Insights on Canadian Society
Workplace artificial intelligence use: A profile of sociodemographic and job characteristics

Overview of the study

Using data from the Canadian Survey on Working Conditions, this article provides a profile of workers aged 15 to 69 who used artificial intelligence (AI) and automation technologies at work during the previous year. The article focuses on generative AI which refers to tools trained on large datasets that are used to create new content and to support tasks such as answering questions and problem-solving. Examples of Generative AI tools include ChatGPT and Google Gemini.

• Generative AI was the most prevalent AI technology used at work from September 2024 to July 2025.
• The proportion of workers who used generative AI nearly doubled over the survey period, increasing from 17% in September 2024 to 30% in July 2025.
• Workers in three industries—professional, scientific and technical services; educational services; and finance, insurance, real estate, rental and leasing—made up one-quarter of workers overall (25%) but represented half of generative AI users (49%).
• Workers with a bachelor’s degree or higher were five times more likely to have used generative AI in the last 12 months (37%) than those with a high school diploma or a lower level of education (7%).

Introduction

Artificial intelligence (AI) and automation technologies are increasingly being integrated into Canadian workplaces, changing the way tasks are performed, decisions are made, goods are produced and services are delivered.^Note 1

Although a growing body of research has focused on the organizational adoption of AI, comparatively little is known about the prevalence of AI use among workers and the characteristics of those users.^Note 2 The use of AI by individual workers has important implications for working conditions, task content and innovation. Further, AI adoption at the organizational level stems in part from day-to-day use by workers. Previous research has shown that the use of AI tools by workers outside formal organizational policies can be an important driver of productivity gains.^Note 3

This study uses data from the Canadian Survey on Working Conditions, which were collected from September 2024 to July 2025. It provides a sociodemographic and job characteristic profile of respondents aged 15 to 69 who reported using AI at work over the previous 12 months. The analysis is primarily descriptive; however, multivariable logistic regression models were used to assess the independent association of key factors with AI use at work.

This article focuses particularly on generative AI tools^Note 4 because of growing public interest and increased investment from companies, governments and investors, driven by its potential to significantly change workflows in many industries and occupations. Generative AI tools such as ChatGPT, Microsoft Copilot and Google Gemini are designed to understand and generate new content—including text, code, images or speech—and can also be used to support problem solving. For a list of AI and automation technologies and examples or definitions, refer to the Data sources, methods and definitions section.

While these tools have the potential to alter the nature of work in some occupations and to affect labour demand, the scope of their impact remains uncertain.^Note 5 How institutions and workers will implement and respond to AI is also evolving rapidly. It should be noted that while the results presented in this study capture whether workers have used these technologies at work in the previous 12 months, they do not capture the intensity or frequency of their use.

Generative artificial intelligence is the most common automation technology used by workers

From September 2024 to July 2025, generative AI was the most common automation technology used by workers; on average, about 2 in 10 workers (22%) reported that they had used it as part of their main job or business in the previous 12 months (Chart 1).

The proportion of workers who had used generative AI at work in the last year was nearly twice as high as the proportion of businesses that reported having used AI in the 12 months leading up to the second quarter of 2025 (12%), according to a recent analysis on AI use by businesses in Canada.^Note 6 This pattern may indicate that adoption is not solely firm-led; it may also reflect worker-driven use, occurring alongside or independently of formal organizational strategies.

Natural language processing^Note 7 (11%) and voice recognition software^Note 8 (6%) represented the second- and third-most common automation technologies used by workers. These are widely available to workers using standard office productivity software or smartphones, but their integration or use may require a dedicated application or software. A further 5% of workers had used machine learning^Note 9 at work in the previous 12 months.

Data table for Chart 1

Data table for Chart 1
Table summary
The information is grouped by artificial intelligence and automation technologies (appearing as row headers), Proportion and 95% confidence interval, calculated using lower, upper and percent units of measure (appearing as column headers).

