Automotive body and glass repairers: AI exposure and career outlook
Automotive body and glass repairers (SOC 49-3021) sit at the 16th percentile for measured AI exposure among the 342 occupations tracked here, measured from a composite of Microsoft Research and Anthropic Economic Index telemetry. An estimated 9% of tasks are already automated and 22% are being reshaped rather than replaced — both modelled figures, not direct measurements. Insulated group: little measured AI exposure so far. No urgency here, though the tools are still worth having in your hands. In Canada the role maps to NOC 72411 (Auto body collision, refinishing and glass technicians and damage repair estimators), and ESDC's COPS 2024–2033 outlook for that unit group is balance.
Figures last updated 2026-06. Every number on this page is labelled measured or modelled; where a source has no coverage for this occupation, it says so rather than showing a zero.
Key facts
- SOC code
- 49-3021
- Canada NOC 2021
- 72411
- TEER level
- 2 — College / Apprenticeship 2+ yrs
- COPS outlook
- Balance
What the measurements actually say
Three independent studies measure AI exposure for this occupation directly. The two percentages below them are ours, modelled from those measurements.
| Metric | Value | Provenance |
|---|---|---|
| AI applicability | 9% | Measured — Microsoft Research, from 200,000 Copilot conversations classified against O*NET work activities. |
| Observed AI usage | 0% | Measured — Anthropic Economic Index, share of tasks observed being performed with Claude (CC-BY 4.0). |
| Academic AI exposure | 13th percentile | Measured — Felten-Raj-Seamans AIOE index across 774 occupations; the index underlying Statistics Canada's Canadian estimates. |
| Automation-pattern usage | Not available | Not covered by Anthropic's collaboration-mode dataset. |
| Augmentation-pattern usage | Not available | Not covered by Anthropic's collaboration-mode dataset. |
| Estimated task automation (modelled) | 9% | Modelled from this occupation's measured telemetry composite, mapped through anchors calibrated to Anthropic's published aggregate findings. Our estimate, not per-occupation telemetry. |
| Estimated task reshaping (modelled) | 22% | Share of tasks where AI acts as co-pilot rather than replacement. Modelled from the measured telemetry composite, calibrated to BCG's published aggregate reshaping rates. Our estimate. |
This skill domain has minimal AI substitution risk. AI is primarily augmenting — not replacing — professionals here.
In Canada: NOC 72411
Automotive body and glass repairers map to NOC 72411 — Auto body collision, refinishing and glass technicians and damage repair estimators at TEER 2 (College / Apprenticeship 2+ yrs). ESDC's COPS 2024–2033 projection for this unit group is Balance.
Mapped via Statistics Canada's official SOC 2018 → NOC 2016 → NOC 2021 correspondence tables. TEER is the second digit of the official NOC code, so it cannot disagree with it.
What this job actually involves
These are the O*NET task statements for Automotive body and glass repairers— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 25, core tasks first.
The exposure figures above are for the occupation as a whole. We do not have a per-task measurement: no published dataset tells us which of these specific tasks AI is doing. Read this as what the job is made of, not as a ranking of what is at risk.
Microsoft's applicability score was itself produced by classifying Copilot conversations against the O*NET work-activity catalogue — the same catalogue these groups come from. The published file gives one number per occupation, not one per activity, so we can tell you that 9% of this occupation's work activities are covered by observed AI usage, but not which ones.
Handling and Moving Objects · 7 tasks
File, grind, sand, and smooth filled or repaired surfaces, using power tools and hand tools.
Core task · Importance 4.5/5 (O*NET)
Fit and weld replacement parts into place, using wrenches and welding equipment, and grind down welds to smooth them, using power grinders and other tools.
Core task · Importance 4.5/5 (O*NET)
Prime and paint repaired surfaces, using paint sprayguns and motorized sanders.
Core task · Importance 4.4/5 (O*NET)
Chain or clamp frames and sections to alignment machines that use hydraulic pressure to align damaged components.
Core task · Importance 4.3/5 (O*NET)
Position dolly blocks against surfaces of dented areas and beat opposite surfaces to remove dents, using hammers.
