Economic and Social Reports
Potential occupational exposure to artificial intelligence and automation among certified journeypersons in Canada

Artificial intelligence (AI) and automation hold the potential to transform the nature of work, raising concerns about how different occupations may be affected (Frenette and Frank, 2020; Mehdi and Morissette, 2024; Mehdi and Frenette, 2024). Although often used interchangeably, AI and automation represent different concepts: AI encompasses technologies capable of performing complex, non-routine and cognitive tasks, whereas automation refers to systems or machines designed to perform simple, routine and non-cognitive tasks.

Recent estimates suggested that approximately 60% of employees in Canada may be highly exposed to AI-related job transformations, with AI complementing rather than replacing the work of about half of these individuals (Mehdi and Morissette, 2024). By contrast, about 1 in 10 workers may face a high likelihood (70% probability or greater) of automation-related job transformation (Frenette and Frank, 2020). However, these rates may vary substantially across occupations. 

The risks associated with technological advancements are particularly relevant for the skilled trades, where work is task-intensive and specialized. Mehdi and Morissette (2024) found that skilled trades occupations such as plumbers, carpenters and welders—where men are more likely to be employed than women—may face lower exposure to AI-related job transformation relative to other occupations. However, Frenette and Frank (2020) found that the skilled trades—particularly industrial, electrical and construction jobs that are also male-dominated—may face a higher risk of automation than other occupations. These contrasting patterns highlight how different forms of technological change—AI versus automation—may affect occupational groups and gendered labour market patterns in distinct ways.

With ongoing advancements in the skilled trades and the high demand for such workers across Canada (Employment and Social Development Canada [ESDC], 2025a), it is important to understand how AI and automation may affect certified^Note  journeypersons—“individuals who have completed an apprenticeship program or earned a certificate of qualification” (Statistics Canada, 2023).

This article examines potential exposure to AI- and automation-related job transformation among certified journeyperson occupations using data from two sources: (1) the 2023 Registered Apprenticeship Information System (RAIS) and (2) the 2016 Longitudinal and International Study of Adults (LISA). Journeyperson occupations were identified as the set of National Occupational Classification (NOC) codes that appeared in the 2023 RAIS, limited to 19 trades (based on the 2021 NOC codes, see Statistics Canada, 2021) with certification available across all provinces and territories to ensure consistency across jurisdictions, according to the Ellis Chart (ESDC, 2025b).^Note Note 

The occupations in this group were then assessed for potential AI occupational exposure and complementarity using the complementarity-adjusted AI occupational exposure (C-AIOE) index, which was also used by Mehdi and Morissette (2024) and Mehdi and Frenette (2024). Automation risk was assessed using the methodology of Frenette and Frank (2020), which requires individual-level skill use data (e.g., frequency of job tasks such as public speaking, advising, persuading, negotiating and performing physical work). Skill use could vary across workers with the same occupation; therefore, knowing only the occupation of each worker is not sufficient for estimating the risk of automation-related job transformation. As skill use data are not available in the RAIS, automation risk was estimated using the 2016 LISA—the same data source used by Frenette and Frank (2020).^Note Note 

A caveat of this study is that employers may not immediately replace human labour with AI or automation, even if it is technologically feasible to do so, because of financial, legal and institutional constraints. Consequently, exposure to AI or automation does not necessarily imply a risk of job loss. At the very least, it could imply a certain degree of job transformation.^Note  For example, simple tasks could be replaced by technology while the human worker pivots to supervising the machine or reviewing the machine’s output rather than being displaced. Given the uncertainty surrounding technological progress, the estimates presented in this article should be interpreted with caution.

Journeyperson occupations may face relatively lower exposure to artificial intelligence-related job transformation compared with other occupations

AI exposure and complementarity^Note  were assessed using the C-AIOE index, which classifies occupations into three distinct groups based on the median exposure and complementarity scores across occupations: (1) high exposure, low complementarity; (2) high exposure, high complementarity; and (3) low exposure (see Felten et al. [2021] and Pizzinelli et al. [2023] for details on methodology). Figure 1 shows how journeyperson occupations map onto this index. The majority of journeypersons certified in occupations such as plumbers, carpenters, and welders appear to be less exposed to AI-related job transformation than others. This finding is not surprising, since the types of tasks in these occupations tend to involve more manual labour, which may be less susceptible to AI substitutability or replacement. However, the repetitive nature of some tasks within these occupations increases the potential for automation.

Description for Figure 1

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 two different colours. The colours are used to distinguish journeyperson occupations from other occupations. The journeyperson occupations were identified based on those found on the 2023 Registered Apprenticeship Information System, and which are certifiable across all provinces and territories. Some examples of journeyperson occupations include carpenters, plumbers, cooks, heavy-duty equipment mechanics, machinists, cooks, and hairstylists and barbers. 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 and bottom-left quadrant which comprise the left-half of the chart contain data points representing occupations which might be relatively less exposed to AI. All the journeyperson occupations identified in this study fall into this group. The top-right quadrant contain data points representing occupations which might be highly exposed to AI and highly complementary with AI. Some examples include registered nurses, physicians, teachers, mechanical and civil and electrical engineers. The bottom-right quadrant contain data points representing occupations which might be highly exposed to AI but less complementary with AI. Some examples include data entry clerks, general office support workers, web designers, and database analysts and data administrators.

