Medical equipment repairers: AI exposure and career outlook
Medical equipment repairers (SOC 49-9062) sit at the 31st 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 16% of tasks are already automated and 36% are being reshaped rather than replaced — both modelled figures, not direct measurements. Of the Claude usage observed in this occupation, 66% follows an automation pattern and 34% an augmentation pattern, which places it in the Displacement risk group: the routine layer of this work is being automated outright. The judgment layer is not, so the move is to own more of it. In Canada the role maps to NOC 22310 (Electrical and electronics engineering technologists and technicians), 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
- 49-9062
- Canada NOC 2021
- 22310
- TEER level
- 2 — College / Apprenticeship 2+ yrs
- 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 | 16% | 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 | 44th percentile | Measured — Felten-Raj-Seamans AIOE index across 774 occupations; the index underlying Statistics Canada's Canadian estimates. |
| Automation-pattern usage | 66% | Measured — the share of observed Claude usage in this occupation where the task is handed over rather than iterated on (Anthropic Economic Index). |
| Augmentation-pattern usage | 34% | 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) | 16% | 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) | 36% | 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. |
AI is transforming how this work is done. Professionals who adapt their workflows will thrive; those who don't face increasing competition.
In Canada: NOC 22310
Medical equipment repairers map to NOC 22310 — Electrical and electronics engineering technologists and technicians at TEER 2 (College / Apprenticeship 2+ yrs). 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 Medical equipment repairers— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 20, 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.
1 of these 12statements 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 16% of this occupation's work activities are covered by observed AI usage, but not which ones.
Inspecting Equipment, Structures, or Materials · 3 tasks
Test or calibrate components or equipment, following manufacturers' manuals and troubleshooting techniques, using hand tools, power tools, or measuring devices.
Core task · Importance 4.6/5 (O*NET)
Inspect, test, or troubleshoot malfunctioning medical or related equipment, following manufacturers' specifications and using test and analysis instruments.
Core task · Importance 4.5/5 (O*NET)
Test, evaluate, and classify excess or in-use medical equipment and determine serviceability, condition, and disposition, in accordance with regulations.
Core task · Importance 4.1/5 (O*NET)
Handling and Moving Objects · 3 tasks
Perform preventive maintenance or service, such as cleaning, lubricating, or adjusting equipment.
Core task · Importance 4.5/5 (O*NET)
Disassemble malfunctioning equipment and remove, repair, or replace defective parts, such as motors, clutches, or transformers.
Core task · Importance 4.4/5 (O*NET)
Install medical equipment.
Core task · Importance 4.1/5 (O*NET)
Getting Information · 2 tasks
Plan and carry out work assignments, using blueprints, schematic drawings, technical manuals, wiring diagrams, or liquid or air flow sheets, following prescribed regulations, directives, or other instructions as required.
Core task · Importance 4.1/5 (O*NET) · 100% of observed AI use on this task is automation-pattern
Study technical manuals or attend training sessions provided by equipment manufacturers to maintain current knowledge.
Core task · Importance 4.0/5 (O*NET)
Monitoring Processes, Materials, or Surroundings · 1 task
Examine medical equipment or facility's structural environment and check for proper use of equipment to protect patients and staff from electrical or mechanical hazards and to ensure compliance with safety regulations.
Core task · Importance 4.2/5 (O*NET)
Documenting/Recording Information · 1 task
Keep records of maintenance, repair, and required updates of equipment.
Core task · Importance 4.4/5 (O*NET)
Training and Teaching Others · 1 task
Explain or demonstrate correct operation or preventive maintenance of medical equipment to personnel.
Core task · Importance 4.0/5 (O*NET)
Monitoring and Controlling Resources · 1 task
Research catalogs or repair part lists to locate sources for repair parts, requisitioning parts and recording their receipt.
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 Medical equipment 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
Upskill in AI-driven automation (e.g., Make, Zapier) and generative tools in your domain. The opportunity is to pivot from execution to directing AI-augmented workflows.
AI is transforming tasks rather than replacing entire jobs. Routine scheduling and communication automate, but human decision-making remains critical.
Obtain technical certifications in advanced diagnostic tools and smart systems relevant to medical equipment 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 reshaping band, not an occupation-specific research finding about medical equipment repairers.
Sources for this occupation
Only the sources that hold data for medical equipment 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.
- 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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