AI holds the key to reversing Canada’s productivity crisis but an AI-focused industrial strategy is required

Artificial intelligence is the only technology that can reverse Canada’s decades-long decline in productivity but it will require an AI-focused industrial strategy spearheaded by government, according to a new report from the Public Policy Forum (PPF).

While the precise trajectory of AI is impossible to foresee, there is no going back because AI’s potential benefits are “incredibly compelling,” says the report by Dr. Peter Nicholson (photo at left), PhD, PPF fellow and former deputy chief of staff in the Prime Minister’s Office and inaugural president of the Council of Canadian Academies.

“The world cannot un-learn AI, and global competition among businesses will continue to drive the technology,” Nicholson says in his report.

Also, major players such as the U.S., China and the European Union all see their economic prosperity and military security at stake and thus find themselves in the AI race “without a finish line.”

There was about US$90 billion of private investment globally in AI in 2023, of which $25 billion was allocated to generative AI, according to the report.

The report says the three key elements of Canada’s AI industrial strategy should be:

• AI regulatory development and harmonization in areas of shared jurisdiction and among provinces.
• Targeting and supporting AI high-impact areas such as the energy transition, health care systems and supply-chain logistics.
• Government serving as a model user of AI, including being a lead customer through strategic procurement for Canadian suppliers and applying AI to government operations.

The potentially unprecedented power of AI derives from its open-ended capacity to amplify and augment the human mind, the report notes. “This sets it utterly apart from any of the transformative technologies of the past.”

Nicholson cites falling labour productivity as the key reason for the decades-long decline in productivity in Canada and all highly developed countries. He argues that fundamentally, AI increases labour productivity by:

• Augmenting human capabilities, thus enabling individuals to create value more quickly.
• Substituting for humans in various tasks, thus creating value without requiring worker hours directly while freeing workers to create value in other ways.

While it’s difficult to predict where AI will have its greatest impact, several significant use cases are already being implemented or are on the horizon, according to the report. These applications include:

• Processing data and information of virtually any kind. This is a generic capability from which applications abound: summarizing enormous volumes of text; pattern recognition to enhance medical diagnosis; and text, image and sound creation in virtually any field, including the creative arts.
• Augmenting the productivity of software engineers by significantly increasing output volume without sacrificing quality. Boosting software productivity and quality has the knock-on effect of improving productivity in virtually every other application area – an AI multiplier effect.
• Boosting productivity in manufacturing and in goods production generally – such as more flexible and intelligent robots, supply chain optimization, better fault detection that leads to proactive maintenance and reduced downtime, and generation of design options that optimize manufacturability, regulatory compliance and cost.

AI also clearly has the potential to transform productivity in most aspects of the services sectors (which comprise about 80 percent of Canada’s businesses) just as earlier generations of machinery and factory automation did in agriculture and manufacturing, the report notes. “A services-focused economy will eventually no longer be a brake on robust productivity growth.”

• Improving marketing strategies, such as preparation of promotional materials (text, image, video) or micro-targeted customer identification and inducements. Indirectly, these applications already provide funding for the development of the leading-edge AI systems by Amazon, Microsoft, Alphabet and Meta.
• Enabling widespread, high-quality language translation implemented in real time with voice synthesis (including via a smart phone app), thereby enhancing productive collaboration and cross-cultural understanding in many everyday situations.
• Improved services and capabilities in the financial sector, for example risk analysis, complex document processing associated with lending agreements and regulatory compliance, implementing high-frequency trading strategies, and mass personalization of retail financial products.
• Enriching and personalizing education from the early years through adult learning and job training. The potential payoff in terms of productivity will be to build human capital far more efficiently than ever before, including re-training/upskilling workers that AI augments or replaces.
• Boosting productivity across the health care system to: automate much of the administrative burden using AI to process and provide management recommendations based on information in a variety of formats; assist diagnosis and suggest treatment options; and provide deep analytical support for health system planning to optimize the allocation of scarce resources.
• Improving the quality and efficiency of government services. Because most government activity is information-intensive, there are a great many tasks ideally suited for AI-based innovation. The potential for significant economy-wide productivity improvement is directly amplified by the sheer scale of modern government, and indirectly by the potential for more effective design and targeting of policy and regulation.
• Equipping various kinds of robots with the capability to perform flexibly in unstructured environments. Self-drive vehicles and applications of drones already demonstrate AIs with agency in the physical world.

As of late 2024, the Google spin-off company Waymo is carrying more than 100,000 riders a week in its fleet of wholly autonomous taxis – with no driver at all – along the downtown streets of San Francisco, Phoenix, Austin and Los Angeles, the report says.

