Construction equipment operators: AI exposure and career outlook
Construction equipment operators (SOC 47-2070) sit at the 3rd 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 3% of tasks are already automated and 11% are being reshaped rather than replaced — both modelled figures, not direct measurements. Of the Claude usage observed in this occupation, 48% follows an automation pattern and 52% an augmentation pattern, which places it in the 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 73400 (Heavy equipment operators), 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
- 47-2070
- Canada NOC 2021
- 73400
- TEER level
- 3 — 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 | 3% | Measured — Microsoft Research, from 200,000 Copilot conversations classified against O*NET work activities. Occupation-group average of 3 detailed roles. |
| Observed AI usage | 0% | Measured — Anthropic Economic Index, share of tasks observed being performed with Claude (CC-BY 4.0). Occupation-group average of 3 detailed roles. |
| Academic AI exposure | 16th percentile | Measured — Felten-Raj-Seamans AIOE index across 774 occupations; the index underlying Statistics Canada's Canadian estimates. Occupation-group average of 3 detailed roles. |
| Automation-pattern usage | 48% | Measured — the share of observed Claude usage in this occupation where the task is handed over rather than iterated on (Anthropic Economic Index). Occupation-group average of 1 detailed roles. |
| Augmentation-pattern usage | 52% | Measured — the complement of the row above; the two sum to 100% by construction. This is the pattern where the person stays in the loop. |
| Estimated task automation (modelled) | 3% | 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) | 11% | 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 73400
Construction equipment operators map to NOC 73400 — Heavy equipment operators at TEER 3 (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 Construction equipment operators— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 51, 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 3% of this occupation's work activities are covered by observed AI usage, but not which ones.
SOC 47-2070 is a BLS occupation group. These statements are pooled across the 3 detailed occupations in it, so a given task may apply to only some of them.
Handling and Moving Objects · 3 tasks
Take actions to avoid potential hazards or obstructions, such as utility lines, other equipment, other workers, or falling objects.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 3 occupations in this group
Drive pilings to provide support for buildings or other structures, using heavy equipment with a pile driver head.
Core task · Importance 4.5/5 (O*NET) · reported by 1 of 3 occupations in this group
Align machines, cutterheads, or depth gauge makers with reference stakes and guidelines or ground or position equipment, following hand signals of other workers.
Core task · Importance 4.5/5 (O*NET) · reported by 1 of 3 occupations in this group
Controlling Machines and Processes · 3 tasks
Move hand and foot levers of hoisting equipment to position piling leads, hoist piling into leads, and position hammers over pilings.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 3 occupations in this group
Start engines, move throttles, switches, or levers, or depress pedals to operate machines, such as bulldozers, trench excavators, road graders, or backhoes.
Core task · Importance 4.6/5 (O*NET) · reported by 1 of 3 occupations in this group
Move levers and turn valves to activate power hammers, or to raise and lower drophammers that drive piles to required depths.
Core task · Importance 4.6/5 (O*NET) · reported by 1 of 3 occupations in this group
Inspecting Equipment, Structures, or Materials · 2 tasks
Locate underground services, such as pipes or wires, prior to beginning work.
Core task · Importance 4.5/5 (O*NET) · reported by 1 of 3 occupations in this group
Conduct pre-operational checks on equipment to ensure proper functioning.
Core task · Importance 4.5/5 (O*NET) · reported by 1 of 3 occupations in this group
Updating and Using Relevant Knowledge · 1 task
Learn and follow safety regulations.
Core task · Importance 4.9/5 (O*NET) · reported by 1 of 3 occupations in this group
Performing General Physical Activities · 1 task
Coordinate machine actions with other activities, positioning or moving loads in response to hand or audio signals from crew members.
Core task · Importance 4.5/5 (O*NET) · reported by 1 of 3 occupations in this group
Repairing and Maintaining Electronic Equipment · 1 task
Repair and maintain equipment, making emergency adjustments or assisting with major repairs as necessary.
Core task · Importance 4.4/5 (O*NET) · reported by 1 of 3 occupations in this group
Coordinating the Work and Activities of Others · 1 task
Signal operators to guide movement of tractor-drawn machines.
Core task · Importance 4.4/5 (O*NET) · reported by 1 of 3 occupations in this group
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 Construction equipment operators 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 construction equipment operators 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 construction equipment operators.
Sources for this occupation
Only the sources that hold data for construction equipment operators 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.
- Anthropic Economic Index — collaboration split (release 2026-06-26, CC-BY 4.0): Measured share of observed Claude usage following an automation rather than an augmentation pattern. This is the second axis of the grouping above.
- 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
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This is the picture for the role. What about your skills?
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