Artificial intelligence and automation technologies	Proportion	95% confidence interval
		lower	upper
	percent
Notes: Error bars represent 95% confidence intervals. Source: Statistics Canada, Canadian Survey on Working Conditions, 2024 and 2025.
Generative artificial intelligence tools	22.1	21.4	22.9
Natural language processing	10.7	10.2	11.3
Voice recognition software	6.1	5.6	6.5
Machine learning	4.9	4.5	5.3
Machine vision software or image recognition tools	2.7	2.4	3.0
Robotics	2.0	1.7	2.2
Automated storage and retrieval systems	1.6	1.4	1.8
Augmented reality	1.1	0.9	1.2
Automated guided vehicles (AGV) or AGV systems	0.8	0.7	1.0

Generative artificial intelligence use at work nearly doubled from September 2024 to July 2025

The proportion of workers who reported using generative AI nearly doubled during the survey period, increasing from 17% in September 2024 to 30% in July 2025 (Chart 2). Notably, nearly half of the increase (7 percentage points) occurred from March to July 2025, pointing to potentially accelerating uptake. The use of other AI and automation technologies at work also rose during the survey period, but at a more moderate pace, with the use of natural language processing increasing roughly 2 percentage points to 12% and the use of machine learning rising roughly 2 percentage points to 6%.

Data table for Chart 2

Data table for Chart 2
Table summary
The information is grouped by Selected AI and automation technologies (appearing as row headers), September to October 2024, December 2024 to January 2025, March to April 2025, June to July 2025, Proportion, 95% confidence interval, Proportion, 95% confidence interval, Proportion, 95% confidence interval, Proportion and 95% confidence interval, calculated using lower, upper, lower, upper, lower, upper, lower, upper and percent units of measure (appearing as column headers).

Selected artificial intelligence and automation technologies	September to October 2024			December 2024 to January 2025			March to April 2025			June to July 2025
	Proportion	95% confidence interval		Proportion	95% confidence interval		Proportion	95% confidence interval		Proportion	95% confidence interval
		lower	upper		lower	upper		lower	upper		lower	upper
	percent
Notes: Error bars represent 95% confidence intervals. Source: Statistics Canada, Canadian Survey on Working Conditions, 2024 and 2025.
Generative artificial intelligence tools	16.6	15.3	18.0	20.1	18.5	21.6	22.6	21.1	24.1	29.5	27.5	31.2
Natural language processing	9.5	8.5	10.6	11.4	10.2	12.7	10.2	9.1	11.3	11.9	10.6	13.2
Voice recognition software	5.6	4.8	6.4	5.6	4.9	6.5	6.3	5.4	7.2	6.7	5.7	7.8
Machine learning	4.2	3.6	5.0	4.8	4.0	5.6	4.9	4.1	5.7	5.7	4.8	6.6

Workers in professional, scientific and technical services lead generative artificial intelligence use

The use of generative AI at work was concentrated in a few industries. From September 2024 to July 2025, the proportion of workers who had used generative AI as part of their main job or business over the previous 12 months was highest among those working in professional, scientific and technical services (52%); educational services (42%); and finance, insurance, real estate, rental and leasing (38%). While workers in these three industries made up one-quarter of workers overall (25%), they represented half of generative AI users (49%).

The proportion of workers who had used generative AI in the last 12 months was lowest in accommodation and food services (5%), agriculture (6%), and retail trade (9%)—industries characterized by manual and customer-facing tasks, where the applicability of generative AI remains limited. These associations remained after adjusting for job and sociodemographic characteristics.^Note 10

Half of workers in natural and applied sciences occupations used generative artificial intelligence

The use of generative AI at work was also concentrated in certain occupational groups, even after adjusting for job and sociodemographic characteristics. Notably, half (49%) of workers in natural and applied science occupations (which include computer and information systems professionals, architects, and engineers) had used generative AI at work in the previous 12 months (Chart 3). This was followed by workers in management occupations, 38% of whom had used generative AI. Conversely, the proportion was lowest among workers in occupations in manufacturing and utilities (5%) and in trades, transport and equipment operators and related occupations (5%).

Data table for Chart 3

Data table for Chart 3
Table summary
The information is grouped by Occupation (appearing as row headers), Proportion and 95% confidence interval, calculated using lower, upper and percent units of measure (appearing as column headers).

