Use of generative artificial intelligence tools among Canadian workers, March 2026
Released: 2026-07-30
Generative artificial intelligence (AI) tools—such as those used to produce text, images and computer code—have emerged rapidly and are increasingly shaping how tasks are performed across workplaces in Canada. While the capabilities of these technologies continue to evolve, their integration into day-to-day work is uneven.
Today, Statistics Canada releases the first results from a regular series on AI use by Canadian workers. Results presented today are based on supplementary questions to the Labour Force Survey collected for workers aged 15 to 69 years in March 2026.
The vast majority of workers are aware of generative artificial intelligence tools, and about one-half are familiar with how these tools could be applied to their current work
In March 2026, the vast majority (93.4%) of workers reported being aware of generative AI tools. Among them, just over one-half (51.5%) were familiar with how these tools could be applied to their current work, including 15.0% who reported being very familiar. A further 11.1% were not familiar with how these tools could be applied to their work. At the same time, 37.4% indicated being familiar with generative AI tools, but they did not believe they were applicable to their work.
Just over one in three workers used generative artificial intelligence tools at work in the past 12 months
In March 2026, 41.6% of workers reported having used at least one AI or automation technology as part of their main job or business over the previous 12 months. Generative AI tools were by far the most commonly reported AI or automation technology, having been used by 35.9% of workers, or just over one in three workers.
Across industries, use of generative AI tools at work was more prevalent in professional, scientific and technical services (65.6%), finance, insurance, real estate, rental and leasing (59.2%) and educational services (53.0%). In comparison, their use was lowest in accommodation and food services (16.3%), agriculture (17.5%) and transportation and warehousing (21.1%).
Potential exposure to and complementarity with artificial intelligence occupational groups
Occupations can be classified into three groups based on their degree of potential exposure to and complementarity with artificial intelligence (AI): (1) high exposure and high complementarity (HEHC), (2) high exposure and low complementarity (HELC) and (3) low exposure (LE) (regardless of the degree of complementarity).
HEHC occupations, such as doctors, nurses, teachers and engineers, are associated with tasks with more potential complementarity with AI and therefore might be more likely to benefit from these technologies. In contrast, HELC occupations, including occupations in retail sales, office support and software development and accounting, may be more susceptible to task replacement by AI. LE occupations, such as skilled trades, service jobs and first responders, may be less likely to be affected by AI-related transformation.
In March 2026, 31.2% of workers aged 15 to 69 years were employed in HEHC occupations, 29.6% in HELC occupations and 39.3% in LE occupations.
For more information on recent employment trends based on these occupational groupings, see "Canadian employment trends in the era of generative artificial intelligence: Early evidence."
Workers in high-exposure, high-complementarity occupations are most likely to use generative artificial intelligence tools, with notable age and gender differences
In March 2026, the use of generative AI was most common in occupations with high exposure to and complementarity with (HEHC) AI (see the Potential exposure to and complementarity with artificial intelligence occupational groups text box).
Over half (53.8%) of workers in HEHC occupations reported using generative AI tools at work. This was followed by those in high-exposure, low-complementarity (HELC) occupations (45.9%). The share of workers using generative AI tools was significantly lower among workers in low exposure (LE) occupations (14.2%).
The use of generative AI at work varied by age group. For workers in HEHC occupations, use was higher among core-aged workers aged 25 to 54 years (56.9%), compared with workers aged 55 years and older (45.3%) and youth aged 15 to 24 years (39.1%).
Similarly, just over half (51.9%) of core-aged workers in HELC occupations reported using generative AI tools, compared with one-third of young (33.2%) and older (31.5%) workers. Although the use of generative AI tools was relatively low within LE occupations, it was lowest among older workers, with 6.7% of those aged 55 years and older reporting using generative AI tools at work, compared with 15.3% of younger and 16.3% of core-aged workers.
Chart 1
Proportion of workers aged 15 to 69 using generative artificial intelligence (AI) at work in the past 12 months, by potential occupational exposure to and complementarity with AI and by age, March 2026
Within each potential exposure to and complementarity with AI occupational group, men were on average more likely than women to use generative AI tools at work. In HEHC occupations, 57.0% of men reported using generative AI tools, compared with 50.9% of women. A larger difference was observed in HELC occupations, where use was 11.7 percentage points higher among men (52.9%) than women (41.2%). There was virtually no gender difference within LE occupations, with 14.1% of men and 14.4% of women reporting using generative AI tools at work.
