Payroll and Timekeeping Clerks
Office & Administrative SupportAI exposure
- Data source: BLSPublished: 2026-08
Very high· relative
LowFour relative bandsVery highGroup-level value
Scale, basis and source
Four relative bands (Low / Moderate / High / Very high)
831 detailed occupations in the BLS Employment Projections table. Assigned per National Employment Matrix (NEM) code, so occupations sharing a NEM code carry the same band
- Data source: AnthropicPublished: 2026-03
0.049
0.000Range of values carried here0.745Scale, basis and source
- Data source: ILOPublished: 2025
0.61
0.09Range of values carried here0.70Group-level value
Scale, basis and source
Generative AI exposure index, 0–1 as published
ISCO-08 unit group — every occupation sharing the code gets this value
Computed by this site, not published by the ILO: of the 1,012 occupations this site links to the ILO dataset, 3% score at or above this value.
What kind of figure this source publishes
The BLS category is a relative rank, not an absolute level, and it is not a first-hand measurement: it groups an occupation's percentile ranks across several published studies into four bands. It is not an employment or wage forecast, not a probability of adoption, and it does not separate automation from augmentation.
Task-level exposure
Show 15 hidden tasks| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Compute wages and deductions, and enter data into computers.43-3051 | 0.005075.0 | —0 |
Review time sheets, work charts, wage computation, and other information to detect and reconcile payroll discrepancies.43-3051 | 0.001725.0 | —0 |
Provide information to employees and managers on payroll matters, tax issues, benefit plans, and collective agreement provisions. | —0 | 0.002552.2 |
Keep track of leave time, such as vacation, personal, and sick leave, for employees. | —0 | 0.002347.8 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Compute wages and deductions, and enter data into computers.O*NET Task ID 2516 | 1.0 |
Compile employee time, production, and payroll data from time sheets and other records.O*NET Task ID 2517 | 1.0 |
Review time sheets, work charts, wage computation, and other information to detect and reconcile payroll discrepancies.O*NET Task ID 2518 | 1.0 |
Verify attendance, hours worked, and pay adjustments, and post information onto designated records.O*NET Task ID 2519 | 1.0 |
Record employee information, such as exemptions, transfers, and resignations, to maintain and update payroll records.O*NET Task ID 2520 | 1.0 |
Issue and record adjustments to pay related to previous errors or retroactive increases.O*NET Task ID 2521 | 1.0 |
Complete time sheets showing employees' arrival and departure times.O*NET Task ID 2524 | 1.0 |
Post relevant work hours to client files to bill clients properly.O*NET Task ID 2525 | 1.0 |
Distribute and collect timecards each pay period.O*NET Task ID 2526 | 1.0 |
Complete, verify, and process forms and documentation for administration of benefits, such as pension plans, and unemployment and medical insurance.O*NET Task ID 2527 | 1.0 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page