Occupation	Proportion	95% confidence interval
		lower	upper
	percent
Note: Error bars represent 95% confidence intervals. Source: Statistics Canada, Canadian Survey on Working Conditions, 2024 and 2025.
Natural and applied sciences and related occupations, except management	48.7	45.7	51.5
Management occupations	38.3	35.9	41.2
Occupations in education, law and social, community and government services, except management	33.4	31.3	36.0
Business, finance and administration occupations, except management	30.2	27.9	32.3
Occupations in art, culture, recreation and sport, except management	29.7	24.8	34.9
All occupations	22.1	21.4	22.9
Health occupations, except management	11.5	9.6	13.4
Sales and service occupations, except management	10.4	9.0	11.9
Natural resources, agriculture and related production occupations, except management	8.4	4.5	12.7
Trades, transport and equipment operators and related occupations, except management	5.1	4.0	6.5
Occupations in manufacturing and utilities, except management	4.8	3.1	6.7

The training, education, experience and responsibilities^Note 11 associated with a worker’s occupation provide additional insight into the types of skills required for their job or business, as well as the applicability of different AI and automation technologies. Notably, workers in professional occupations that typically require a bachelor’s degree or higher (44%) were much more likely to have used generative AI in the previous 12 months than workers in jobs that usually require a high school diploma or less (10%) or that do not have formal educational requirements (3%); this association remained after adjusting for covariates.

Generative artificial intelligence use is lowest among private sector employees in smaller firms

The likelihood that a worker will use AI in their job may be influenced by whether they are self-employed or are an employee at a small or large organization. For example, larger firms may have greater financial and technical capacity for dedicated implementation support,^Note 12 while self-employed workers may have fewer restrictions on their use of AI.

Overall, past-year use of generative AI use at work was higher among self-employed workers (26%) and public sector employees (25%), as well as among employees in larger private sector firms with 100 to 500 employees (24%) and over 500 employees (23%) (Chart 4). Conversely, prevalence was lower among employees in smaller private sector firms with fewer than 20 employees (15%) and 20 to 99 employees (17%). After accounting for other sociodemographic, industry and occupational characteristics,  employees in private sector firms with fewer than 20 employees continued to show a significantly lower prevalence of past year generative AI use only relative to those in private sector firms with 100 to 500 employees. However, no significant differences were observed compared with the other firm categories.

Data table for Chart 4

Data table for Chart 4
Table summary
The information is grouped by Class of worker and firm size (appearing as row headers), Proportion and 95% confidence interval, calculated using lower, upper and percent units of measure (appearing as column headers).

Class of worker and firm size	Proportion	95% confidence interval
		lower	upper
	percent
Note: Error bars represent 95% confidence intervals. Source: Statistics Canada, Canadian Survey on Working Conditions, 2024 and 2025.
Self-employed	25.5	23.3	27.8
Public sector employee (regardless of firm size)	25.4	24.0	27.0
Private sector employee (firm with fewer than 20 employees)	15.1	13.2	17.3
Private sector employee (firm with 20 to 99 employees)	17.4	15.3	19.5
Private sector employee (firm with 100 to 500 employees)	24.1	21.7	26.8
Private sector employee (firm with over 500 employees)	23.3	21.9	24.9

Artificial intelligence and control over work pace

AI and automation technologies are increasingly being adopted in the workplace to boost efficiency and productivity, but they can also influence work pace and worker autonomy.^{Note 13Note 14}

From September 2024 to July 2025, around one in six workers (16%) reported that their speed or pace of work had depended on automated software or computer procedures, in the past 12 months. This proportion was higher among workers who reported using AI or automation technologies (24%).

Among workers who used AI, fewer who used generative AI (23%) or voice recognition software (23%) reported that their work pace was dependent on software or computer procedures compared with users of other technologies. This may reflect occupations in which AI is integrated in a supportive role and its use is largely determined by the worker. By contrast, the proportion of workers whose pace was dependent on automated software or computer procedures was higher among those who reported using automated storage and retrieval systems (42%), machine learning (40%), and machine vision software or image recognition tools (38%). This may be indicative of occupational roles in which these technologies determine when and how quickly tasks are completed. Please refer to the Data sources, methods and definitions section for definitions and examples of these technologies.

Workers with higher levels of educational attainment are more likely to have used artificial intelligence and automation technologies

Previous research has shown that workers with higher levels of education are more likely to work in occupations where AI could potentially replace or augment some or all job tasks.^Note 15

From September 2024 to July 2025, workers with a bachelor’s degree or higher were five times more likely to have used generative AI in the last 12 months (37%) than those with a high school diploma or less (7%) (Chart 5). Large differences were also observed between workers with a bachelor’s degree or higher and those with a high school diploma or less in the proportion of workers who used natural language processing (17% versus 4%, respectively), voice recognition software (9% versus 3%, respectively) and machine learning (8% versus 2%, respectively).

