Analytical Studies Branch Research Paper Series
Experimental Estimates of Potential Artificial Intelligence Occupational Exposure in Canada

Acknowledgements

The authors would like to thank Li Xue, Marc Frenette and Vincent Hardy from Statistics Canada, and Jessica Gallant, Matthew Calver, Jacob Loree and Alan Stark from the Department of Finance Canada for their helpful and constructive comments.

Abstract

Past studies on technological change have suggested that occupations involving routine and manual tasks will face a higher risk of automation-related job transformation. However, recent advances in artificial intelligence (AI) challenge prior conclusions, as AI is increasingly able to perform non-routine and cognitive tasks. These advances have the potential to affect a broader segment of the labour force than previously thought. This study provides experimental estimates of the number and percentage of workers in Canada potentially susceptible to AI-related job transformation based on the complementarity-adjusted AI occupational exposure index of Pizzinelli et al. (2023), inspired by Felten, Raj and Seamans (2021). Results from the 2016 and 2021 censuses of population suggest that, on average, about 60% of employees in Canada could be exposed to AI-related job transformation, and about half of this group are in jobs that may be highly complementary with AI. Unlike previous waves of automation, which mainly transformed the jobs of less educated employees, AI is more likely to transform the jobs of highly educated employees. Despite facing potentially higher exposure to AI-related job transformation, highly educated employees may be in jobs that could benefit from AI technologies. Compared with employees in other industries, exposure to AI-related job transformation is higher for employees in professional, scientific and technical services; finance and insurance; information and cultural industries; educational services; and health care and social assistance. However, education and health care professionals are more likely to be in jobs that are highly complementary with AI. Employees in industries such as construction, and accommodation and food services face relatively lower exposure to AI-related job transformation. Whether occupations that may benefit from AI will experience relatively higher employment and wage growth remains to be seen, as this depends on factors such as firm productivity and the ability of workers in those occupations to leverage the potential benefits of AI.

Executive summary

Recent developments in the field of artificial intelligence (AI) have fuelled excitement, as well as concerns, regarding its implications for society and the economy. While previous waves of technological transformation raised concerns regarding the future of jobs involving routine and manual tasks, a broader segment of the labour force could be affected in an era when sophisticated large language models such as ChatGPT increasingly excel at performing non-routine and cognitive tasks typically done by highly skilled workers. AI encompasses a lot more than just natural language processing. These technologies not only have the capacity to automate routine tasks but can also augment human decision-making processes and create entirely new opportunities for innovation and efficiency. As AI continues to evolve, it has the potential to reshape industries, redefine job roles and transform the nature of work. With the transformative effects of AI already in motion, it raises renewed concerns about job transformation and the need for workforce adaptation.

This study adopts the complementarity-adjusted AI occupational exposure index of Pizzinelli et al. (2023), inspired by the original AI occupational exposure measure of Felten, Raj and Seamans (2021), and applies it to data from the 2016 and 2021 censuses of population. The experimental estimates presented in this study are largely based on the technological feasibility of automating job tasks. Employers may not immediately replace human labour with AI, even if it is technologically feasible to do so, because of financial, legal and institutional constraints. Consequently, exposure to AI does not necessarily imply a risk of job loss. At the very least, it could imply a certain degree of job transformation (Frenette and Frank, 2020). Additionally, some economists argue that the risks and benefits currently being attributed to AI may be exaggerated (Acemoglu and Johnson, 2024; McElheran et al., 2024), and productivity increases at the macroeconomic level may be modest at best (Acemoglu, 2024).

Following Pizzinelli et al. (2023), this study groups occupations into three categories based on their exposure to and complementarity with AI: (1) high exposure and low complementarity, (2) high exposure and high complementarity, and (3) low exposure. Results suggest that in May 2021, on average, around 4.2 million employees (31%) in Canada were in the first group, about 3.9 million (29%) were in the second group and about 5.4 million (40%) were in the third group. This distribution was very similar in May 2016. Unlike previous waves of automation, which mainly transformed the jobs of less educated employees performing routine and non-cognitive tasks, AI is more likely to transform the jobs of highly educated employees performing non-routine and cognitive tasks. However, highly educated employees are also more likely to hold jobs that are highly complementary with AI technologies than less educated employees. But workers will still need the skills to be able to leverage the potential benefits of AI. Compared with employees in other industries, exposure to AI-related job transformation is higher for employees in professional, scientific and technical services; finance and insurance; information and cultural industries; educational services; and health care and social assistance. However, education and health care professionals are more likely to be in jobs highly complementary with AI. Employees in industries such as construction, and accommodation and food services face relatively lower exposure to AI-related job transformation.

There is a lot of uncertainty when it comes to predicting the transformative effects of technological changes on the labour market. This study provides a static picture of AI occupational exposure based on employment compositions in May 2016 and May 2021, which were fairly similar. How workers respond and adapt to the potentially evolving labour market in the long run remains to be seen. The index used in this study is subjective and based on judgments regarding some current possibilities of AI. Consequently, the applicability of the index may decrease over time as AI capabilities grow and AI can perform an increasing number of tasks currently done by human workers. Alternative measures of AI exposure could provide further insights. Future research could also attempt to answer the question, “What happened to workers whose jobs were exposed to AI-related job transformation?”

1 Introduction

A couple of centuries ago, the Industrial Revolution and the forces of globalization coalesced to fundamentally change the global economy. These forces served as catalysts for the technological progress that has been a cornerstone of economic development. Technological advancements and innovation paved the way for machines to take over some labour-intensive tasks and allowed workers to focus on more cognitive tasks requiring creativity and critical thinking. Adoption of new technologies also led to the obsolescence of some jobs, serving as a pathway toward higher productivity. A prominent example of this is the advent of computers, which undoubtedly replaced some jobs but also created new ones in the process (see, e.g., Autor, Levy and Murnane [2003] or Graetz and Michaels [2018]). However, higher productivity may not always translate to higher wages for workers (Acemoglu and Johnson, 2024).