Waymo’s achievement is the culmination of more than 35 million kilometres of AI model on-road training dating from 2015. 

• Amplifying innovation itself. AI has literally a super-human capacity to: absorb, process, summarize and interpret information of all kinds, including from sensors in the physical environment and from the entirety of the world’s research literature; recognize subtle patterns in unstructured data, including insights that cross conventional disciplinary boundaries; perform increasingly complex logical inference; and generate hypotheses based on all of the foregoing and engage in dialog with human researchers.

These capacities to augment human research skills have the potential to radically increase the productivity of the discovery process itself. “This will ultimately be the most transformative impact of AI since it promises to increase the rate of productivity growth of the economy as a whole.”

The economic promise of AI and addressing its risks and impacts

Goldman Sachs, in a widely cited analysis in 2023, projected AI would cause the rate of growth of U.S. productivity to increase by an annual average of almost 1.5 percentage points over a 10-year period following widespread adoption of AI applications.

That may seem a small number, Nicolson’s report says, but it represents a more than doubling of the current trend rate, with much greater increases in sectors where AI applications show the earliest potential.

A recent analysis by TD Economics estimated that ramping up AI adoption could raise Canada’s GDP by five to eight percent in 10 years relative to its baseline projection. This would translate to an AI-induced increase in the rate of productivity growth of 0.5-0.7 percentage points – “still significant by comparison with the dismal growth rate of Canada’s productivity in recent years.”

There is widespread public concern regarding a range of anticipated impacts of AI, the report says. Canadians appear to be among the most worried, ranking at or above a 21-country average on almost every impact area.

This is to be expected, the report adds, given the widely cited concerns expressed by some prominent Canadian AI experts (such as Yoshua Bengio and Geoffrey Hinton) together with a natural wariness of the unknown, particularly given experience with the downsides of social media and rampant disinformation online.

“But unless public skepticism is credibly addressed by governments and businesses, many beneficial applications of AI will be delayed or prevented. And so, too, would be the positive impact on Canada’s productivity and living standards.”

The positive case for AI needs to be complemented by compelling evidence that the understandable public concerns can be effectively managed, according to the report.

The report says there are there three issues directly associated with the economic implications of AI that have seized the attention of policymakers everywhere:

1. The impact of AI on employment.
2. Assuring healthy competition in the AI marketplace.
3. Regulating the role of AI in provision of goods and services.

“Experience from past technological revolutions suggests that each can be managed so that AI’s transformative benefits are achieved while preserving human values and purpose,” the report says.

For example, in the first decade of the 20^th century, a million Canadians – about 35 percent of Canada’s entire workforce – were employed in farming. In 1970, the number had fallen to 480,000 or 5.6 percent of total employment. By 2023, agricultural employment was down to 256,000, a mere 1.3 percent of Canadian jobs.

Yet total farm output was many times greater than 50 or 100 years earlier, the report notes. “That’s the payoff from productivity growth.”

When it comes to promoting a competitive environment for AI development, the report says recent experience from the information and communications technology sectors will be of direct relevance, such as:

• Non-discriminatory access to cloud resources and other data infrastructure will be essential. Rules in an AI context could draw on experience in ensuring fair access to telecommunications networks.
• Choke points resulting from proprietary control of the most powerful AI models can be mitigated by support of open-source platforms like Hugging Face and EleutherAI. There is a very large global community of researchers, developers and financiers prepared to volunteer skill, time and money to ensure a rich ecosystem of AI models analogous to those that developed open-source computer operating systems like Linux.
• Competitive AI development depends on affordable access to enormous computing power. Governments can contribute, as Canada’s federal government has done, earmarking  $2 billion over five years for a new AI compute access fund and a Canadian AI sovereign compute strategy.

Policy creativity will be required to establish protocols for responsible data sharing that allow smaller companies to access high-quality datasets while maintaining user privacy, and to ensure that users and businesses have control over their data and can port it between platforms, enabling them to move from one AI service to another, the report says.

How best to regulate AI and Canada’s AI advantages

When it comes to regulation, a lot of AI products should be “regulated” simply by market acceptance or rejection, according to the report.

But where more is at stake, it adds, rigorous testing protocols need to be developed and enforced. Such methods include: “red teaming” to identify vulnerabilities and weaknesses; independent audits of an AI system’s performance, safety and compliance with ethical standards; regulatory sandboxes; and controlled product testing.

Is Canada ready, willing and able to seize the AI opportunities outlined in the report? Nicholson said his answer is a qualified “Yes,” given Canada’s several significant advantages:

• Research excellence.