The greater likelihood of using AI among workers with a bachelor’s degree or higher persisted after accounting for industry, occupational, workplace and worker characteristics. As such, workers with higher levels of education were more likely to have used these technologies, even when they worked in occupations or industries with relatively low adoption rates.

Data table for Chart 5

Data table for Chart 5
Table summary
The information is grouped by Select Artificial intelligence and automation technologies (appearing as row headers), High school or less , Postsecondary, below bachelor's degree, Postsecondary, bachelor's degree or higher, Proportion, Proportion and Proportion, calculated using 95% confidence interval, 95% confidence interval, 95% confidence interval, lower, upper, lower, upper, lower, upper and percent units of measure (appearing as column headers).

Select artificial intelligence and automation technologies	High school or less			Postsecondary, below bachelor's degree			Postsecondary, bachelor's degree or higher
	Proportion	95% confidence interval		Proportion	95% confidence interval		Proportion	95% confidence interval
		lower	upper		lower	upper		lower	upper
	percent
Note: Error bars represent 95% confidence intervals. Source: Statistics Canada, Canadian Survey on Working Conditions, 2024 and 2025.
Generative artificial intelligence tools	7.4	6.3	8.6	15.6	14.6	16.6	37.0	35.5	38.5
Natural language processing	3.8	3.0	4.7	8.0	7.1	8.8	17.4	16.4	18.6
Voice recognition software	2.8	2.2	3.5	5.4	4.6	6.1	8.6	7.9	9.5
Machine learning	2.1	1.5	2.8	3.4	2.8	3.9	8.0	7.2	8.8

Generative artificial intelligence use at work is highest among core-aged workers

Patterns and types of AI use also vary by age, reflecting differences in occupational roles, career stages and the ways technologies are integrated into jobs.^Note 16 From September 2024 to July 2025, the prevalence of generative AI use at work was higher among workers aged 25 to 54 (27%) than among those aged 15 to 24 (10%) or 55 to 69 (15%).

However, when accounting for job and sociodemographic characteristics, differences between younger workers and core-aged workers were no longer statistically significant. The multivariable models indicate that the lower rate of generative AI use at work among younger workers was particularly the result of their concentration in occupations with less prevalent use and lower educational and skill requirements. Conversely, workers aged 55 to 69 continued to be less likely than core-aged workers (aged 25 to 54) to have used generative AI, even after adjusting for these factors.

Women are less likely than men to report using various artificial intelligence and automation technologies over the previous year

From September 2024 to July 2025, women (22%) and men (22%) were overall equally likely to have used generative AI at work in the past 12 months. However, since men are overrepresented in some occupations and industries with lower rates of AI use, this may have masked gender differences.

Indeed, when these factors, in addition to educational attainment, were now considered, differences between men and women emerged. Women were now less likely to report using generative AI at work, suggesting that these differences persist beyond industry and occupational composition. For example, in the women-dominated industry of health care and social assistance, 21% of men reported using generative AI at work, compared with 13% of women. Similarly, in the professional, scientific and technical services industry, where men make up a larger proportion of the workforce than women, men (55%) were more likely to report using generative AI at work than women (47%). However, these gender differences should be interpreted in the context of variations in occupational tasks and digital exposure within industries as well as the use of relatively broad occupational categories in the multivariable models, which do not capture more granular differences in task content.

Men (11%) and women (11%) were equally likely to report having used natural language processing at work during the previous 12 months. Conversely, men were more likely than women to report having used machine learning (6% versus 4%) over the previous year. These gender differences persisted even after accounting for worker and workplace characteristics. Please see the Data sources, methods and definitions section for definitions and examples of these technologies.

Generative artificial intelligence use is more common among workers in British Columbia and Ontario

There were provincial and regional variations in worker-reported technology use at work over the survey period. From September 2024 to July 2025, the proportion of workers who had used generative AI at work in the past year was highest in British Columbia (25%), Ontario (24%), Quebec (21%) and Alberta (21%), while it was lowest in Manitoba and Saskatchewan (17%) and Atlantic Canada (18%) (Chart 6).

However, for most AI and automation technologies, provincial and regional differences in overall prevalence largely reflected variations in industrial and occupational composition and worker characteristics. For example, the lower prevalence of generative AI use at work in Manitoba and Saskatchewan and Atlantic Canada was no longer statistically significant relative to Ontario after adjusting for other characteristics in the multivariable regression model. However, workers in British Columbia remained more likely to have used generative AI at work in the past year than those in Ontario, even after accounting for these factors.