More generally, automation has become a defining feature of modern economies, including Canada’s. It has revolutionized various industries by streamlining processes, increasing efficiency and reducing operational costs, among other things. It has also raised concerns about the future of workers. The widely cited study by Frey and Osborne (2013), which estimated automation risks in the United States, has spurred a growing body of literature surrounding automation (see, e.g., Arntz, Gregory and Zierahn [2016]; Oschinski and Wyonch [2017]; Nedelkoska and Quintini [2018]; Frenette and Frank [2020]; and Georgieff and Milanez [2021]). Frenette and Frank (2020) estimated that approximately 1/10 of employees in Canada could be at high risk (probability of 70% or higher) of automation-related job transformation.

The prevailing thought from the automation literature is that highly educated or highly skilled individuals are less susceptible to automation-related job transformation because they are more likely to perform non-routine and cognitive tasks, which are thought to be less automatable. However, another source of disruption, which has the potential to upend prior notions, is emerging: artificial intelligence (AI).^Note While AI has been around for decades (e.g., video games, image recognition), it was not until 2022 when it became mainstream and surged in popularity, partly fuelled by the release of ChatGPT by OpenAI.

The unprecedented pace of advancements in the field of AI and its increasing integration into society and the economy have led some researchers to call this a pivotal moment in history, akin to the transformative shifts brought on by the Industrial Revolution (Cazzaniga et al., 2024). ChatGPT is just one example of a large language model (LLM) that has unlocked the remarkable possibilities of AI. AI can also perform complex tasks like generating music and videos from text input (e.g., Sora by OpenAI). AI encompasses a wide range of applications, including natural language processing, machine learning, computer vision and robotics. These technologies not only have the capacity to automate routine tasks but can also augment human decision-making processes and create entirely new opportunities for innovation and efficiency. As the field of AI continues to evolve, it has the potential to reshape industries, redefine job roles and transform the nature of work. In today’s rapidly evolving technological landscape, the integration of AI into various aspects of society, from virtual assistants and recommendation algorithms to autonomous vehicles and predictive analytics, questions naturally arise regarding its impact on society and the economy. The widespread adoption of AI raises renewed concerns about job transformation, skill mismatches and the need for workforce adaptation.

The primary objective of this study is to quantify the level of potential AI occupational exposure (AIOE) in Canada. By employing experimental methods, this study offers preliminary insights into how AI may affect the Canadian labour market and the potential risks and benefits it holds for workers.

This study adopts the complementarity-adjusted AIOE (C-AIOE) index proposed by Pizzinelli et al. (2023). The original AIOE index, which is often cited in the literature, was proposed by Felten, Raj and Seamans (2021) as a way of measuring how AI applications overlap with the human abilities needed to perform a given job. In light of recent advancements in LLMs, Felten, Raj and Seamans (2023) considered an alternate index that weighted language modelling more heavily and found that it was highly correlated with the original AIOE index. Recognizing that AI can complement human labour, the International Monetary Fund (IMF) study by Pizzinelli et al. (2023) proposed the C-AIOE index, which attempts to account for the potential complementarity of AI across occupations, in addition to direct exposure. These measures focus on “narrow” AI, which refers to “computer software that relies on highly sophisticated algorithmic techniques to find patterns in data and make predictions about the future” (Broussard, 2018; Felten, Raj and Seamans, 2021). This definition encompasses generative AI (e.g., LLMs, image recognition) but does not capture exposure to “general” AI, which refers to “computer software that can think and act autonomously and is combined with automation and robot technologies” (Pizzinelli et al., 2023). International comparisons of AIOE based on the original AIOE index have been done (see, e.g., Georgieff and Hyee [2021] and OECD [2023]). An IMF study by Cazzaniga et al. (2024) compared AI exposure and potential complementarity across countries using the C-AIOE index but did not analyze Canadian data in detail. They found that around 60% of jobs in advanced economies may be highly exposed to AI-related job transformation. As will be shown, this is similar to the share estimated for Canada.

This study offers Canadian evidence on AIOE and asks the following research questions:

1. Which occupations are potentially exposed to AI-related job transformation?
2. Which occupations may benefit from AI-related job transformation?
3. How does the distribution of AIOE vary by industry, education level, employment income and other worker characteristics?

The experimental AI exposure estimates in this study are largely based on the technological feasibility of automating job tasks. Employers may not immediately replace humans with AI, even if it is technologically feasible, for several reasons (see, e.g., Bryan, Sood and Johnston [2024]), including financial, legal and institutional factors. Consequently, exposure to AI does not necessarily imply a risk of job loss. At the very least, it could imply some degree of job transformation (Frenette and Frank, 2020). AI could lead to the creation of new tasks within existing jobs or create entirely new jobs. Additionally, some economists argue that the risks and benefits of AI may be exaggerated (Acemoglu and Johnson, 2024; McElheran et al., 2024), and productivity increases at the macroeconomic level may be modest at best (Acemoglu, 2024). Evidence from the United States suggests that the adoption of AI has been more prevalent in larger firms (McElheran et al., 2024), as some employers may not yet find it economically optimal to adopt such technologies (Svanberg et al., 2024). Whether this will contribute to a productivity gap between smaller and larger firms is unclear. Predicting the effects of technological changes on the labour market is not an exact science, as some subjectivity is usually involved. For example, more than a decade after Frey and Osborne (2013), it is still difficult to precisely measure the effect of automation on labour markets, as changes are ongoing (Georgieff and Milanez, 2021). Although the diffusion of new technology can take time (Feigenbaum and Gross, 2023), measuring the impact of AI could be challenging given the rapid pace of advancements. The experimental estimates presented in this study should be interpreted with caution. Only time will tell whether predicted changes brought on by new technologies will come to fruition.

The remainder of this article is organized as follows. Section 2 briefly describes the AIOE index of Felten, Raj and Seamans (2021) and the complementarity-adjusted variant of Pizzinelli et al. (2023). Section 3 presents the results, and Section 4 provides concluding remarks and suggestions for future research.

