AI Job Risk in Canada
Canada pairs an advanced, urban knowledge economy in finance, technology, and professional services with a resource economy built on energy, mining, and forestry that operates across vast, sparsely populated territory. That geography matters as much as sector mix: AI adoption moves fastest in the office towers of Toronto, Montreal, and Calgary, while extraction, transportation, and the bilingual federal and provincial public service depend on physical operations and language-specific administrative work that resist quick automation.
Average AI Risk
45.12 / 100
Jobs Analyzed
204
How to read this page in practice
The notes below explain how to interpret the country score, what kinds of sector mix usually raise or lower it, and what this comparison can and cannot tell you.
How to Read This Country
Canada is easiest to read by separating concentrated urban knowledge work from resource extraction and the public administration spread across the country. Banking, insurance, IT services, and corporate administrative work sit in the exposed layer, concentrated in a handful of major metro areas with deep financial and tech sectors. Mining, oil and gas field operations, forestry, and the large bilingual public service sit in the more durable layer, where remote site work, safety-critical operations, and French-English service obligations under federal law keep substantial human staffing in place.
What Drives the Score
Employment concentrates in financial services and technology in central Canada, energy and mining in Alberta and parts of the west, forestry and fisheries in coastal and northern regions, and a large public sector shaped by federal bilingualism requirements. AI pressure is sharpest in bank back offices, insurance underwriting, IT support, and administrative processing in major cities. It spreads more slowly through oil sands and mine site operations, forestry and fisheries work tied to remote regions, and public-facing government services that must be delivered in both English and French, which adds translation and compliance layers that slow any purely automated rollout.
What Holds Up Better
What stays durable is work anchored to physical resource operations and to Canada's bilingual public administration: mine and rig technicians, foresters, fisheries workers, and civil servants delivering federally mandated services in two languages. The country's reliance on a resource sector operating far from major population centers also means site supervision, equipment maintenance, and safety judgment retain outsized importance relative to economies built more heavily around office-based services, and translation obligations add a layer of work that has no equivalent in unilingual economies.
What This Page Does Not Claim
A single Canadian score blends a small number of dense, AI-exposed metro economies with vast resource and rural regions where the labor market looks entirely different. It also cannot capture how bilingual service obligations slow automation in public administration specifically in Canada, a factor with no equivalent in most peer economies. Read the number together with the urban-resource divide and provincial variation, rather than as one uniform national trend.
Jobs Most At Risk from AI
This table is a current snapshot of the jobs that appear on the higher-risk side within this country profile. It is useful as a directional comparison, not as a permanent national ranking.
| Rank | Job | Risk Score |
|---|---|---|
| 1 | Software Tester | 85 |
| 2 | Data Entry Clerk | 82 |
| 3 | Retail Cashier | 79 |
| 4 | Data Analyst | 79 |
| 5 | Bookkeeper | 78 |
| 6 | Accounting Clerk | 77 |
| 7 | Truck Driver | 77 |
| 8 | QA Engineer | 77 |
| 9 | Proofreader | 76 |
| 10 | Translator | 74 |
| 11 | Insurance Underwriter | 73 |
| 12 | Mobile App Developer | 73 |
| 13 | Software Engineer | 73 |
| 14 | Civil Drafter | 73 |
| 15 | Taxi Driver | 72 |
| 16 | System Administrator | 71 |
| 17 | Bank Teller | 69 |
| 18 | Tax Preparer | 69 |
| 19 | Programmer | 69 |
| 20 | IT Support Specialist | 67 |
Jobs Safest from AI
This table shows the jobs that currently appear on the lower-risk side within this country profile. Read it as a structural comparison of work, not as a guarantee that these roles will stay unchanged.
| Rank | Job | Risk Score |
|---|---|---|
| 1 | Surgeon | 10 |
| 2 | Therapist | 11 |
| 3 | Electrician | 11 |
| 4 | Plumber | 11 |
| 5 | Psychologist | 12 |
| 6 | Paramedic | 14 |
| 7 | Nurse | 15 |
| 8 | Dentist | 15 |
| 9 | Psychiatrist | 16 |
| 10 | School Counselor | 16 |
| 11 | Veterinarian | 17 |
| 12 | Machine Learning Engineer | 17 |
| 13 | Professor | 18 |
| 14 | Doctor | 19 |
| 15 | Air Traffic Controller | 19 |
| 16 | Social Worker | 20 |
| 17 | Elevator Technician | 21 |
| 18 | Teacher | 22 |
| 19 | Aircraft Mechanic | 22 |
| 20 | Astronomer | 22 |
Industry Risk
This table compares the industries that shape the country score today. It is most useful for seeing which parts of the economy pull the average up or down.
| Industry | Industry Average Risk Score |
|---|---|
| Media | 64.67 |
| Retail | 62.5 |
| Finance | 59.87 |
| Technology | 54.78 |
| Transportation | 45.1 |
| Manufacturing | 41.63 |
| Energy | 37.67 |
| Construction | 34.25 |
| Science | 32.33 |
| Education | 31.92 |
| Healthcare | 26.13 |
Frequently asked questions
Q.Which jobs are most at risk from AI in Canada?
In Canada, the jobs with the highest AI risk scores include Software Tester. The full ranking of the most and least exposed jobs in Canada is shown above.
Q.Which jobs are safest from AI in Canada?
The Canada roles least exposed to AI automation include Surgeon, which tend to rely on physical work, in-person interaction, or accountable judgment.
Q.How exposed is Canada to AI automation?
A country's exposure mostly reflects what its workforce actually does. Canada combines highly exposed office and back-office work with more durable physical, field, or care work, so a single national score is a broad signal rather than a full picture.
Q.Does a high AI risk score mean jobs will disappear in Canada?
No. The score measures how exposed typical tasks are to automation, not a forecast of job losses. Real-world adoption also depends on cost, regulation, and local labor conditions.