Much has been written about Canada’s lagging innovation and productivity indicators. The federal government has struggled to offer effective solutions, alternating between big gambles on select sectors and splashy announcements that end with a fizzle.

Earlier this month, however, Industry Minister François-Philippe Champagne quietly put out a press release detailing plans to allocate the Budget 2024 centrepiece: a $2-billion investment in artificial intelligence (AI). It signaled a welcome shift toward incorporating implementation and impact in Canada’s innovation ambitions.

An important element of Champagne’s announcement is the expansion of data centres—places where Canadian businesses and researchers can access the computer resources necessary to explore AI solutions. This approach presents an opportunity to leverage technology where our country is already a leader, and can share the benefits broadly.

While academics have conducted plenty of AI-focused research, most Canadians have a limited understanding of its potential. Adoption is barely on the radar. This limits economic growth, and fails to move the needle on productivity. To paraphrase Champagne: if adoption lags, Canada risks squandering AI’s immense potential. 

The recent announcement suggests a vast majority of federal AI investments will continue to flow to current players. Yet, a portion has notionally been set aside to offer compute resources that will prioritize public-private partnerships, accelerate AI adoption, and foster practical, industry-relevant research. If such models land, they have the potential to facilitate AI integration and strengthen partnerships within Canada’s AI ecosystem.

In the past, the Pan-Canadian Artificial Intelligence Strategy sought to address these issues by focusing on increasing AI patents as a metric of commercialization success. This approach missed the mark for small- and medium-sized enterprises (SMEs), which make up 98 per cent of the private sector and more often rely on security protocols and non-disclosure agreements than patents.

Applied research is a promising alternative. Demand-driven, it encourages industry to define the purpose of the research, putting innovation within reach of even the smallest company. As AI transitions from theoretical to practical, efforts must increasingly demonstrate how AI can be leveraged to enhance competitiveness, improve products, and foster growth.

In our vision, Canada’s global leadership in theoretical research at the three existing AI research centres would be supplemented by three new Applied AI Research Centres. The new facilities would focus on addressing the needs of entrepreneurs, community organizations, students, and others for whom the existing research centres were out of reach.

Canada’s polytechnics are ideal homes for these centres. Their applied research track records illustrate strong industry relationships and—most importantly—allow businesses to retain the intellectual property arising from their collaborations. Current AI research at polytechnics is dealing with pragmatic applications such as forest fire prevention, natural resource extraction, and health care.

As entrepreneurship incubators, polytechnics often provide space, equipment, and expertise designed to turn a concept into reality. We also foresee the centres as destinations for businesses struggling to integrate AI technologies, providing the critical infrastructure that most SMEs lack.  Housing this capacity alongside applied research expertise stands to make AI experimentation accessible, put commercialization within reach, and generate positive downstream impacts for the economy.

Instead of merely encouraging individuals and organizations to adopt AI, Canada would be actively supporting them on the journey.

Like many elements of Canada’s innovation strategy, national investments in AI stand to benefit from a stronger emphasis on near-to-commercialization support. In fields where Canadian researchers excel and lead, companies, organizations, non-profits, and individuals should be supported to follow and commercialize. This approach ensures investments in the research ecosystem continue to be valued and useful.