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AI Automation Index
Will AI Replace Brokerage Clerks?
Task-level automation exposure for Brokerage Clerks: 10 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
63%
weighted task index
10 of 10 tasks scored
Median Salary
$65,750 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Medium
35,940 people hold this job — size, not growth
Projected 2025–35
-7.6%
employment change
3,400 openings a year · BLS line item
Pay by state
BLS OEWS publishes 40 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$48,360 – $95,450
Highest medians
District of Columbia $95,450
New York $78,930
Vermont $78,200
California $77,960
Connecticut $75,330
Lowest medians
Montana $48,360
Kansas $49,990
South Dakota $56,080
Iowa $56,360
Kentucky $57,690
State figures come from the 43-4011 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 63%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 0%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 15%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 43%
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
10 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) 53%
6 tasks
Medium exposure (≥ 35%) 47%
4 tasks
Low exposure (below 35%) 0%
0 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
#8145 · Core · 52% automatable
Correspond with customers and confer with coworkers to answer inquiries, discuss market fluctuations, or resolve account problems.
interpersonal — Drafting responses easy; live de-escalation is human
Confer with coworkers to coordinate work activities. Respond to customer problems or complaints.
#20622 · Core · 68% automatable
Document security transactions, such as purchases, sales, conversions, redemptions, or payments, using computers, accounting ledgers, or certificate records.
text generation — Compliance docs drafted; human owns regulatory accuracy
Prepare documentation for contracts, transactions, or regulatory compliance.
#8149 · Core · 45% automatable
File, type, or operate standard office machines.
physical manipulation — Filing physical documents requires hands in the world
File documents or records.
#20623 · Core · 44% automatable
Perform clerical tasks, such as answering phones or distributing mail.
interpersonal — Call routing automatable; nuanced calls need human
Answer telephones to direct calls or provide information. Distribute incoming mail.
#8148 · Core · 68% automatable
Prepare forms, such as receipts, withdrawal orders, transmittal papers, or transfer confirmations, based on transaction requests from stockholders.
text generation — Compliance docs drafted; human owns regulatory accuracy
Prepare documentation for contracts, transactions, or regulatory compliance.
#8147 · Supplemental · 59% automatable
Schedule and coordinate transfer and delivery of security certificates between companies, departments, and customers.
decision support — Algorithmic scheduling with human oversight of constraints
Schedule operational activities. Coordinate operational activities.
#8150 · Supplemental · 78% automatable
Monitor daily stock prices and compute fluctuations to determine the need for additional collateral to secure loans.
data analysis — Financial data monitoring is digital and AI-analyzable
Monitor financial information.
#8153 · Supplemental · 85% automatable
Verify ownership and transaction information and dividend distribution instructions to ensure conformance with governmental regulations, using stock records and reports.
data analysis — Transactional data verification is rule-based and reliable
Verify accuracy of financial or transactional data.
#8151 · Supplemental · 88% automatable
Compute total holdings, dividends, interest, transfer taxes, brokerage fees, or commissions and allocate appropriate payments to customers.
data analysis — Financial calculations fully automatable
Calculate financial data.
#8152 · Supplemental · 75% automatable
Prepare reports summarizing daily transactions and earnings for individual customer accounts.
text generation — Technical reports drafted; human verifies content
Prepare research or technical reports.
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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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