2 Methods

The objective of this study is to estimate the extent to which occupations in Canada are potentially exposed to AI-related job transformation and the extent to which AI can potentially complement human labour in those occupations. This study uses the novel C-AIOE index of Pizzinelli et al. (2023) to achieve this objective. This measure is computed at the occupational level based on data from the Occupational Information Network (O*NET), which was created in the late 1990s by the United States Department of Labor to quantify and track the skills and abilities used across more than 1,000 different occupations (https://www.onetonline.org). Thus, the measure used in this study relies on occupational attribute data from the United States, which has a similar skill profile as Canada.

The C-AIOE index is based on the original AIOE index of Felten, Raj and Seamans (2021), which measures the relationship between 52 human abilities and 10 AI applications, weighted by the degree of complexity and importance of those skills for a given occupation $i$,

$${\text{AIOE}}_i=\frac{{{\displaystyle \sum }}_{j=1}^{52}{A}_j{L}_{ji}{I}_{ji}}{{{\displaystyle \sum }}_{j=1}^{52}{L}_{ji}{I}_{ji}},$$

where $j$ indexes 52 occupational abilities; $L_{ji}$ is the prevalence score from O*NET and $I_{ji}$ is the importance score from O*NET for ability $j$ in occupation $i$; and $A_j={\displaystyle \sum }_{k=1}^{10}{x}_{kj}$ is the exposure to AI of ability $j$ computed as the sum of the relatedness scores, $x_{kj}$, of ability $j$ with 10 AI applications.^Note This index is a relative measure of AI exposure (e.g., $AIO{E}_m>AIO{E}_n$ implies that occupation $m$ faces greater exposure to AI-related job transformation than occupation $n$). See Felten, Raj and Seamans (2021) for details.

Because the AIOE index is agnostic regarding the implications of occupations being exposed to AI, Pizzinelli et al. (2023) proposed a variant of the AIOE index that accounts for the potential complementarity of AI. They make the case that certain occupations may be less conducive to the unsupervised use of AI than others. For example, judges and medical professionals are examples of occupations where job aspects such as the criticality of decisions and the gravity of the consequences of errors may require human workers to make the final decision (Cazzaniga et al., 2024). The C-AIOE of Pizzinelli et al. (2023) is computed as

$${\text{C-AIOE}}_i={\text{AIOE}}_i\times \left(1-w\times \left({\theta }_i-{\theta }_{MIN}\right)\right),$$

where $0\le w\le 1$ is a weight chosen by the researcher that controls the influence of the complementary parameter ($\theta$), ${\theta }_i$ is the complementarity index of occupation and $i$ and ${\theta }_{MIN}$ is the minimum observed $\theta$ value among all occupations. A weight of $w=0$ reverts the C-AIOE back to the original AIOE (e.g., no role for AI complementarity), while $w=1$ allows maximum potential AI complementarity for occupation $i$.^Note Like the AIOE index, the complementarity index is also a relative measure, with a higher value indicating higher potential complementarity. The complementarity index for occupation $i$, ${\theta }_i$, is computed using O*NET data on “work contexts” and “job zones” for that particular occupation. To do so, 11 work contexts (each score ranging from 0 to 100) and the job zone (ranging from 1 to 5) are combined into six components as follows:

1. Communication
   1. Face to face
   2. Public speaking

Although AI can play a role in enhancing certain aspects of communication, the nuanced complexities of face-to-face interactions and public speaking could remain predominantly within the realm of human expertise.

2. Responsibility
   1. For outcomes
   2. For others’ health

AI has the potential to transform many sectors in the economy, including health care, where tough decisions are routinely made, and such decisions may still require human oversight and judgment.

3. Physical conditions
   1. Exposure to outdoor environments
   2. Physical proximity to others

Jobs requiring substantial outdoor exposure and proximity to others require a certain level of adaptability and teamwork (e.g., firefighters, construction workers). Integrating AI into highly advanced machinery in diverse work environments could be costly.

4. Criticality
   1. Consequence of errors
   2. Freedom of decisions
   3. Frequency of decisions

The importance of human oversight may become increasingly evident as AI continues to automate decision-making processes. In professions such as air traffic control or nursing, where human judgment is paramount, the combination of data analysis and instinct is essential for responding to unexpected scenarios. While AI can offer valuable data and recommendations, thereby potentially reducing human error and accelerating decision making, the indispensability of human oversight remains clear.

5. Routine
   1. Degree of automation (100 minus the O*NET score so that occupations with a low degree of automation receive higher values)
   2. Unstructured versus structured work

Occupations involving routine tasks have historically been more susceptible to technological transformation. Despite differences between AI and previous waves of automation, routine-intensive occupations remain particularly vulnerable to transformation. In contrast, less structured jobs may necessitate more advanced technologies for AI to operate autonomously.

6. Skills (job zone):

   Job zone is an indicator of the extent of preparation required for a job. This value must be rescaled to align with the five other components by multiplying it by 20, so that it ranges from 20 to 100 instead of 1 to 5. A higher value indicates more extensive preparation.

Occupations with high educational or training requirements may be more conducive to integrating the skills complementary with AI, as providing instructions to AI and leveraging it require some level of expertise and proficiency.

A score for each of the six components is computed by averaging the work contexts within each component (e.g., the score for communication is the average of face-to-face and public speaking work contexts). For the skills component, the score is the rescaled job zone value. Then, θ is calculated as the average of the six component scores divided by 100. See Pizzinelli et al. (2023) for more details regarding the derivation of the C-AIOE index and the sensitivity analyses.

This index does have some limitations, as pointed out by Pizzinelli et al. (2023). The selection of O*NET variables that serve as inputs of the index is subjective and relies on judgment regarding the factors that matter for the interaction between AI and human workers. However, Pizzinelli et al. (2023) show that the work contexts are not all systematically related to each other and offer a multifaceted take on the potential complementarity of AI with human workers. The index considers how human abilities may overlap with 10 AI applications, but as AI capabilities improve, the likelihood of AI supplanting tasks typically performed by human workers may grow. Consequently, the applicability of the index could decrease over time.^Note Moreover, while the index captures the potential exposure of occupational abilities and tasks to AI, it does not account for advances in robotics, sensors and other technologies that could potentially integrate with AI (Felten, Raj and Seamans, 2021).

