Material moving machine operators: AI exposure and career outlook
Material moving machine operators (SOC 53-7000) sit at the 9th 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 6% of tasks are already automated and 16% are being reshaped rather than replaced — both modelled figures, not direct measurements. Of the Claude usage observed in this occupation, 81% follows an automation pattern and 19% 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 72500 (Crane 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
- 53-7000
- Canada NOC 2021
- 72500
- TEER level
- Not available
- 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 | 6% | Measured — Microsoft Research, from 200,000 Copilot conversations classified against O*NET work activities. Occupation-group average of 15 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 15 detailed roles. |
| Academic AI exposure | 13th percentile | Measured — Felten-Raj-Seamans AIOE index across 774 occupations; the index underlying Statistics Canada's Canadian estimates. Occupation-group average of 17 detailed roles. |
| Automation-pattern usage | 81% | 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 6 detailed roles. |
| Augmentation-pattern usage | 19% | 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) | 6% | 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) | 16% | 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 72500
Material moving machine operators map to NOC 72500 — Crane operators. 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 (closest match of several correspondence candidates). TEER is the second digit of the NOC code, so it is only as certain as the code is. Several correspondence candidates were available here and we took the closest by title, so we do not report a TEER level for it.
What this job actually involves
These are the O*NET task statements for Material moving machine operators— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 237, 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 6% of this occupation's work activities are covered by observed AI usage, but not which ones.
SOC 53-7000 is a BLS occupation group. These statements are pooled across the 15 detailed occupations in it, so a given task may apply to only some of them.
Inspecting Equipment, Structures, or Materials · 3 tasks
Inspect crane site conditions to determine ground stability.
Core task · Importance 4.8/5 (O*NET) · reported by 1 of 15 occupations in this group
Inspect and adjust crane mechanisms or lifting accessories to prevent malfunctions or damage.
Core task · Importance 4.6/5 (O*NET) · reported by 1 of 15 occupations in this group
Inspect trucks prior to beginning routes to ensure safe operating condition.
Core task · Importance 4.5/5 (O*NET) · reported by 1 of 15 occupations in this group
Controlling Machines and Processes · 3 tasks
Move levers to position dredges for excavation, to engage hydraulic pumps, to raise and lower suction booms, and to control rotation of cutterheads.
Core task · Importance 4.8/5 (O*NET) · reported by 1 of 15 occupations in this group
Move levers, depress foot pedals, or turn dials to operate cranes, cherry pickers, electromagnets, or other moving equipment for lifting, moving, or placing loads.
Core task · Importance 4.8/5 (O*NET) · reported by 1 of 15 occupations in this group
Move levers or controls that operate lifting devices, such as forklifts, lift beams with swivel-hooks, hoists, or elevating platforms, to load, unload, transport, or stack material.
Core task · Importance 4.6/5 (O*NET) · reported by 1 of 15 occupations in this group
Monitoring Processes, Materials, or Surroundings · 1 task
Monitor pumps and flow lines for gas and fluid leaks.
Core task · Importance 4.6/5 (O*NET) · reported by 1 of 15 occupations in this group
Estimating the Quantifiable Characteristics of Products, Events, or Information · 1 task
Determine load weights and check them against lifting capacities to prevent overload.
Core task · Importance 4.5/5 (O*NET) · reported by 1 of 15 occupations in this group
Processing Information · 1 task
Verify tank car, barge, or truck load numbers to ensure car placement accuracy based on written or verbal instructions.
Core task · Importance 4.6/5 (O*NET) · reported by 1 of 15 occupations in this group
Handling and Moving Objects · 1 task
Seal outlet valves on tank cars, barges, and trucks.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 15 occupations in this group
Operating Vehicles, Mechanized Devices, or Equipment · 1 task
Move controls to drive gasoline- or electric-powered trucks, cars, or tractors and transport materials between loading, processing, and storage areas.
Core task · Importance 4.5/5 (O*NET) · reported by 1 of 15 occupations in this group
Guiding, Directing, and Motivating Subordinates · 1 task
Direct helpers engaged in placing blocking or outrigging under cranes.
Core task · Importance 4.6/5 (O*NET) · reported by 1 of 15 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 Material moving machine 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.
Master workflow automation platforms (e.g., Make, Zapier, CRM integrations) to double your output and position for fractional leadership roles.
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 material moving machine operators.
Sources for this occupation
Only the sources that hold data for material moving machine 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
Other roles in Management & Ops.
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