public lectures.
  8. Build and maintain usable public data. Data availability can be a barrier to AI adoption, particularly among SMEs. Data sets must use common standards, be secure and protect privacy, and include metadata that makes them directly usable for training and fine-tuning AI systems.
  9. Identify and address adoption barriers. The early-stage investments, partnerships and identification of metrics should be geared to help drive policy with the objective of providing incentives and removing barriers to accelerating adoption.
  10. Demonstrate leadership through public sector adoption. Deploy AI tools across federal departments to show confidence, de-risk adoption for others, increase demand for new tools and showcase quick wins. Federal departments should adopt task-specific AI tools to improve service delivery and internal operations. These examples can reduce perceived risk, build public trust and create Canadian reference cases. Procurement should prioritize AI solutions that solve public problems and help scale Canadian firms.

1. Source: OECD, Data for 1970-2019.

2. Source: Statistics Canada. Table 36-10-0706-01.

3. 2025 Stanford HAI AI Index Report (p. 401) based on Ipsos polling.

In June 2025, the CAIAI convened an advisory group of independent AI adoption experts whose views guided this brief:

  • Paul Samson - President, Centre for International Governance Innovation
  • Grace Wright - Program Manager, Centre for International Governance Innovation
  • Avi Goldfarb - Professor, University of Toronto
  • Mark Daley - Chief AI Officer, University of Western Ontario
  • Anil Arora - Former Chief Statistician, Statistics Canada
  • Joël Blit - Associate Professor, University of Waterloo
  • Danielle Goldfarb - Senior Fellow, Munk School of Global Affairs & Public Policy
  • Stephen Tapp - Chief Executive Officer, Centre for the Study of Living Standards
  • Jaxson Khan - Senior Fellow, Munk School of Global Affairs & Public Policy
  • Sean Mullin - Former Economic Advisor, Office of the Prime Minister of Canada
  • Sonia Sennik - Chief Executive Officer, Creative Destruction Lab
  • Tracey Forrest - Research Director, Centre for International Governance Innovation
  • Sanjeev Gill - Associate Vice President, Innovation, University of Waterloo