As O*NET is an American database, the occupations are coded according to the Standard Occupational Classification (SOC) system. The complementarity parameter and the AIOE index were computed based on version 28.2 of the O*NET database, which uses the 2018 SOC. The AIOE index was computed at the six-digit level, while the complementarity parameter was computed at the eight-digit level and then aggregated to the six-digit level by averaging the parameter values (e.g., the values associated with SOC codes 12-3456.01 and 12-3456.02 would be averaged to obtain the value for SOC code 12-3456). The six-digit SOC codes were then converted to the four-digit codes of version 1.3 of the Canadian National Occupational Classification (NOC) 2016 so the rich set of dimensions from the 2016 and 2021 censuses of population (reference week in May) could be used to examine AIOE in Canada.^Note The sample was restricted to employees aged 18 to 64 living off reserve in private dwellings, excluding full-time members of the Canadian Armed Forces. Employment in some industries, such as accommodation and food services, decreased from May 2016 to May 2021 because of the COVID-19 pandemic, so the 2016 Census of Population was also used as a robustness check. However, results suggest that the share of employees exposed to AI-related job transformation changed very little in general.

3 Results

Figure 1 presents the AIOE and potential complementarity (θ) for Canadian occupations. The median AIOE was around 6.0, while the median complementarity was about 0.6. Following Pizzinelli et al. (2023), an occupation is considered “high exposure” if its AIOE exceeds the median AIOE and “low exposure” otherwise. Likewise, an occupation is considered “high complementarity” if its potential complementarity exceeds the median complementarity and “low complementarity” otherwise.^Note Based on this, occupations are grouped into four quadrants in Figure 1: high exposure and low complementarity, high exposure and high complementarity, low exposure and low complementarity, and low exposure and high complementarity. For simplicity, the latter two categories are combined into a single category, “low exposure,” in subsequent analyses. High-exposure, low-complementarity occupations are those that may be highly exposed to AI-related job transformation and whose tasks could be replaceable by AI in the future. High-exposure, high-complementarity occupations are those that may be highly exposed to AI-related job transformation but could be highly complementary with AI. However, workers will still need the necessary skills to leverage the complementary benefits of AI. Low-exposure jobs are those that may be less exposed to AI-related job transformation than others.^Note

Description for figure

Potential artificial intelligence occupational exposure (AIOE) and complementarity in Canada

This chart shows a scatter plot with the AI occupational exposure index ranging from 5 to 7 on the horizontal axis and the complementarity index ranging from 0.4 to 0.8 on the vertical axis. There are 490 data points. Each data point represents an occupation as per the 4-digit National Occupation Classification version 2016 and are colour-coded with three different colours. The colours are used to distinguish the occupations according to their minimum educational requirement. Occupations requiring a bachelor's degree or higher are represented by blue, occupations requiring some postsecondary education below bachelor's degree are represented by green, and occupations requiring high school or less education are represented by red. The chart shows the relationship between AI occupational exposure and the extent to which AI can play a complementary role in a given occupation. A higher AI occupational exposure index is associated with greater potential occupational exposure to AI. A higher complementarity index is associated with greater potential complementarity with AI. The median AI occupational exposure index score of 6 and the median complementarity index score of 0.6 are used to group the various occupations into four quadrants. The top-left quadrant contain data points representing occupations which might be relatively less exposed to AI and highly complementary with AI. The majority of occupations in that quadrant require some postsecondary education below bachelor's degree but there are also a few which require high school or less education. Some examples include firefighters, plumbers, and carpenters. The bottom-left quadrant contain data points representing occupations which might also be relatively less exposed to AI but also less complementary with AI. The majority of occupations in that quadrant require high school or less education but there are also a few which require some postsecondary education below bachelor's degree. Some examples include food and beverage servers, labourers in processing, manufacturing and utilities, and welders and related machine operators. The top-right quadrant contain data points representing occupations which might be highly exposed to AI and highly complementary with AI. The majority of occupations in that quadrant require a bachelor's degree or higher education but there are a few which require some postsecondary education below bachelor's degree. Some examples include general practitioners and family physicians, secondary school teachers, and electrical engineers. The bottom-right quadrant contain data points representing occupations which might be highly exposed to AI but less complementary with AI. This quadrant has fewer data points than the other quadrants and the occupations represented by the data points have a mixture of educational requirements. Some examples include data entry clerks, economists, computer network technicians, and computer programmers and interactive media developers.

Notes: The AIOE index and potential complementarity are based on Felten, Raj and Seamans (2021) and Pizzinelli et al. (2023). An occupation is considered high-exposure if its AIOE index exceeds the median AIOE across all occupations (6.0) and considered low-exposure otherwise. Similarly, an occupation is considered high-complementarity if its complementarity parameter exceeds the median complementarity across all occupations (0.6) and considered low-complementarity otherwise. Occupations in this chart are based on the 4-digit National Occupational Classification (NOC) 2016 version 1.3 converted from the United States Standard Occupational Classification (SOC) 2018. Of the 500 NOC occupations, 10 occupations which represented less than 1% of Canadian employment, were excluded due to a lack of Occupational Information Network (O*NET) data for computing the AIOE or complementarity parameter.

Source: Occupational Information Network (O*NET) version 28.2.

Figure 1 shows that jobs potentially highly exposed to AI-related job transformation are generally those that require higher education. Although these jobs could face relatively more exposure to AI-related transformation, occupations such as family physicians, teachers and electrical engineers may be complementary with AI technologies given their relatively high complementarity scores. In contrast, occupations such as computer programming, which may also require relatively high education, have low complementarity scores, suggesting less potential complementarity with AI. There is considerable uncertainty, however, regarding the extent to which AI can actually replace human labour.