Canada is home to world-class AI research capability with global leaders in the field like Yoshua Bengio, Geoffrey Hinton and Richard Sutton (internationally renowned for his pioneering work on “reinforcement learning”), among many others. Canada also has three top-ranked national AI research organizations – Mila in Montreal, the Vector Institute in Toronto and Amii in Edmonton – as well as vibrant regional hubs from coast to coast.

“This outstanding intellectual capital has branded Canada as a global leader while supporting a steady flow of superbly trained talent, thus making the country a compelling destination for investment,” the report notes.

In the 10 years through 2023, Canada attracted almost US$11 billion of private investment in AI, the 5^th largest total in the world, although only seven percent as much as the U.S., by far the global leader.

• A base of globally competitive AI companies.

Canada has already established a solid position in the still-emerging AI industry with pioneering companies like Kinaxis, Coveo, Element AI (since acquired by ServiceNow), BlueDot, Mindbridge and BenchSci, among many others.

Canada’s major banks are among the global leading developers and users of AI in finance, with RBC currently ranked 3^rd and all of the Big Five banks ranked in the global Top 25.

• Supportive government.

The federal government, early on, made AI a focus of support – beginning with the 2017 pan-Canadian AI strategy, managed by CIFAR and so far funded with $567 million.

In addition, the 2024 federal budget included $2.4 billion for several AI support initiatives, headlined by $2 billion for computing power needed to train and operate Canadian-based AI models.

Provincial governments have also been developing AI industrial strategies, most notably Quebec, which has pledged a $217 million investment in the AI sector (2022-27).

Despite these impressive advantages, Canada continues to be challenged by the long-standing difficulty of converting its leading-edge knowledge into commercial innovation.

According to the report, this problem is the legacy of Canada’s industrial structure –  weighted toward resource extraction, construction, finance and U.S. branch plant investment – that reflects the country’s traditional position within a tightly integrated North American economy.

“The AI revolution creates the opportunity to turn the page. But this will depend on the willingness of businesses large and small and in virtually every sector of the economy to step up to the opportunity.”

However, polling of a representative sample of more than 13,000 businesses by Statistics Canada revealed that only 14 percent have used generative AI tools or are imminently planning to do so, while almost three-quarters are not even considering the option.

“The impact of AI on Canada’s rate of productivity growth depends almost entirely on the extent and speed of uptake of applications by businesses and public sector entities,” the report notes.

Nicholson’s report says three themes should be the key elements of an AI industrial strategy for Canada:

• Regulatory development and harmonization.

A lack of regulatory certainty is well-known to discourage investment and AI will be no exception. Given the extremely dynamic nature of the field, AI regulation will inevitably be in flux, but every effort needs to be made to formulate and adhere to basic principles and achieve consistency across jurisdictions.

“Domestically, it is essential to promote AI regulatory harmonization in areas of shared jurisdiction and among provinces in their areas of exclusive jurisdiction, such as delivery of health care, education, energy and resource development.”

Rules with respect to data and privacy are central to development and use of AI models and thus require priority attention.

• Supporting high pay-off sectors.

“An AI industrial strategy requires that choices be made.” While this is tough to do in a geographically and culturally vast federation like Canada, the country’s resources are limited and impact depends on their focus.

For example, AI can play a major role in the clean energy transition, but market forces alone in the heavily regulated electricity sector may not be sufficient to seize the opportunity in a timely way, the report says. Well-designed policy and program interventions can tip the balance.

Beyond that, there is a limited number of areas that have particular potential to boost productivity through application of AI due either to their scale and importance or their strategic position in the economy, such as health care systems and supply chain logistics. “Government support should be directed preferentially to such high-impact areas.”

• Government as a model user of AI:

There are at least three ways by which the application of AI to the government’s own operations can make a major contribution to an AI-based industrial strategy:

1. AI applied in the administrative and decision-support functions of government promises eventually to improve the efficiency and quality of service. If well-delivered, this will increase public confidence in AI applications, without which AI’s potential to improve productivity will be greatly diminished.
2. A government committed to AI can be a lead customer for Canadian suppliers through strategic procurement. The government, as an early buyer, can help suppliers ascend the learning curve and then provide the validation that promotes market expansion.
3. A government’s committed use of AI will provide a wealth of practical experience from a user’s perspective to inform development of wise policy and regulation in this novel area.

Asks Nicholson: Will AI power a new era of productivity growth and material prosperity for Canada? His answer is: “Yes, it will. Only the precise trajectory, and especially the timing, remain to be discovered.”

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