Key takeaways

• 4% of Canadian workers were aware of generative AI tools, but only 35.9% had used generative AI in the previous 12 months.
• Use varied considerably across industry sectors. It was highest in professional, scientific, and technical services (65.6%), followed by finance, insurance, real estate, rental, and leasing (59.2%) and educational services (53.0%).
• Workers in occupations with high exposure to AI were much more likely to use generative AI compared to those with low exposure. Use reached 53.8% in occupations where AI has high potential to complement workers versus 14.2% in occupations with low exposure.
• Workers who used generative AI did not use it for most of their work. Nearly two-thirds (63.5%) said they used it for some tasks, but not most. Only 11.6% used it for most or almost all tasks.
• Among workers who did not use generative AI, the most common reason was that they did not see it as relevant to their work. Lack of AI skills or knowledge was reported by a much smaller share of non-users (5.8%).
• AI use also differed by age and gender in some occupations. This suggests that access to and use of these tools are not the same across all groups of workers.

Plain language summary

This report finds that generative AI use varies considerably by potential occupational exposure to AI. The report classifies occupations into the following three groups based on their degree of potential exposure to and complementarity with AI:

• High exposure and high complementarity (HEHC)
• High exposure and low complementarity (HELC)
• Low exposure (LE) (regardless of complementarity)

In this context, complementarity refers to the extent to which AI is expected to enhance human work rather than replace it.

In March 2026, 53.8% of workers in HEHC occupations used generative AI at work. This was followed by 45.9% in HELC occupations and 14.2% in LE occupations.

Use also varied by age, particularly in high-exposure occupations, where core-aged workers (those aged 24 to 59) were more likely to use generative AI than their younger or older counterparts. Men were more likely than women to use generative AI in HEHC and HELC occupations. There was little gender difference in LE occupations.

Use of generative AI also differed across occupational groups: It was highest among workers in legislative and senior management occupations (75.1%) and natural and applied sciences occupations (67.5%), and lowest in the trades, transport, and equipment operator occupations (14.7%) and natural resources and agricultural occupations (17.0%). Use was also lower among private-sector employees (33.4%) than among public-sector employees (41.2%) and self-employed workers (39.6%). However, within HEHC and HELC occupations, use was similar across public- and private-sector workers.

Most people who used generative AI in their work did so moderately. Nearly two-thirds (63.5%) used it for some tasks, but not most. Broad usage remained relatively rare, with 11.6% using it for most or almost all tasks. Moderate usage was the most common pattern across HEHC, HELC, and LE occupations, while broad usage was somewhat more common in HELC occupations.

About 31.4% of workers used generative AI daily, while 38.3% did so a few times per week. Daily use was more common in high-exposure occupations. It was particularly high in natural and applied sciences work. Among workers who did not use generative AI, the most common reason was that it was not applicable to their work (56.0%). However, two other reasons included lack of interest (21.6%) and security, privacy, environmental, or ethical concerns (9.8%). Only a small percentage of respondents said their lack of use was due to insufficient skills or knowledge (5.8%).

Definitions

• AI complementarity: The extent to which AI is expected to work alongside and support workers rather than to perform their tasks for them.
• High exposure, high complementarity (HEHC) jobs: Jobs in which AI could affect many tasks, but is more likely to help workers than to replace them. For example, AI might help someone to analyze information, write, or make decisions.
• High exposure, low complementarity (HELC) jobs: Jobs in which AI could affect many tasks and has greater potential to perform tasks that workers currently do, rather than mainly helping them do tasks themselves.
• Low exposure (LE) jobs: Jobs at which fewer work tasks can be done (or are significantly affected by) AI. Jobs involving physical or hands-on tasks are more likely to be considered LE.

Why the results matter

For the public sector

The findings show where generative AI use is growing and where it remains limited. This information could help governments plan workforce policies, training, and support.

For employers and employment services

The results show which types of jobs are using generative AI more and can help identify where workers may need AI training or workplace guidance.

For post-secondary institutions and students

The findings can help institutions understand where AI is becoming part of work and prepare students with skills that are relevant to their future careers.

For educators, career development professionals, and job seekers

The report can help career development professionals and job seekers understand how AI is changing the skills that are needed at work. It suggests that strong communication, collaboration, and digital skills will continue to be valuable.

For people working with underrepresented and underserved groups

Differences in AI use by age and gender can help identify groups that may need more opportunities to access, learn about, and use these tools.

For people in the LMI ecosystem

By providing data on how workers are actually using generative AI, the findings can help compare potential AI exposure to what is happening in the workplace.

For most Canadians

The results show that high exposure to AI does not mean an entire job is being replaced. Most workers who use generative AI do so for only some of their tasks.