Low-exposure occupations appear to be those that usually do not require a high level of education. Some examples of occupations facing relatively low exposure to AI-related job transformation are carpenters; welders; plumbers; food and beverage servers; labourers in processing, manufacturing and utilities; and firefighters. However, as illustrated by Figure 1, AI has the potential to transform a broad set of occupations regardless of skill level. The diffusion of AI could also have downstream general equilibrium effects. For example, although less educated employees may be in jobs potentially less exposed to AI-related job transformation, highly educated employees from high-exposure jobs could transition to low-exposure jobs, displacing less educated employees (see, e.g., Beaudry, Green and Sand [2016]).

Chart 1 aggregates the various NOC occupations into 28 distinct jobs to simplify the analysis and precisely identify the number and distribution of employees falling into the three AI exposure groups: (1) high exposure and low complementarity, (2) high exposure and high complementarity, and (3) low exposure. In May 2021, on average, roughly 4.2 million employees (31%) in Canada were in the first group, about 3.9 million (29%) were in the second group and about 5.4 million (40%) were in the third group.

Chart 1 start

Data table for Chart 1

Data table for Chart 1 Table summary
This table displays the results of . The information is grouped by Occupations (appearing as row headers), Low exposure, High exposure, low complementarity and High exposure, high complementarity, calculated using percentage of employees units of measure (appearing as column headers).

Occupations	High exposure, low complementarity	High exposure, high complementarity	Low exposure
	percentage of employees
Notes: The sample consists of employees aged 18 to 64 living off reserve in private dwellings, excluding full-time members of the Canadian Armed Forces. The numbers in parentheses indicate the codes from version 1.3 of the National Occupational Classification 2016. The occupations are ranked according to the number of employees from most (top) to least (bottom). The artificial intelligence occupational exposure index and potential complementarity are based on Felten, Raj and Seamans (2021) and Pizzinelli et al. (2023). Sources: Statistics Canada, Census of Population, 2021; and Occupational Information Network version 28.2.
Management occupations (0)	6	87	7
Support occupations in sales and service (66, 67)	1	0	99
Administrative occupations in finance, insurance and business (12, 13)	82	18	0
Office support and co-ordination occupations (14, 15)	76	0	24
Transport and heavy equipment operators and servicers (74, 75)	0	0	100
Professional occupations in education services (40)	12	88	0
Sales and service supervisors (62, 63)	19	27	54
Support occupations in law and social services (42, 43, 44)	32	34	34
Industrial, electrical and construction trades (72)	0	0	100
Service representatives and other customer and personal services occupations (65)	77	2	21
Professional occupations in business and finance (11)	100	0	0
Sales representatives and salespersons in wholesale and retail trade (64)	89	11	0
Technical occupations related to natural and applied sciences (22)	34	40	26
Computer and information systems professionals (217)	100	0	0
Maintenance and equipment operation trades (73)	0	7	93
Professional occupations in law and social, community and government services (41)	24	76	0
Assisting occupations in support of health services (34)	0	0	100
Assemblers and labourers in manufacturing and utilities (95, 96)	0	0	100
Professional occupations in nursing (30)	0	100	0
Technical occupations in health (32)	13	18	69
Machine operators and supervisors in manufacturing and utilities (92, 94)	0	10	90
Occupations in art, culture, recreation and sports (51, 52)	46	33	21
Natural resources, agriculture and related production occupations (8)	0	0	100
Engineers (213, 214)	13	87	0
Trades helpers, construction labourers and related occupations (76)	0	0	100
Professional occupations in health (except nursing) (31)	0	86	14
Physical and life science professionals (211, 212)	1	99	0
Architects and statisticians (215, 216)	25	75	0

Chart 1 end

At least three-quarters of employees in the following occupations were in the first group (i.e., highly exposed to AI-related job transformation and whose tasks could be replaceable with AI in the future): administrative occupations in finance, insurance and business; office support and co-ordination occupations; sales representatives and salespersons in wholesale and retail trade; service representatives and other customer and personal services occupations; professional occupations in business and finance; and computer and information systems professionals. Interestingly, among the 28 occupations, computer and information systems professionals experienced the highest growth (39%) from May 2016 to May 2021. However, this does not necessarily mean that computer and information systems professionals will be in less demand in the future because of AI. While these professionals may be in high-exposure, low-complementarity jobs, they are integral to maintaining and improving the underlying AI infrastructure, and this may lead to the creation of new tasks or jobs. Around 85% of employees or more in management occupations, professional occupations in education services and professional occupations in health (except nursing), as well as engineers, were in the second group (i.e., potentially highly exposed to AI-related job transformation, but AI can complement human labour as long as the worker possesses the necessary skills). Some occupations that could be less susceptible to AI-related job transformation (third group) were support occupations in sales and service; trades helpers, construction labourers and related occupations; assisting occupations in support of health services; and natural resources, agriculture and related production occupations.

Chart 2 shows the AI exposure distribution by industry based on the North American Industry Classification System 2017, at the two-digit level. More than half of employees in the following industries were in high-exposure, low-complementarity jobs: professional, scientific and technical services; finance and insurance; and information and cultural industries. In contrast, educational services, and health care and social assistance employed proportionately more employees who may be beneficiaries of AI. Within the health care and social assistance industry, it is mostly the professional occupations (e.g., nurses, physicians) that may be complementary with AI technologies (Figure 1). Employees in industries such as accommodation and food services, manufacturing, construction, and transportation and warehousing may face relatively lower exposure to AI-related job transformation.

Chart 2 start

Data table for Chart 2

Data table for Chart 2 Table summary
This table displays the results of . The information is grouped by Industries (appearing as row headers), Low exposure, High exposure, low complementarity and High exposure, high complementarity, calculated using percentage of employees units of measure (appearing as column headers).

