Assemblers and fabricators: AI exposure and career outlook
Assemblers and fabricators (SOC 51-2090) sit at the 27th 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 14% of tasks are already automated and 32% are being reshaped rather than replaced — both modelled figures, not direct measurements. Of the Claude usage observed in this occupation, 46% follows an automation pattern and 54% 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 93200 (Aircraft assemblers and aircraft assembly inspectors), 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
- 51-2090
- Canada NOC 2021
- 93200
- TEER level
- Not available
- COPS outlook
- Balance
What the measurements actually say
Two of three independent studies measure AI exposure for this occupation directly; the other reports no coverage for it. The two percentages below them are ours, modelled from those measurements.
| Metric | Value | Provenance |
|---|---|---|
| AI applicability | 11% | Measured — Microsoft Research, from 200,000 Copilot conversations classified against O*NET work activities. |
| Observed AI usage | Not available | Not covered by Anthropic's occupation-level dataset. |
| Academic AI exposure | 29th 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 | 46% | 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 | 54% | 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) | 14% | 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) | 32% | 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 93200
Assemblers and fabricators map to NOC 93200 — Aircraft assemblers and aircraft assembly inspectors. 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 Assemblers and fabricators— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 11 of 11, 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.
2 of these 11statements do carry one measured signal: Anthropic publishes, per O*NET task, how much of the observed Claude usage on it looked like automation rather than iteration. Those are marked below. It still says how people use AI on that task, not how much of it AI can do — and the unmarked statements carry nothing rather than inheriting the occupation's figure.
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 11% of this occupation's work activities are covered by observed AI usage, but not which ones.
SOC 51-2090 is a BLS occupation group. These statements are pooled across the 1 detailed occupations in it, so a given task may apply to only some of them.
Handling and Moving Objects · 3 tasks
Rotate through all the tasks required in a particular production process.
Core task · Importance 4.2/5 (O*NET)
Provide assistance in the production of wiring assemblies.
Supplemental task · Importance 4.2/5 (O*NET)
Package finished products and prepare them for shipment.
Supplemental task · Importance 3.9/5 (O*NET)
Getting Information · 1 task
Review work orders and blueprints to ensure work is performed according to specifications.
Core task · Importance 4.4/5 (O*NET)
Judging the Qualities of Objects, Services, or People · 1 task
Perform quality checks on products and parts.
Core task · Importance 4.6/5 (O*NET) · 39% of observed AI use on this task is automation-pattern
Organizing, Planning, and Prioritizing Work · 1 task
Determine work assignments and procedures.
Core task · Importance 4.0/5 (O*NET) · 53% of observed AI use on this task is automation-pattern
Performing General Physical Activities · 1 task
Shovel, sweep, or otherwise clean work areas.
Core task · Importance 3.7/5 (O*NET)
Controlling Machines and Processes · 1 task
Operate machinery and heavy equipment, such as forklifts.
Supplemental task · Importance 3.3/5 (O*NET)
Repairing and Maintaining Electronic Equipment · 1 task
Maintain production equipment and machinery.
Supplemental task · Importance 4.2/5 (O*NET)
Documenting/Recording Information · 1 task
Complete production reports to communicate team production level to management.
Supplemental task · Importance 4.1/5 (O*NET)
Training and Teaching Others · 1 task
Supervise assemblers and train employees on job procedures.
Core task · Importance 3.9/5 (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 Assemblers and fabricators 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 assemblers and fabricators 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 assemblers and fabricators.
Sources for this occupation
Only the sources that hold data for assemblers and fabricators 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.
- 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.
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This is the picture for the role. What about your skills?
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