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AI Automation Index
Will AI Replace Payroll and Timekeeping Clerks?
Task-level automation exposure for Payroll and Timekeeping Clerks: 21 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
74%
weighted task index
21 of 21 tasks scored
Median Salary
$58,260 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
High
153,140 people hold this job — size, not growth
Projected 2025–35
-15.9%
employment change
11,400 openings a year · BLS line item
Pay by state
BLS OEWS publishes 54 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$34,200 – $74,670
Highest medians
District of Columbia $74,670
California $65,340
Washington $64,560
Colorado $64,140
Massachusetts $63,670
Lowest medians
Puerto Rico $34,200
Virgin Islands $38,100
Guam $38,550
Mississippi $45,170
Arkansas $46,110
State figures come from the 43-3051 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 74%
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 47%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 45%
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 13/100
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
21 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) 83%
17 tasks
Medium exposure (≥ 35%) 11%
3 tasks
Low exposure (below 35%) 6%
1 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
#2519 · Core · 82% automatable
Verify attendance, hours worked, and pay adjustments, and post information onto designated records.
data analysis — Employee info verification against records is automatable
Verify employee information.
#2515 · Core · 55% automatable
Process and issue employee paychecks and statements of earnings and deductions.
data analysis — Financial execution software-mediated; authorization human
Execute sales or other financial transactions.
#2516 · Core · 87% automatable
Compute wages and deductions, and enter data into computers.
data analysis — Financial calculations fully automatable
Calculate financial data. Enter information into databases or software programs.
#18562 · Core · 83% automatable
Process paperwork for new employees and enter employee information into the payroll system.
data analysis — Data entry into systems automatable
Enter information into databases or software programs. Record personnel information.
#2528 · Core · 80% automatable
Prepare and balance period-end reports, and reconcile issued payrolls to bank statements.
data analysis — Reconciling transaction records is rule-based matching
Reconcile records of sales or other financial transactions. Prepare research or technical reports.
#2518 · Core · 82% automatable
Review time sheets, work charts, wage computation, and other information to detect and reconcile payroll discrepancies.
data analysis — Employee info verification against records is automatable
Verify employee information.
#2526 · Core · 28% automatable
Distribute and collect timecards each pay period.
physical manipulation — Physically distributing materials to people
Distribute materials to employees or customers.
#2520 · Core · 80% automatable
Record employee information, such as exemptions, transfers, and resignations, to maintain and update payroll records.
data analysis — Record personnel information into databases
Record personnel information.
#2521 · Core · 80% automatable
Issue and record adjustments to pay related to previous errors or retroactive increases.
data analysis — Record personnel information into databases
Record personnel information.
#18563 · Core · 80% automatable
Keep track of leave time, such as vacation, personal, and sick leave, for employees.
data analysis — Record personnel information into databases
Record personnel information.
#2517 · Core · 85% automatable
Compile employee time, production, and payroll data from time sheets and other records.
data analysis — Compiling data or documentation is largely automatable
Compile data or documentation.
#2522 · Core · 78% automatable
Keep informed about changes in tax and deduction laws that apply to the payroll process.
information retrieval — AI curates updates; human maintains working knowledge
Maintain current knowledge related to work activities.
#2524 · Core · 80% automatable
Complete time sheets showing employees' arrival and departure times.
data analysis — Record personnel information into databases
Record personnel information.
#2523 · Core · 72% automatable
Provide information to employees and managers on payroll matters, tax issues, benefit plans, and collective agreement provisions.
text generation — Providing routine information to coworkers is text-based
Provide information to coworkers.
#18564 · Core · 82% automatable
Conduct verifications of employment.
data analysis — Employee info verification against records is automatable
Verify employee information.
#23954 · Supplemental · 64% automatable
Prepare and file payroll tax returns.
text generation — Standard financial documents drafted from data
Prepare financial documents. File documents or records.
#2529 · Supplemental · 85% automatable
Compile statistical reports, statements, and summaries related to pay and benefits accounts, and submit them to appropriate departments.
data analysis — Compiling data or documentation is largely automatable
Compile data or documentation.
#20901 · Supplemental · 85% automatable
Balance cash and payroll accounts.
data analysis — Reconciling transaction records is rule-based matching
Reconcile records of sales or other financial transactions.
#2527 · Supplemental · 78% automatable
Complete, verify, and process forms and documentation for administration of benefits, such as pension plans, and unemployment and medical insurance.
data analysis — Detecting recording errors is deterministic pattern matching
Check data for recording errors. Prepare documentation for contracts, transactions, or regulatory compliance.
#20902 · Supplemental · 42% automatable
Train employees on organizations' timekeeping systems.
interpersonal — Operational training requires live human delivery
Train others in operational procedures.
#2530 · Supplemental · 45% automatable
Coordinate special programs, such as United Way campaigns, that involve payroll deductions.
interpersonal — Operational coordination requires live human orchestration
Coordinate operational activities.
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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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