Use of generative artificial intelligence tools highest among management occupations and natural and applied sciences and related occupations
In March 2026, generative AI use was highest among workers in legislative and senior management occupations (75.1%) and natural and applied sciences (67.5%), and use was lowest among workers in trades, transport and equipment operators (14.7%) and natural resource, agriculture and related occupations (17.0%). These results align with differences in potential occupational AI exposure, as management and scientific occupations are more likely to be considered highly exposed to AI, whereas occupations in agriculture or trades have lower potential exposure to AI.
Chart 2
Proportion of workers aged 15 to 69 using generative artificial intelligence tools at work in the past 12 months by broad occupational group, March 2026
Use of generative AI in the past 12 months was lower among private sector employees (33.4%), compared with public sector employees (41.2%) and those who were self-employed (39.6%).
Part of this difference reflected the higher share of occupations with high potential exposure to AI in the public sector. Within HEHC occupations, the use of generative AI tools by workers in the private (54.9%) and public (54.5%) sectors was similar. Likewise, there was little difference between the private (45.8%) and public (43.1%) sectors within HELC occupations.
Nearly two-thirds of users apply generative artificial intelligence tools to some tasks at their job, while usage for most tasks remains rare
In March 2026, among people who reported using generative AI in the past 12 months, most used AI moderately in their work; that is, they used AI tools for some but not most tasks. Nearly two-thirds (63.5%) of users fell into this category. Meanwhile, minimal usage, referring to use for almost no tasks, was reported by one-quarter (24.9%) of users.
Broad usage, which captures use across most or nearly all tasks, remained relatively rare. Among users of generative AI tools at work, 8.0% reported using them for most tasks and 3.6%, for almost all tasks, for a combined total of 11.6%.
Moderate usage prevailed across all potential exposure to and complementarity with AI occupational groups. In March 2026, two-thirds of users in high-exposure occupations reported using generative AI for some, but not most, tasks (67.6% in HEHC and 63.6% in HELC), compared with one-half (50.7%) in LE occupations.
Among generative AI users, broad usage was more common in HELC occupations (15.4%) relative to HEHC (9.2%) and LE occupations (9.9%).
Chart 3
Extent of generative artificial intelligence (AI) use across work tasks among workers aged 15 to 69 by potential occupational exposure to and complementarity with AI, March 2026
Just over 3 in 10 users work with generative artificial intelligence tools daily, while nearly 4 in 10 use them weekly
Most workers who use generative AI tools do so regularly, with a majority reporting daily or weekly use. In March 2026, daily use was reported by just over 3 in 10 users (31.4%), while 38.3% used these tools a few times per week. In comparison, just over one in five users (21.7%) reported using AI tools a few times per month, while 8.7% used them a few times a year.
For most occupational groups, using generative AI tools a few times per week was the most reported frequency among users. Daily use of generative AI tools at work was concentrated in certain occupations in March 2026. In particular, 45.6% of users in natural and applied sciences reported using these tools daily, compared with lower shares among occupations in manufacturing and utilities (18.6%) as well as natural resources, agriculture and related occupations (18.2%). This pattern is consistent with differences in potential occupational exposure to AI, as daily use was more common in high-exposure occupations—across both low- (37.1%) and high- (31.1%) complementarity roles—than among workers in LE occupations (18.2%).
Chart 4
Frequency of generative artificial intelligence (AI) use among workers aged 15 to 69 by potential occupational exposure to and complementarity with AI, March 2026
Over half of non-users report that generative artificial intelligence tools are not applicable to their work
In March 2026, among workers who did not use generative AI as part of their main job or business in the previous 12 months—representing nearly two-thirds (64.1%) of all workers—the most commonly cited reason was that generative AI tools had no applicability to their work (56.0%). This reason was reported more frequently by private sector employees (59.8%) than by public sector employees (47.7%) and self-employed workers (48.6%).
Meanwhile, one in five workers (21.6%) indicated that they had no interest in using generative AI tools, and this was more commonly reported by self-employed workers (26.9%) than by public sector (21.4%) or private sector (20.8%) employees.
