Hazardous materials removal workers: AI exposure and career outlook
Hazardous materials removal workers (SOC 47-4041) sit at the 1st 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 2% of tasks are already automated and 9% 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 75110 (Construction trades helpers and labourers), and ESDC's COPS 2024–2033 outlook for that unit group is moderate risk of shortage.
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
- 47-4041
- Canada NOC 2021
- 75110
- TEER level
- 5 — Short-Term Demonstration
- COPS outlook
- Moderate risk of Shortage
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 | 2% | 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 | 32nd 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) | 2% | 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) | 9% | 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 75110
Hazardous materials removal workers map to NOC 75110 — Construction trades helpers and labourers at TEER 5 (Short-Term Demonstration). ESDC's COPS 2024–2033 projection for this unit group is Moderate risk of Shortage.
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 Hazardous materials removal workers— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 19, 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 2% of this occupation's work activities are covered by observed AI usage, but not which ones.
Handling and Moving Objects · 7 tasks
Remove asbestos or lead from surfaces, using hand or power tools such as scrapers, vacuums, or high-pressure sprayers.
Core task · Importance 4.6/5 (O*NET)
Build containment areas prior to beginning abatement or decontamination work.
Core task · Importance 4.5/5 (O*NET)
Prepare hazardous material for removal or storage.
Core task · Importance 4.3/5 (O*NET)
Comply with prescribed safety procedures or federal laws regulating waste disposal methods.
Core task · Importance 4.3/5 (O*NET)
Operate machines or equipment to remove, package, store, or transport loads of waste materials.
Core task · Importance 4.0/5 (O*NET)
Sort specialized hazardous waste at landfills or disposal centers, following proper disposal procedures.
Core task · Importance 3.8/5 (O*NET)
Remove or limit contamination following emergencies involving hazardous substances.
Supplemental task · Importance 3.8/5 (O*NET)
Performing General Physical Activities · 3 tasks
Clean contaminated equipment or areas for reuse, using detergents or solvents, sandblasters, filter pumps, or steam cleaners.
Core task · Importance 4.1/5 (O*NET)
Clean mold-contaminated sites by removing damaged porous materials or thoroughly cleaning all contaminated nonporous materials.
Core task · Importance 4.0/5 (O*NET)
Load or unload materials into containers or onto trucks, using hoists or forklifts.
Core task · Importance 3.8/5 (O*NET)
Inspecting Equipment, Structures, or Materials · 1 task
Identify asbestos, lead, or other hazardous materials to be removed, using monitoring devices.
Supplemental task · Importance 4.1/5 (O*NET)
Documenting/Recording Information · 1 task
Record numbers of containers stored at disposal sites, specifying amounts or types of equipment or waste disposed.
Core task · Importance 4.1/5 (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 Hazardous materials removal workers 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 hazardous materials removal workers 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 hazardous materials removal workers.
Sources for this occupation
Only the sources that hold data for hazardous materials removal workers 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.
Related occupations
Other roles in Trades & Healthcare.
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.