Industries	High exposure, low complementarity	High exposure, high complementarity	Low exposure
	percentage of employees
Notes: The sample consists of employees aged 18 to 64 living off reserve in private dwellings, excluding full-time members of the Canadian Armed Forces. The industry classifications are based on the North American Industry Classification System 2017. The industries are ranked according to the number of employees from most (top) to least (bottom). The artificial intelligence occupational exposure index and potential complementarity are computed using Occupational Information Network data and are based on Felten, Raj and Seamans (2021) and Pizzinelli et al. (2023). Sources: Statistics Canada, Census of Population, 2021; and Occupational Information Network version 28.2.
Health care and social assistance	23	38	39
Retail trade	37	23	40
Manufacturing	16	20	64
Educational services	23	69	8
Professional, scientific and technical services	57	35	8
Public administration	45	31	24
Construction	13	14	73
Transportation and warehousing	19	15	66
Accommodation and food services	7	4	89
Finance and insurance	68	30	2
Administrative and support, waste management and remediation services	39	14	47
Wholesale trade	33	33	34
Other services (except public administration)	26	21	53
Information and cultural industries	56	32	12
Mining, quarrying, and oil and gas extraction	16	25	59
Agriculture, forestry, fishing and hunting	12	10	78
Real estate and rental and leasing	36	42	22
Arts, entertainment and recreation	25	29	46
Utilities	26	34	40
Management of companies and enterprises	59	36	5

Chart 2 end

Employees in larger enterprises (in the commercial sector) may face relatively higher exposure to AI-related job transformation (Chart 3), compared with their counterparts in smaller enterprises. Roughly over one-third of workers in enterprises with 500 or more employees were in high-exposure, low-complementarity jobs in May 2016. This compares with 25% to 28% of workers in smaller enterprises. However, employees in larger enterprises were somewhat more likely to be in jobs complementary with AI than their counterparts in smaller enterprises.

Chart 3 start

Data table for Chart 3

Data table for Chart 3 Table summary
This table displays the results of . The information is grouped by Enterprise size (appearing as row headers), Low exposure, High exposure, low complementarity and High exposure, high complementarity, calculated using percentage of employees units of measure (appearing as column headers).

Enterprise size	High exposure, high complementarity	High exposure, low complementarity	Low exposure
	percentage of employees
Notes: The sample consists of employees aged 18 to 64 living off reserve in private dwellings, excluding full-time members of the Canadian Armed Forces. The artificial intelligence occupational exposure index and potential complementarity are computed using Occupational Information Network data and are based on Felten, Raj and Seamans (2021) and Pizzinelli et al. (2023). The number of employees within an enterprise was computed by integrating Census of Population data with the Longitudinal Worker File. The commercial sector excludes employees from public administration, educational services, and health care and social assistance. Other industries which were excluded: monetary authorities - central bank; religious, grant-making, civic, and professional and similar organizations; and private households. Sources: Statistics Canada, Census of Population, 2016, and Longitudinal Worker File, 2015 and 2016; and Occupational Information Network version 28.2.
500 or more employees	23	36	41
100 to 499 employees	21	28	51
20 to 99 employees	19	25	56
Fewer than 20 employees	18	28	54

Chart 3 end

Educational attainment has historically been one of the most important indicators of whether a worker will be resilient to technological shocks. The growing consensus from the labour economics literature is that less educated workers face a higher risk of automation-related job transformation than highly educated workers because the former group is more likely to perform routine and manual tasks that are more susceptible to being automated. However, Chart 4 shows that AI could affect a broader segment of the labour force than previously thought because it has the capacity to perform non-routine and cognitive tasks. Highly educated employees may face higher exposure to AI-related job transformation, as was shown in Figure 1. The highest shares of high-exposure, low-complementarity jobs are held by employees with a bachelor’s degree (37%) or a college, CEGEP or other certificate or diploma below a bachelor’s degree (36%), followed by those with a graduate degree (32%), high school or less education (25%), and an apprenticeship or trades certificate or diploma (15%). However, employees with a bachelor’s degree or higher were more likely to hold jobs that may be highly complementary with AI than those with an education below the bachelor’s degree level, as long as the potential beneficiaries of AI possess the necessary skills. Employees with an apprenticeship or trades certificate or diploma may be less exposed to AI-related job transformation, as 73% were in low-exposure occupations. However, as previously mentioned, a more nuanced view is that while less educated workers may face potentially lower exposure to AI-related job transformation, highly educated workers from high-exposure jobs may transition to low-exposure jobs, displacing less educated workers (see, e.g., Beaudry, Green and Sand [2016]).

Chart 4 start

Data table for Chart 4

Data table for Chart 4 Table summary
This table displays the results of . The information is grouped by Highest level of education (appearing as row headers), Low exposure, High exposure, low complementarity and High exposure, high complementarity, calculated using percentage of employees units of measure (appearing as column headers).

Highest level of education	High exposure, low complementarity	High exposure, high complementarity	Low exposure
	percentage of employees
Notes: The sample consists of employees aged 18 to 64 living off reserve in private dwellings, excluding full-time members of the Canadian Armed Forces. The artificial intelligence occupational exposure index and potential complementarity are computed using Occupational Information Network data and are based on Felten, Raj and Seamans (2021) and Pizzinelli et al. (2023). Sources: Statistics Canada, Census of Population, 2021; and Occupational Information Network version 28.2.
High school or less	25	13	62
Apprenticeship or trades certificate or diploma	15	12	73
College, CEGEP or other certificate or diploma below bachelor's degree	36	26	38
Bachelor's degree	37	46	17
Graduate degree	32	58	10

Chart 4 end

Many of the results presented so far are contrary to the findings on automation documented in the labour economics literature over the past two decades, raising concerns about the nexus of automation and AI. Frenette and Frank (2020) estimated that around 1/10 of employees in Canada were at high risk (probability of 70% or more) of automation-related job transformation in 2016. Chart 5 suggests that exposure to AI-related job transformation decreases as the risk of automation-related job transformation increases. The majority of employees (60%) in jobs at high risk of automation-related transformation were in jobs that may be least exposed to AI-related transformation (Chart 5). In contrast, 18% of employees in jobs at low risk (probability of less than 50%) of automation were in low-exposure jobs. However, although potentially highly exposed to AI-related job transformation, employees at a lower risk of automation-related job transformation hold jobs that could be highly complementary with AI. Jobs facing a moderate risk (probability of 50% to less than 70%) of automation-related transformation were most likely to be high-exposure, low-complementarity jobs. These findings are important, as they suggest that the distinction between manual and cognitive tasks and between repetitive and non-repetitive tasks used in the last two decades in labour economics to understand automation-related technological transformation may not apply to AI.

