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AI Automation Index
Will AI Replace File Clerks?
Task-level automation exposure for File Clerks: 19 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
58%
weighted task index
19 of 19 tasks scored
Median Salary
$43,600 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Medium
73,440 people hold this job — size, not growth
Projected 2025–35
-15.8%
employment change
5,600 openings a year · BLS line item
Pay by state
BLS OEWS publishes 51 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$25,700 – $50,660
Highest medians
Oregon $50,660
Hawaii $50,630
Illinois $49,920
Alaska $49,910
California $49,040
Lowest medians
Puerto Rico $25,700
Mississippi $31,260
West Virginia $31,600
Louisiana $32,200
Guam $32,360
State figures come from the 43-4071 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 58%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 26%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 28%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 20%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 20/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
19 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 62%
12 tasks
Medium exposure (≥ 35%) 13%
2 tasks
Low exposure (below 35%) 25%
5 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Constituent Tasks
#20628 · Core · 39% automatable
Perform general office activities, such as typing, answering telephones, operating office machines, processing mail, or securing confidential materials.
text generation — Typing documents is core AI text output
Sort mail. Operate office equipment. Type documents.
#20629 · Core · 82% automatable
Keep records of materials filed or removed, using logbooks or computers and generate computerized reports.
data analysis — Maintain inventory records from provided counts
Maintain inventory records.
#2596 · Core · 85% automatable
Gather materials to be filed from departments or employees.
data analysis — Compiling data or documentation is largely automatable
Compile data or documentation.
#20630 · Core · 79% automatable
Find, retrieve, and make copies of information from files in response to requests and deliver information to authorized users.
information retrieval — Search files and references for info
Search files, databases or reference materials to obtain needed information. Provide information to coworkers.
#2593 · Core · 45% automatable
Add new material to file records or create new records as necessary.
physical manipulation — Filing physical documents requires hands in the world
File documents or records.
#2597 · Core · 85% automatable
Sort or classify information according to guidelines, such as content, purpose, user criteria, or chronological, alphabetical, or numerical order.
data analysis — Compiling data or documentation is largely automatable
Compile data or documentation.
#2599 · Core · 82% automatable
Scan or read incoming materials to determine how and where they should be classified or filed.
text generation — Reading and deriving actions from text is core LLM
Read materials to determine needed actions.
#2605 · Core · 15% automatable
Eliminate outdated or unnecessary materials, destroying them or transferring them to inactive storage, according to file maintenance guidelines or legal requirements.
physical manipulation — Physical filing and storage of records
Store records or related materials.
#2602 · Core · 72% automatable
Answer questions about records or files.
text generation — Providing routine information to coworkers is text-based
Provide information to coworkers.
#2601 · Core · 12% automatable
Assign and record or stamp identification numbers or codes to index materials for filing.
physical manipulation — Attaching tags to containers is physical
Attach identification information to products, items or containers.
#2600 · Core · 10% automatable
Place materials into storage receptacles, such as file cabinets, boxes, bins, or drawers, according to classification and identification information.
physical manipulation — Physically placing and storing items
Store items.
#2603 · Core · 72% automatable
Modify or improve filing systems or implement new filing systems.
text generation — AI drafts analysis procedures; human tailors and approves
Develop data analysis or data management procedures.
#20627 · Supplemental · 85% automatable
Input data, such as file numbers, new or updated information, or document information codes into computer systems to support document and information retrieval.
data analysis — Data entry into systems automatable
Enter information into databases or software programs.
#20631 · Supplemental · 85% automatable
Complete general financial activities, such as processing accounts payable, reviewing invoices, collecting cash payments, or issuing receipts.
data analysis — Transactional data verification is rule-based and reliable
Verify accuracy of financial or transactional data.
#2595 · Supplemental · 60% automatable
Track materials removed from files to ensure that borrowed files are returned.
information retrieval — Tracking via systems is digital; physical reconciliation needs humans
Track goods or materials.
#2604 · Supplemental · 80% automatable
Perform periodic inspections of materials or files to ensure correct placement, legibility, or proper condition.
text generation — Document compliance checking against requirements is automatable
Examine documents to verify adherence to requirements.
#2609 · Supplemental · 20% automatable
Retrieve documents stored in microfilm or microfiche and place them in viewers for reading.
physical manipulation — Physical operation of office machines and jams
Operate office equipment.
#2607 · Supplemental · 20% automatable
Operate mechanized files that rotate to bring needed records to a particular location.
physical manipulation — Physical operation of office machines and jams
Operate office equipment.
#2608 · Supplemental · 72% automatable
Design forms related to filing systems.
text generation — AI drafts analysis procedures; human tailors and approves
Develop data analysis or data management procedures.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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