In 2015, Sobeys, Canada’s second largest food retailer, revealed plans to reduce its workforce in Milton, Ont., by 400 employees, moving operations to its state-of-the-art, almost entirely automated warehouse in Vaughan, Ont. According to media reports, Sobeys expects to cut roughly 1,300 jobs in both Ontario and Alberta. While this move is aimed at enhancing the competitiveness of the company, particularly as it competes with large international chains, it will have serious implications for a large number of workers in cities like Milton.

As more and more companies embrace automation, it is unsurprising that technology and its impacts on the labour force have been a central facet of many recent policy discussions, and the topic of substantial research. However, to date, studies examining the potential labour market impacts of modern advances in machine learning, robotics, and other technologies, have largely been limited to the national scale in Canada. While these studies are necessary to set the context, Canada’s large, diverse economy means that the effects will undoubtedly be experienced differently across the country.

To better understand the different regional impacts of automation, the Brookfield Institute for Innovation + Entrepreneurship (BII+E), through an application of McKinsey & Company data, aims to uncover which towns, cities, and regions are most susceptible to automation. It is our goal to help policymakers better understand and respond to potential regional tensions between innovation and inclusive growth in Canada.

Which industries in Canada have the highest potential for automation?

We began by first identifying the proportion of work activities with the potential to be automated across various Canadian industries using McKinsey data. McKinsey’s analysis departs from many earlier studies by taking into account that a job is comprised of a variety of work activities, some more susceptible to automation than others. Their research investigated 54 countries across the globe, and found that 49 percent of the activities that people are paid to do can be automated using current technology, however less than five percent of occupations can be fully automated.

In Canada, the proportion of work activities with the potential for automation is equivalent to 7.7 million jobs across all industries, based on 2011 data.


Some industries, such as manufacturing and mining, involve a higher concentration of tasks that are susceptible to automation. Employment in the five most susceptible industries represents 24 percent of the Canadian labour force. However, Canada also has a sizeable portion of its labour force employed in industries relatively insulated from automation. Overall, 28 percent of Canada’s total employment is in the four least susceptible industries, which include health care and social assistance, as well as professional, scientific and technical services.

How is risk distributed throughout Canada?

By mapping McKinsey statistics to the latest Canadian data (National Household Survey 2011), we were able to identify the concentration of work with the potential to be automated for all Canadian Census Metropolitan Areas (CMAs) and Census Agglomerations (CAs). The data shows that risks associated with automation will be unevenly distributed across Canada. Many CMAs and CAs specializing in manufacturing or resource extraction are most at risk. These towns and cities are found primarily in Saskatchewan and Alberta, as well as southwestern Ontario and southern Quebec.

For example, almost one-quarter of the labour force in Ingersoll, a small town in southwestern Ontario, is employed in the manufacturing industry. This town has already felt the brunt of some of the trends associated with the decline in manufacturing in the province. In January 2017, General Motors announced that it is cutting 625 jobs from its assembly plant in the area and moving them to Mexico. As recent studies from the United States have shown, both automation and globalization have taken a significant toll on manufacturing employment since the 1990s, particularly for routine assembly line jobs requiring less than a post-secondary education.

The Regional Municipality of Wood Buffalo is another example. This municipality occupies a significant land mass in northeastern Alberta and is home to both Fort McMurray and a large portion of the Athabasca Oil Sands. Wood Buffalo, where 30 percent of total employment is concentrated in oil and other extractive industries, was hit particularly hard by the recent oil shock. However, even as the price of oil rebounds, many now fear labour saving technologies will result in a leaner, more technologically-driven oil industry—preventing a return to pre-shock job numbers in these regions.

Even Canada’s largest cities are not immune to the effects of automation. In the country’s three largest CMAs—Toronto, Montreal, and Vancouver—the potential for automation is equivalent to nearly 2.7 million jobs.

On the other hand, Canada is also home to a diverse array of towns and cities with a high concentration of industries relatively insulated from the effects of automation. These include many smaller cities and towns with either a large hospital presence such as Corner Brook, Nfld., home to the largest regional hospital in the west of the province, or post-secondary institutions, such as Kingston, Ont., home to Queen’s University. It also includes, to a lesser extent, some of Canada’s larger cities, which have diverse economies that employ people across the skill spectrum. However, technology now has the capacity to automate work activities across all industries. Therefore, cities and towns across Canada may feel its impacts, regardless of their industrial composition.

However, the increasing capacity of technology combined with the fact that many of Canada’s cities and towns have a similar distribution of employment across industries, means that automation has the potential to impact a significant portion of the labour force in cities and towns across Canada.

What does this mean for the future of work?

The potential to automate a task does not necessarily mean that it will be automated. Considerations such as cost and cultural preference may argue in favour of labour. The diversity of a local economy will also determine the potential impact of automation on a given city or town. For example, a highly specialized city with a significant proportion of work with the potential for automation is likely more vulnerable to these technological trends compared to a similar city with a more diverse economy.

In cases where technology adoption does displace workers, it is possible that some jobs will be changed rather than lost, or that new jobs will be created, likely with different skill requirements. Take for example, prominent U.S. investment bank Goldman Sachs, which at its peak employed over 600 stock traders. Today, thanks to machine-learning algorithms capable of making complex trades, these 600 traders have been reduced to just two. However, technological developments also increased demand for other jobs and skills in the company. About one-third of Goldman Sach’s staff—roughly 9,000 people—are now employed as computer engineers.

In fact, technological progress has been one of the most significant drivers of productivity and long-term economic growth in Canada. As Canada’s population ages and the future of many of our resource extractive industries becomes increasingly uncertain, technologically-driven innovation will arguably become even more important to secure future economic growth.

However, the industries and for cities and towns that are more susceptible to automation is less clear. Technology is rapidly encroaching on a whole new set of job tasks, increasing the risk of automation for a large number of jobs outside the realm of what was once technically feasible, ranging from truck drivers to law clerks.

The uneven distribution of this risk should not be understated. Canadians will experience the impacts of automation differently, depending on the community they live in, the industry they work in, and their education and skill levels, as well as their income level and demographic characteristics. It will be important for policymakers to understand who is likely to be hit harder by technological change, in order to design policies and programs that will help to mitigate these negative impacts.

For more information, read Automation Across the Nation Understanding the potential impacts of technological trends across Canada, a data insights report.

In the coming months, BII+E will continue to examine the differentiated impacts of automation on disparate regions and individuals across the country. Our goal is to interrogate and analyze the industrial, socioeconomic, and demographic characteristics associated with a higher susceptibility to automation. We aim to create a profile of the regions and people that are most likely to be impacted by automation, recognizing the complex array of factors that will influence the future of work.

We will build on these initial data visualizations to inform the Canadian discourse on what technology and innovation mean for inclusive economic growth in Canada. By mapping these trends and relationships at a more micro level, we will build a more robust evidence base to inform the design of policies related to skills training, employment, and social safety nets.

For media enquiries, please contact Coralie D’Souza, Director of Communications, Events + Community Relations at the Brookfield Institute for Innovation + Entrepreneurship.