Journeyperson occupations may be more likely to face automation-related job transformation than other occupations

Around 20% of employees in journeyperson occupations were predicted to be at high risk of automation-related job transformation, compared with 13% in other occupations—a statistically significant difference (Chart 1).^Note  However, the difference in automation risk between men and women within journeyperson occupations was not statistically significant. This finding may be partly explained by the fact that the journeyperson occupations examined in this study—those with certification available across all provinces and territories—are largely male-dominated, with men making up more than 85% of workers in these jobs.

Data table for Chart 1

Data table for chart 1
Table summary
This table displays the results of Data table for chart 1 Percent and 95% confidence interval, calculated using lower bound and upper bound units of measure (appearing as column headers).

	Percent	95% confidence interval
		lower bound	upper bound
Notes: The methdology for estimating automation risk is based on the work of Frenette and Frank (2020) who used the 2016 Longitudinal and International Study of Adults survey. The estimates presented on this chart are based on a slighly larger sample than the one used by Frenette and Frank (2020). This is because Frenette and Frank (2020) had a broader focus and examined worker characteristics beyond occupation and sex so their sample was restricted to respondents who provided valid responses to many other questions. The vertical bars overlaid on the bars indicate the 95% confidence interval. Sources: Statistics Canada, Longitudinal and International Study of Adults, 2016; and Registered Apprenticeship Information System, 2023.
Journeyperson occupations	20.3	14.1	26.5
Other occupations	12.8	11.7	13.9

Conclusion

While recent developments in AI and automation technologies have sparked excitement and concerns about their implications for the economy and society, the net impact of such advancements on jobs remains unclear. The findings in this study indicate that while the majority of certified journeyperson occupations may be less exposed to AI-related job transformation than other occupations, they could face a comparatively higher risk of automation by machines. The estimates presented in this article are intended to be forward-looking, based on current assessments of technology—they do not account for longer-term factors such as the adaptability of workers, businesses or governments, or the rate and intensity of AI adoption across industries. For example, while the percentage of businesses that reported using AI to produce goods or deliver services doubled from 6% in the 2023/2024 period to 12% in the 2024/2025 period, the percentage of such businesses reporting a decrease in employment because of AI remained unchanged at 6% (Bryan et al., 2024; Bryan et al., 2025).

The results presented in this study can inform labour market policies related to reskilling and career planning. Even if AI and automation were to have no net impact on jobs, they may still affect other facets of the economy, such as labour productivity. How workers, businesses and governments respond to these developments remains uncertain.

Authors

Allison Leanage is with the Health Analysis and Modelling Division and Tahsin Mehdi is with the Economic and Social Analysis and Modelling Division at Statistics Canada.

Acknowledgments

The authors would like to thank Marc Frenette, Ping Ching Winnie Chan, Rubab Arim, Max Stick, Aimé Ntwari, Lahouaria Yssaad and Andrew Fields from Statistics Canada for their comments and suggestions.

References

Bryan, V., Sood, S. and C. Johnston. 2024. Analysis on artificial intelligence use by businesses in Canada, second quarter of 2024. Analysis in Brief. Ottawa: Statistics Canada.

Bryan, V., Sood, S. and C. Johnston. 2025. Analysis on artificial intelligence use by businesses in Canada, second quarter of 2025. Analysis in Brief. Ottawa: Statistics Canada.

Employment and Social Development Canada. 2025a. Find your skilled trade. The future is yours to make. Retrieved on June 24, 2025.

Employment and Social Development Canada. 2025b, August 19. Search Ellis Chart. Ellis Chart. https://www.ellischart.ca/eng/search/s.2.1rch.shtml.

Felten, E., Raj, M. and R. Seamans. 2021. Occupational, Industry, and Geographic Exposure to Artificial Intelligence: A Novel Dataset and its Potential Uses. Strategic Management Journal 42(12): 2195-2217.

Frenette, M. and K. Frank. 2020. Automation and job transformation in Canada: Who’s at risk? Analytical Studies Branch Research Paper Series. Ottawa: Statistics Canada.

Mehdi, T. and M. Frenette. 2024. Exposure to artificial intelligence in Canadian jobs: Experimental estimates. Economic and Social Reports. Ottawa: Statistics Canada.

Mehdi, T. and R. Morissette. 2024. Experimental estimates of potential artificial intelligence occupational exposure in Canada. Analytical Studies Branch Research Paper Series. Ottawa: Statistics Canada.

Pizzinelli, C., Panton, A. J., Tavares, M. M., Cazzaniga, M. and L. Li. 2023. Labour market exposure to AI: Cross-country differences and distributional implications. International Monetary Fund, Staff Discussion Notes no. 216.

Statistics Canada. (2021, September 28). National Occupational Classification (NOC) 2021 Version 1.0 [Web page]. Government of Canada. https://www23.statcan.gc.ca/imdb/p3VD.pl?Function=getVD&TVD=1322554

Statistics Canada. 2023. Earnings and mobility indicators for newly certified journeypersons in Canada, 2021. Ottawa: Statistics Canada.