Core task · Importance 4.2/5 (O*NET)
Fill small dents that cannot be worked out with plastic or solder.
Core task · Importance 4.1/5 (O*NET)
Remove damaged sections of vehicles using metal-cutting guns, air grinders and wrenches, and install replacement parts using wrenches or welding equipment.
Core task · Importance 4.1/5 (O*NET)
Performing General Physical Activities · 2 tasks
Sand body areas to be painted and cover bumpers, windows, and trim with masking tape or paper to protect them from the paint.
Core task · Importance 4.3/5 (O*NET)
Cut and tape plastic separating film to outside repair areas to avoid damaging surrounding surfaces during repair procedure and remove tape and wash surfaces after repairs are complete.
Core task · Importance 4.2/5 (O*NET)
Getting Information · 1 task
Review damage reports, prepare or review repair cost estimates, and plan work to be performed.
Core task · Importance 4.1/5 (O*NET)
Inspecting Equipment, Structures, or Materials · 1 task
Inspect repaired vehicles for proper functioning, completion of work, dimensional accuracy, and overall appearance of paint job, and test-drive vehicles to ensure proper alignment and handling.
Core task · Importance 4.5/5 (O*NET)
Communicating with Supervisors, Peers, or Subordinates · 1 task
Follow supervisors' instructions as to which parts to restore or replace and how much time the job should take.
Core task · Importance 4.3/5 (O*NET)
Task statements and work activities from the O*NET Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under CC BY 4.0. O*NET® is a trademark of USDOL/ETA. USDOL/ETA has not reviewed or approved this material.
That's the average. Is this you?
Everything above describes Automotive body and glass repairers as a whole. Three questions, and nothing is stored or sent.
Where are you in your career?
What does your week mostly look like?
Is your employer deploying AI?
Where the leverage is
With low AI exposure, the focus for this role is on adopting productivity tools. Leverage AI for scheduling, administrative tasks, and analytics to double operational throughput.
Physical requirements and manual dexterity provide strong natural insulation from AI substitution. AI is primarily a peripheral tool for diagnostics.
Obtain technical certifications in advanced diagnostic tools and smart systems relevant to automotive body and glass repairers to prepare for future technological integrations.
This guidance is written per exposure band — three texts across all 342 occupations, framed on the ILO's transformation-versus-displacement distinction. It is editorial guidance for the safe band, not an occupation-specific research finding about automotive body and glass repairers.
Sources for this occupation
Only the sources that hold data for automotive body and glass repairers are listed. A study that does not cover this occupation is not cited here.
- Microsoft Research — Working with AI (Tomlinson et al., arXiv:2507.07935): Measured AI applicability, from 200,000 anonymised Copilot conversations classified against O*NET work activities.
- Anthropic Economic Index: Measured observed AI usage per occupation, from Anthropic's open per-SOC dataset (CC-BY 4.0).
- Felten, Raj & Seamans — AI Occupational Exposure index: Measured academic exposure percentile; the index underlying Statistics Canada's Canadian AI-exposure estimates (Mehdi & Morissette, 2024).
- O*NET Database 31.0 (USDOL/ETA), CC BY 4.0: The occupation's task statements and work activities, verbatim. Joined on SOC 2018 with no crosswalk — O*NET-SOC is built on SOC — and carrying no claim about which tasks AI touches.
- Statistics Canada NOC 2021 concordance: Official SOC 2018 → NOC 2016 V1.3 → NOC 2021 V1.0 correspondence tables. TEER is the second digit of the resolved code, so we report it only where the concordance gave a single candidate.
- ESDC COPS 2024–2033: Projected labour-market assessment per NOC 2021 unit group, from the Canadian Occupational Projection System open dataset.
- BCG — AI Will Reshape More Jobs Than It Replaces (April 3, 2026): Published aggregate reshaping rates, used to calibrate our modelled percentages. The per-occupation figures here are our estimates, not BCG's data.
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This is the picture for the role. What about your skills?
These numbers describe an occupation, not a person. Map your own skills against them, see which pathways stay open, and plan the transition you actually want.