Chart 5 start

Data table for Chart 5

Data table for Chart 5 Table summary
This table displays the results of . The information is grouped by Risk of automation (appearing as row headers), Low exposure, High exposure, low complementarity and High exposure, high complementarity, calculated using percentage of employees units of measure (appearing as column headers).

Risk of automation	High exposure, high complementarity	High exposure, low complementarity	Low exposure
	percentage of employees
Notes: The sample consists of employees aged 18 to 64 from the database used by Frenette and Frank (2020). Occupations at low risk of automation are those with a probability of automation lower than 50%. Occupations with a moderate risk of automation are those with a probability of automation of 50% to less than 70%. Occupations at high risk of automation are those with a probability of automation of 70% or more. The artificial intelligence occupational exposure index and potential complementarity are computed using Occupational Information Network data and are based on Felten, Raj and Seamans (2021) and Pizzinelli et al. (2023). Sources: Statistics Canada, Longitudinal and International Survey of Adults, 2016 (wave 3); and Occupational Information Network version 28.2.
High risk of automation	6	34	60
Moderate risk of automation	19	41	40
Low risk of automation	46	36	18

Chart 5 end

Like previous waves of technological transformation, AI has the potential to boost productivity. But this process can also exacerbate earnings inequality. Chart 6 shows the AI exposure distribution across employment income deciles. More than half of the jobs in the bottom half of the distribution were low-exposure jobs, while around 30% were high-exposure, low-complementarity jobs. The middle of the distribution may be the most vulnerable to AI-related job transformation, with around one-third of jobs being high exposure and low complementarity. Exposure to AI-related job transformation increases with employment income, but higher earners hold jobs that may be highly complementary with AI. Although the top decile had the highest share of jobs potentially exposed to AI-related job transformation, they also had the highest share of jobs (55%) that are highly complementary with AI. If higher earners can take advantage of the complementary benefits of AI, their productivity and earnings growth may outpace those of lower earners, and this could exacerbate earnings inequality (Cazzaniga et al., 2024). However, the diffusion of AI could also potentially reduce earnings inequality if AI happens to adversely affect high-skill occupations (see, e.g., Webb [2020]).

Chart 6 start

Data table for Chart 6

Data table for Chart 6 Table summary
This table displays the results of . The information is grouped by Employment income decile (appearing as row headers), Low exposure, High exposure, low complementarity and High exposure, high complementarity, calculated using percentage of employees units of measure (appearing as column headers).

Employment income decile	High exposure, low complementarity	High exposure, high complementarity	Low exposure
	percentage of employees
Notes: The sample consists of employees aged 18 to 64 living off reserve in private dwellings, excluding full-time members of the Canadian Armed Forces. The artificial intelligence occupational exposure index and potential complementarity are computed using Occupational Information Network data and are based on Felten, Raj and Seamans (2021) and Pizzinelli et al. (2023). Sources: Statistics Canada, Census of Population, 2021; and Occupational Information Network version 28.2.
Decile 1	32	16	52
Decile 2	31	15	54
Decile 3	29	17	54
Decile 4	31	19	50
Decile 5	35	21	44
Decile 6	35	24	41
Decile 7	33	31	36
Decile 8	29	41	30
Decile 9	26	50	24
Decile 10	26	55	19

Chart 6 end

Canada’s record population growth, recently driven by international migration, raises questions about the future of jobs done by immigrants and non-permanent residents. In May 2016, recent immigrants (those who landed from 2011 to 2016) (29%) were just as likely as Canadian-born individuals (29%) to be in high-exposure, low-complementarity jobs (Chart 7). However, by May 2021, while the share of Canadian-born individuals in such jobs remained the same, the share of recent immigrants (those who landed from 2016 to 2021) in these jobs increased to 37%. This was partly driven by the fact that nearly 1/10 of permanent residents who landed from 2016 to 2021 were employed in computer and information systems professions in May 2021—occupations more likely to be high exposure and low complementarity. Less than 5% of permanent residents who landed from 2011 to 2016 were employed in these professions in May 2016. This increasing concentration of recent immigrants in computer and information systems professions has been documented by Picot and Mehdi (forthcoming). Another reason could be the (temporarily) falling share of employment in occupations adversely affected by the COVID-19 pandemic. Non-permanent residents were more likely to be in high-exposure, low-complementarity jobs and low-exposure jobs than Canadian-born individuals. One goal of economic immigration programs is to fill labour and skills shortages. However, perceived labour shortages may eventually incentivize some employers to adopt AI technologies, especially if such shortages are in occupations highly exposed to AI-related job transformation.

Chart 7 start

Data table for Chart 7

Data table for Chart 7 Table summary
This table displays the results of . The information is grouped by Immigrant status (appearing as row headers), Low exposure, High exposure, low complementarity and High exposure, high complementarity, calculated using percentage of employees units of measure (appearing as column headers).

Immigrant status	High exposure, low complementarity	High exposure, high complementarity	Low exposure
	percentage of employees
Notes: The sample consists of employees aged 18 to 64 living off reserve in private dwellings, excluding full-time members of the Canadian Armed Forces. The artificial intelligence occupational exposure index and potential complementarity are based on Felten, Raj and Seamans (2021) and Pizzinelli et al. (2023). Recent immigrants employed in May 2016 are permanent residents who landed in Canada from January 2011 to May 2016. Recent immigrants employed in May 2021 are permanent residents who landed in Canada from January 2016 to May 2021. Sources: Statistics Canada, Census of Population, 2016 and 2021; and Occupational Information Network version 28.2.
Canadian-born individuals
May 2016	29	28	43
May 2021	29	30	41
Recent immigrants
May 2016	29	19	52
May 2021	37	23	40
Non-permanent residents
May 2016	33	21	46
May 2021	35	17	48

Chart 7 end

Appendix Table A.1 (May 2016) and Appendix Table A.2 (May 2021) provide further results disaggregated by field of study, age group, gender, activity limitation status, selected census metropolitan area (CMA), racialized group, full-time or part-time status, union membership status, and whether the job can be done from home.