Data table for Chart 6

Data table for Chart 6
Table summary
The information is grouped by Region (appearing as row headers), Proportion and 95% confidence interval, calculated using lower, upper and percent units of measure (appearing as column headers).

Region	Proportion	95% confidence interval
		lower	upper
	percent
Note: Error bars represent 95% confidence intervals. Source: Statistics Canada, Canadian Survey on Working Conditions, 2024 and 2025.
Atlantic provinces	17.5	15.9	19.1
Quebec	21.2	19.8	22.6
Ontario	23.6	22.2	24.8
Manitoba and Saskatchewan	16.8	15.3	18.3
Alberta	21.1	18.6	23.5
British Columbia	25.0	22.8	27.3

Conclusion

This study provided new information on the use of AI and automation technologies at work, showing a growing uptake of generative AI use among workers in Canada, with variations across job and sociodemographic categories.

Generative AI was the most common automation technology used by workers from September 2024 to July 2025, likely because of its widespread accessibility, usability and adoptability. These factors also help explain an accelerating uptake, with use nearly doubling during this period.

Generative AI use was notably higher among workers in certain occupations and industries. Around half of employed people in natural and applied science occupations and in the professional, scientific and technical services industry reported having used generative AI in the previous 12 months. Workers with a higher educational attainment were also more likely to have used generative AI at work, even when they worked in occupations or industries with lower adoption rates.

Looking ahead, future research will continue to report on trends in the use of AI and automation technologies at work. It will deepen the insights presented in this study by exploring the frequency and intensity of AI and automation technology use.

 

Data sources, methods and definitions

The Canadian Survey on Working Conditions (CSWC) is a new survey that measures the core dimensions of quality of employment—including working time and work–life balance, skills development and training, and security of employment—to better understand how different job characteristics are distributed across the employed population.

The CSWC was collected from September 2024 to July 2025 over the course of four collection periods:

Wave 1: September 23 to October 18, 2024
Wave 2: December 16, 2024, to January 17, 2025
Wave 3: March 17 to April 17, 2025
Wave 4: June 23 to July 18, 2025.

Data from the CSWC reflect the average of the collection periods and are representative of the employed population aged 15 to 69 living in the provinces. Individuals living on Indigenous reserves and other settlements, full-time members of the armed forces, and unpaid family workers are excluded.

For more information on the survey methodology, see Canadian Survey on Working Conditions (CSWC).

The CSWC asked employed people whether they had used any AI or automation technology in the past 12 months in their main job or business. Respondents could select one or more of the AI or automation technologies in the table below or indicate they had not used any of them. Technologies were measured as separate categories based on self-reported use, although some may be closely related and built on similar underlying technologies.

Table 1
Table summary
The information is grouped by Category (appearing as row headers), , calculated using (appearing as column headers).

Category	Help text
Generative AI tools	e.g., ChatGPT, Microsoft Copilot, Google Gemini
Machine learning	Tools which are refined on large training datasets used to automate the prediction or classification of data
Augmented reality	Superimposes digital information, such as images, sounds, or texts onto the user’s view of the real world
Automated guided vehicles (AGV) or AGV systems	e.g., self-driving vehicles
Automated storage and retrieval systems	Systems that use automated machines or robots to store and retrieve items from warehouses or storage facilities.
Machine vision software or image recognition tools	Software that can analyze and understand images or videos using artificial intelligence
Natural language processing	e.g., autocomplete, language checking, language translation
Robotics	No help text provided in the questionnaire
Voice recognition software	e.g., Google Assistant, Siri, Alexa, Cortana

Various factors were also considered in multivariable regression analyses to assess their independent associations with AI use, with a specific focus on generative AI. These factors included job characteristics, such as industry, occupation, occupational skill level (National Occupational Classification training, education, experience and responsibilities level), firm size and class of worker, as well as sociodemographic characteristics, such as educational attainment, age group, gender and region. The results are presented in Table A.1. Descriptive analyses were presented using more detailed occupational, industry, and TEER classifications to provide a granular overview of observed patterns. For multivariable modelling TEER categories were aggregated to capture broad differences in skill level and responsibilities while avoiding unnecessary granularity in a model that already included detailed occupational and industry controls. As a robustness check, alternative model specifications incorporating both more and less aggregated occupational and industry controls were estimated, with substantive findings remaining consistent across specifications.

Appendix

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