Material recording clerks: AI exposure and career outlook
Material recording clerks (SOC 43-5000) sit at the 60th 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 34% of tasks are already automated and 59% are being reshaped rather than replaced — both modelled figures, not direct measurements. Of the Claude usage observed in this occupation, 69% follows an automation pattern and 31% 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 14110 (Survey interviewers and statistical clerks), 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
- 43-5000
- Canada NOC 2021
- 14110
- 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 | 19% | Measured — Microsoft Research, from 200,000 Copilot conversations classified against O*NET work activities. Occupation-group average of 11 detailed roles. |
| Observed AI usage | 4% | Measured — Anthropic Economic Index, share of tasks observed being performed with Claude (CC-BY 4.0). Occupation-group average of 11 detailed roles. |
| Academic AI exposure | 47th percentile | Measured — Felten-Raj-Seamans AIOE index across 774 occupations; the index underlying Statistics Canada's Canadian estimates. Occupation-group average of 12 detailed roles. |
| Automation-pattern usage | 69% | 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 10 detailed roles. |
| Augmentation-pattern usage | 31% | 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) | 34% | 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) | 59% | 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 14110
Material recording clerks map to NOC 14110 — Survey interviewers and statistical clerks. 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 recording clerks— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 216, 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 19% of this occupation's work activities are covered by observed AI usage, but not which ones.
SOC 43-5000 is a BLS occupation group. These statements are pooled across the 11 detailed occupations in it, so a given task may apply to only some of them.
Getting Information · 1 task
Obtain signed receipts for registered, certified, and insured mail, collect associated charges, and complete any necessary paperwork.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 11 occupations in this group
Scheduling Work and Activities · 1 task
Schedule or dispatch workers, work crews, equipment, or service vehicles to appropriate locations, according to customer requests, specifications, or needs, using radios or telephones.
Core task · Importance 4.8/5 (O*NET) · reported by 1 of 11 occupations in this group
Controlling Machines and Processes · 1 task
Monitor various radio frequencies, such as those used by public works departments, school security, and civil defense, to stay apprised of developing situations.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 11 occupations in this group
Working with Computers · 1 task
Scan labels on letters or parcels to confirm receipt.
Core task · Importance 4.8/5 (O*NET) · reported by 1 of 11 occupations in this group
Documenting/Recording Information · 1 task
Record details of calls, dispatches, and messages.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 11 occupations in this group
Interpreting the Meaning of Information for Others · 1 task
Question callers to determine their locations and the nature of their problems to determine type of response needed.
Core task · Importance 4.9/5 (O*NET) · reported by 1 of 11 occupations in this group
Communicating with Supervisors, Peers, or Subordinates · 1 task
Relay information and messages to and from emergency sites, to law enforcement agencies, and to all other individuals or groups requiring notification.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 11 occupations in this group
Communicating with People Outside the Organization · 1 task
Receive incoming telephone or alarm system calls regarding emergency and non-emergency police and fire service, emergency ambulance service, information, and after-hours calls for departments within a city.
Core task · Importance 4.8/5 (O*NET) · reported by 1 of 11 occupations in this group
Assisting and Caring for Others · 1 task
Provide emergency medical instructions to callers.
Core task · Importance 4.9/5 (O*NET) · reported by 1 of 11 occupations in this group
Coordinating the Work and Activities of Others · 1 task
Prepare daily work and run schedules.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 11 occupations in this group
Guiding, Directing, and Motivating Subordinates · 1 task
Determine response requirements and relative priorities of situations, and dispatch units in accordance with established procedures.
Core task · Importance 4.8/5 (O*NET) · reported by 1 of 11 occupations in this group
Performing Administrative Activities · 1 task
Return to the post office with mail collected from homes, businesses, and public mailboxes.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 11 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 recording clerks 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.
Upskill in AI prompt engineering, model validation, and governance. Focus on transition playbooks to pivot toward high-value advisory services.
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 material recording clerks.
Sources for this occupation
Only the sources that hold data for material recording clerks 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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