Nearly 1 in 10 workers who had not used generative AI tools (9.8%) cited security, privacy, environmental or ethical concerns, with public employees (16.9%) being more likely than private employees (7.4%) and self-employed workers (11.1%) to report this reason for not using generative AI.
Barriers related to skills, training and access were reported by a smaller share of non-users. For example, 5.8% indicated a lack of skills or knowledge, although self-employed workers (10.7%) were more likely than public sector employees (6.7%) and private employees (4.7%) to cite this barrier.
About 5.0% of non-users indicated that company or organizational policies limited their use of generative AI tools. This reason was reported most often by public sector employees (8.9%), followed by private sector employees (4.2%) and self-employed workers (1.8%).
Note to readers
As announced in Budget 2025, Statistics Canada has invested in a new technology measurement program, TechStat, which provides data, analysis and insights on the adoption, use and impacts of artificial intelligence (AI) for businesses, individuals and employees.
This is the first TechStat release looking at the use of AI by workers. It is based on supplementary questions to the March 2026 Labour Force Survey (LFS). Additional data collection through a supplement to the LFS is planned for September 2026 and will be repeated regularly in the future. This will allow for trend monitoring, and for more disaggregated data and analysis to be produced using pooled data.
A separate and complementary analysis examining the socio-economic and job characteristics associated with AI use by workers, based on the 2024/2025 Canadian Survey on Working Conditions, was released on June 17, 2026. For more information, see: Workplace artificial intelligence use: A profile of sociodemographic and job characteristics.
Labour Force Survey supplement
This release uses data from the supplement to the LFS from March 2026, which focused on usage of AI in the workplace.
The universe for the March 2026 LFS supplements consists of respondents aged 15 to 69 years who live in the provinces. The sample excludes those living on reserves, full-time members of the regular Armed Forces or those living in institutions.
For more information, including the questionnaire, please see the Labour Market Indicators program. For more detail on the LFS, please consult the Guide to the Labour Force Survey.
Data for the Labour Market Indicators program are now available for March 2026.
Definitions and information on interpretation
Occupation refers to the kind of work the respondent was doing during the reference week, as determined by the type of work reported and the description of the most important duties of the job. Occupations are coded according to the 2021 National Occupational Classification (NOC).
This article uses the complementarity-adjusted AI occupational exposure (C-AIOE) index, which categorizes occupations or NOC codes based on how they can potentially be affected by AI. The C-AIOE assigns each occupation two scores: one measuring its potential exposure to AI, based on the extent to which AI can influence, transform, or perform its tasks, and another capturing complementarity, which reflects the degree to which AI may enhance or augment human labour. Occupations are then classified into three groups using the median exposure and complementarity scores across all occupations: (1) high exposure and low complementarity, (2) high exposure and high complementarity and (3) low exposure. The index should be interpreted as an indication of the potential for AI to transform job tasks rather than as a direct prediction of job loss, since the actual impact of AI adoption depends on multiple economic, institutional and technological factors. Originally developed by Felten, Raj and Seamans (2021) and extended by Pizzinelli et al. (2023), the C-AIOE index has been applied to the Canadian context by Mehdi and Morissette (2024), Mehdi and Frenette (2024, 2026) and others.
Industry refers to the general nature of the business carried out by the employer for whom the employee works (main job only) or of the business for the self-employed (main business only). Industry estimates in this release are based on the 2022 North American Industry Classification System.
The LFS estimates are based on a sample and are therefore subject to sampling variability. The analysis focuses on differences between estimates that are statistically significant at the 95% confidence level.
Due to rounding, estimates and percentages may differ slightly between different Statistics Canada products, such as analytical documents and data tables.
Correction
On August 17, 2026, a correction was made to the labels used in Chart 2 to designate the broad occupational groups according to the National Occupational Classification (NOC) 2021 Version 1.0. Before the correction, the labels were from the Variant of the NOC 2021 Version 1.0 with Aggregates for Analysis of Labour force.
Contact information
For more information, or to enquire about the concepts, methods or data quality of this release, contact us (toll-free 1-800-263-1136; 514-283-8300; infostats@statcan.gc.ca) or Media Relations (statcan.mediahotline-ligneinfomedias.statcan@statcan.gc.ca).
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