Exposure to AI-related job transformation varies substantially not only across fields of study but also on whether the employee has a bachelor’s degree or higher education. For example, employees who studied engineering and engineering technology or health care at a level below a bachelor’s degree were less likely to face AI-related job transformation than employees who studied the same disciplines at the bachelors’ degree or higher level. However, even with increased exposure, the majority of the latter group held jobs that were highly complementary with AI. Close to 60% of employees or more who studied mathematics and computer and information sciences—regardless of where they received their postsecondary education—were in high-exposure, low-complementarity jobs. Employees who studied construction trades and mechanic and repair trades may face relatively lower exposure to AI-related job transformation.

Employees aged 18 to 24 are overrepresented in low-exposure occupations, likely because they do not yet have the necessary experience to be employed in high-skill occupations. Core working-age employees, those aged 25 to 54 years, are generally more likely to hold jobs highly exposed to AI-related job transformation than their younger and older counterparts. But core working-age employees are also more likely to hold jobs that may be highly complementary with AI.

Slightly over one-fifth of men are employed in high-exposure, low-complementarity jobs, compared with 38% of women. This is because men are more likely to be employed in the skilled trades, which may face relatively lower exposure to AI-related job transformation. However, women (33%) are more likely than men (25%) to be employed in occupations that could be highly complementary with AI.

Occupations facing AI-related job transformation are more likely to be in large population centres. The CMAs of Ottawa–Gatineau (39%) and Toronto (37%) had proportionately more high-exposure, low-complementarity employment relative to other CMAs. But urban areas also had proportionately more jobs that could be highly complementary with AI.

Chinese (45%) and South Asian (38%) employees are more likely to hold high-exposure, low-complementarity jobs than other racialized groups. This is partly driven by their relatively higher representation in computer and information systems professions, which potentially highly exposed to AI-related job transformation and whose tasks may be replaceable by AI in the future. However, as noted earlier, these occupations could be integral to maintaining and improving the underlying AI infrastructure.

Unionized employees are almost as likely as their non-unionized counterparts to be highly exposed to AI-related job transformation. However, non-unionized employees (35%) are more likely to be in high-exposure, low-complementarity jobs than unionized employees (23%). This was largely driven by a higher share of unionized employees in health care and education occupations, which are potentially highly exposed to and complementary with AI.

The COVID-19 pandemic has led to significant increases in working from home (see, e.g., Mehdi and Morissette [2021a] or Mehdi and Morissette [2021b]). These jobs are usually held by highly educated employees who may be more exposed to AI-related job transformation than their less educated counterparts. Just over half (51%) of employees with jobs that can be done from home were in high-exposure, low-complementarity occupations, compared with 14% of employees in jobs that cannot be done from home.^Note However, 47% of the former group holds jobs that could be highly complementary with AI, compared with 14% of the latter group. How the advent of AI could affect the labour market in potential future pandemics is unclear (see, e.g., Frenette and Morissette [2021]).

4 Conclusion

This study provides experimental estimates of the number and percentage of employees aged 18 to 64 in Canada potentially susceptible to AI-related job transformation using the C-AIOE index of Pizzinelli et al. (2023) and data from O*NET and the 2016 and 2021 censuses of population. Occupations were grouped into three distinct categories: (1) high exposure and low complementarity, (2) high exposure and high complementarity, and (3) low exposure. Being in the second group does not necessarily reduce AIOE, as workers would still need the necessary skills to be able to leverage the potential complementary benefits of AI.

On average, in May 2021, approximately 4.2 million employees (31%) in Canada were in the first group, about 3.9 million (29%) were in the second group and about 5.4 million (40%) were in the third group. This distribution was similar in May 2016. Employees in the following industries were more likely than others to be in the first group: professional, scientific and technical services; finance and insurance; and information and cultural industries. In contrast, employees in educational services, and health care and social assistance were more likely to be in the second group than other employees. Employees in industries such as accommodation and food services, manufacturing, construction, and transportation and warehousing face relatively less exposure to AI-related job transformation.

Unlike previous waves of automation, which affected routine and non-cognitive jobs, AI could affect a broader segment of the labour force than previously thought. Contrary to previous findings from the technological transformation literature, AI could transform the jobs of highly educated employees to a greater extent than those of their less educated counterparts. However, highly educated employees also hold jobs that may be highly complementary with AI. Previous labour market policy recommendations in response to the threat of automation included supporting upskilling and job transition initiatives. The findings in this article, which reflect the possible role of AI exposure and complementarity for occupations and workers in Canada, may inform future policy discussions on the topic.

The index used in this study is subjective and based on judgments regarding some current possibilities of AI. Consequently, the applicability of the index may decrease over time as AI capabilities grow and AI can perform an increasing number of tasks currently done by human workers. The index is also computed at the occupational level, implicitly assuming that tasks within a given occupation are the same across regions and worker characteristics. However, the ability to adapt and respond to changing skill demands will likely vary across worker characteristics. If tasks vary substantially across regions and worker characteristics, and if some tasks are more vulnerable to AI substitution, the index could be over- or underestimated to a certain extent. For example, computer programmers in one region who spend their work day coding may be more susceptible to AI-related job transformation if AI is proficient in writing that code. In contrast, programmers in another region who spend part of their day interacting face to face with team members may be less susceptible, assuming AI is not yet proficient in face-to-face interactions. To address this, future research could develop alternative measures of AI exposure at the worker level, similar to how Arntz, Gregory and Zierahn (2016) or Frenette and Frank (2020) estimated automation risk. Future studies could also attempt to answer the question, “What happened to workers whose jobs were exposed to AI-related job transformation?”

As AI technologies continue to evolve, they have the potential to reshape industries, redefine job roles and transform the nature of work. AI may also create new challenges and divides and push boundaries. But large-scale AI adoption may take some time, as employers may face financial, legal and institutional constraints. This study provides a static picture of AIOE based on employment compositions in Canada in May 2016 and May 2021, which were fairly similar. How AI affects productivity and how workers and firms adapt to the potentially evolving labour market in the long run remain to be seen.

Appendix

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