Archivists, curators, and museum workers: AI exposure and career outlook
Archivists, curators, and museum workers (SOC 25-4010) sit at the 80th 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 52% of tasks are already automated and 72% are being reshaped rather than replaced — both modelled figures, not direct measurements. Of the Claude usage observed in this occupation, 47% follows an automation pattern and 53% an augmentation pattern — close enough to even that the grouping below should be read loosely, which places it in the Orchestrator opportunity group: AI reaches deep into this work and amplifies it. The leverage goes to whoever directs it, and that can be you.
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
- 25-4010
- Canada NOC 2021
- 51102
- TEER level
- 1 — University Degree
- COPS outlook
- Not assessed
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 | 24% | Measured — Microsoft Research, from 200,000 Copilot conversations classified against O*NET work activities. Occupation-group average of 3 detailed roles. |
| Observed AI usage | 18% | 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 | 61st 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 | 47% | 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 3 detailed roles. |
| Augmentation-pattern usage | 53% | 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) | 52% | 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) | 72% | 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 faces significant AI substitution. The opportunity is in pivoting to AI orchestration and higher-order human judgment within the domain.
In Canada: NOC 51102
Archivists, curators, and museum workers map to NOC 51102 — Archivists at TEER 1 (University Degree). ESDC's COPS 2024–2033 projection for this unit group is not assessed.
Mapped via Statistics Canada's official SOC 2018 → NOC 2016 → NOC 2021 correspondence tables (closest match of several correspondence candidates). Several candidates were available, and this one matched the occupation title on every word — no other did — so the TEER level read off its second digit is reported.
What this job actually involves
These are the O*NET task statements for Archivists, curators, and museum workers— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 52, 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 24% of this occupation's work activities are covered by observed AI usage, but not which ones.
SOC 25-4010 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.
Inspecting Equipment, Structures, or Materials · 2 tasks
Determine whether objects need repair and choose the safest and most effective method of repair.
Core task · Importance 4.8/5 (O*NET) · 71% of observed AI use on this task is automation-pattern · reported by 1 of 3 occupations in this group
Study object documentation or conduct standard chemical and physical tests to ascertain the object's age, composition, original appearance, need for treatment or restoration, and appropriate preservation method.
Core task · Importance 4.5/5 (O*NET) · reported by 1 of 3 occupations in this group
Handling and Moving Objects · 2 tasks
Install, arrange, assemble, and prepare artifacts for exhibition, ensuring the artifacts' safety, reporting their status and condition, and identifying and correcting any problems with the set up.
Core task · Importance 4.6/5 (O*NET) · reported by 1 of 3 occupations in this group
Clean objects, such as paper, textiles, wood, metal, glass, rock, pottery, and furniture, using cleansers, solvents, soap solutions, and polishes.
Core task · Importance 4.5/5 (O*NET) · reported by 1 of 3 occupations in this group
Drafting, Laying Out, and Specifying Technical Devices, Parts, and Equipment · 2 tasks
Organize archival records and develop classification systems to facilitate access to archival materials.
Core task · Importance 4.8/5 (O*NET) · reported by 1 of 3 occupations in this group
Establish and administer policy guidelines concerning public access and use of materials.
Core task · Importance 4.6/5 (O*NET) · reported by 1 of 3 occupations in this group
Getting Information · 1 task
Prepare archival records, such as document descriptions, to allow easy access to information.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 3 occupations in this group
Thinking Creatively · 1 task
Create and maintain accessible, retrievable computer archives and databases, incorporating current advances in electronic information storage technology.
Core task · Importance 4.6/5 (O*NET) · reported by 1 of 3 occupations in this group
Updating and Using Relevant Knowledge · 1 task
Specialize in particular materials or types of object, such as documents and books, paintings, decorative arts, textiles, metals, or architectural materials.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 3 occupations in this group
Assisting and Caring for Others · 1 task
Provide reference services and assistance for users needing archival materials.
Core task · Importance 4.8/5 (O*NET) · reported by 1 of 3 occupations in this group
Guiding, Directing, and Motivating Subordinates · 1 task
Direct activities of workers who assist in arranging, cataloguing, exhibiting, and maintaining collections of valuable materials.
Core task · Importance 4.5/5 (O*NET) · reported by 1 of 3 occupations in this group
Providing Consultation and Advice to Others · 1 task
Recommend preservation procedures, such as control of temperature and humidity, to curatorial and building staff.
Core task · Importance 4.7/5 (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 Archivists, curators, and museum workers 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
Transition to high-value advisory, complex case analysis, or AI system oversight. The pivot is to move from routine execution to AI orchestration and quality assurance.
Cognitive, information-based tasks are highly exposed. Entry-level displacement pressure is high, necessitating transition to advisory and governance roles.
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 high risk band, not an occupation-specific research finding about archivists, curators, and museum workers.
Sources for this occupation
Only the sources that hold data for archivists, curators, and museum workers 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.
- 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 Science & Education.
This is the picture for the role. What about your skills?
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