Canada is short of the workers
AI reaches furthest into.
Two official pictures of the same labour market disagree. ESDC projects a shortage of pharmacists, psychologists, dietitians, teachers and engineers over the next decade. Measured AI-usage data puts those same occupations in the highest exposure band we track. Both are true, and almost nobody is reading them together.
- Short-staffed and heavily exposed
- 15 occupations
- Of those, a strong shortage risk
- 4 occupations
- Exposed but amplified, not replaced
- 81% of exposed occupations
The overlap
Of 342 occupations, 100 carry an ESDC projection that Canada will struggle to fill them. Separately, measured telemetry from Microsoft Research and the Anthropic Economic Index places a set of occupations in the top exposure band — the work AI already reaches deepest into. 15 occupations appear on both lists.
That combination is the awkward one. A shortage says employers will want more of these people. High exposure says more of the task is already being done with AI. Neither cancels the other out, and the policy response to one is not the response to the other.
Exposure: Microsoft Research (arXiv:2507.07935) and Anthropic Economic Index, measured. Outlook: ESDC COPS 2024–2033, verbatim. Percentile is a composite of the two telemetry measures.
Exposure is not the same as replacement
It is worth being precise about what a high exposure score means, because the number is routinely read as a countdown. It measures how much of an occupation's work AI already touches. It says nothing on its own about whether the person doing that work is amplified or removed.
Pairing exposure with Goldman Sachs's augmentation-versus-substitution classification separates the two. Across 342 occupations, 249 (73%) are measurably exposed — but 81% of those are classed as amplified rather than substituted. Only 47 occupations (14%) are both exposed and classed as substituted.
One thing to hold alongside that number: it counts occupations, not people. Every occupation here is weighted equally, whether it employs a few thousand people or a million. An analysis weighted by employment can land somewhere quite different — the Future Skills Centre, working from 13 million job postings and weighting by workers, found a far more even split between work AI complements and work it can automate. We hold no per-occupation employment counts, so we cannot weight ours, and the honest reading is that these two answers are measuring related but different things.
Exposure measured; the substituted/amplified split is Goldman Sachs's classification, not ours.
The displacement that exists is not where the headlines put it
Grouped by domain, substitution is not spread evenly and it is not concentrated in the fields that dominate coverage. Business, Finance & Legal accounts for the sharpest concentration: 27 of its 29 occupations. Technology, by contrast, has none — despite a mean exposure percentile in the same range.
| Domain | Roles | Substituted | Mean exposure |
|---|---|---|---|
| Marketing & Sales | 10 | 880% | 85th |
| Business, Finance & Legal | 29 | 2793% | 69th |
| Technology | 43 | 00% | 67th |
| Science & Education | 43 | 00% | 67th |
| Creative & Media | 27 | 00% | 65th |
| Management & Ops | 50 | 1224% | 54th |
| Trades & Healthcare | 140 | 00% | 29th |
Exposure does not track how much training a job requires
Canada classifies occupations by TEER — the training, education, experience and responsibility a role typically demands. If AI exposure were a story about low-skilled work, displacement would fall as TEER rises. It does not. It sits between 9% and 13% across every level but one, and spikes at TEER 4 — 41% — before falling again.
| TEER | Typically requires | Roles | Substituted | Mean exposure |
|---|---|---|---|---|
| 0 | Management | 32 | 13%4 roles | 55th |
| 1 | University degree | 113 | 12%13 roles | 64th |
| 2 | College / apprenticeship, 2+ yrs | 106 | 11%12 roles | 41th |
| 3 | College / apprenticeship, under 2 yrs | 46 | 13%6 roles | 34th |
| 4 | Secondary school | 27 | 41%11 roles | 57th |
| 5 | Short-term demonstration | 11 | 9%1 roles | 31th |
TEER 4 covers 27 occupations here — a small group, and the figure should be read with that in mind.
Method, and what this does not show
- Exposure is measured, not forecast.It comes from Microsoft Research's analysis of 200,000 Copilot conversations against O*NET work activities, and from the Anthropic Economic Index's open per-occupation usage data. 336 of 342 occupations carry a measured composite; the rest say so rather than showing a number we invented.
- The Canadian outlook is ESDC's, verbatim. COPS 2024–2033, per NOC 2021 unit group, for 327 of 342 occupations. National only — the source publishes no provincial split, so neither do we.
- The occupations are US SOC codes mapped to NOC 2021through Statistics Canada's published correspondence tables. 177 resolved to a single unit group; 158 had several candidates and took the closest by title.
- Substituted versus amplified is Goldman Sachs's call, not a measurement of ours.
- Every figure counts occupations, not workers. All 342 are weighted equally regardless of how many people they employ, so no percentage here describes a share of the workforce. Weighting by employment would need per-occupation headcounts we do not hold — the Statistics Canada Labour Force Survey is the source that would supply them — and it could move the balance substantially, since the occupations classed as substituted are not necessarily the small ones.
- No salary data. We hold none we can stand behind across all 342 occupations, so we publish none.
Full method and sources · The Canadian picture · All 342 occupations
Citing this
Free to quote and link with attribution. Underlying figures are computed from the occupation dataset at build time, so the numbers on this page and on each occupation page cannot disagree.
Fractional Manager (2026). Canada is short of the workers AI reaches furthest into. https://fractionalmanager.org/research/canada-labour-shortage-ai-exposure