Will AI replace Payroll and Timekeeping Clerks?
In theory, AI could do about 84% of the work in Payroll and Timekeeping Clerks. In practice, as of late 2025, almost no one is actually using it that way — yet.
O*NET-SOC 43-3051
How your 15 core tasks split
Top = what GPT-4 judged AI could speed up. Bottom = how much AI was actually used for these tasks (Anthropic's March 2026 report, usage from Aug & Nov 2025). The gap is the real story.
Back in 2023, GPT-4 judged AI could, in theory, assist with a high share of this job's tasks (~84%). By late 2025, real-world AI use had reached about 5% of its task activity (still rare). The gap between that 2023 forecast and today is the real story.
Where this job sits among 738 jobs
Each dot is one of 738 U.S. jobs. Right = AI can do more of it. Up = AI is actually used more.
Read this as a range, not a verdict
The signals here partly disagree — AI's theoretical reach (~84%) and its real-world use (~5%) tell different stories. AI-risk scores also shift a lot by which model does the rating (2.7%–51.5% in one 2026 study), so this is a direction of travel, not a fixed answer.
See all 15 tasks, ratedBased on real task-level AI scores — click to collapse
- Verify attendance, hours worked, and pay adjustments, and post information onto designated records.
- Compute wages and deductions, and enter data into computers.
- Process paperwork for new employees and enter employee information into the payroll system.
- Review time sheets, work charts, wage computation, and other information to detect and reconcile payroll discrepancies.
- Distribute and collect timecards each pay period.
- Record employee information, such as exemptions, transfers, and resignations, to maintain and update payroll records.
- Issue and record adjustments to pay related to previous errors or retroactive increases.
- Keep track of leave time, such as vacation, personal, and sick leave, for employees.
- Compile employee time, production, and payroll data from time sheets and other records.
- Complete time sheets showing employees' arrival and departure times.
- Process and issue employee paychecks and statements of earnings and deductions.
- Prepare and balance period-end reports, and reconcile issued payrolls to bank statements.
- Keep informed about changes in tax and deduction laws that apply to the payroll process.
- Provide information to employees and managers on payroll matters, tax issues, benefit plans, and collective agreement provisions.
- Conduct verifications of employment.
- ⚠️ None — every core task is at least partly within AI's reach. The job won't vanish, but almost all of it changes.
How we measured this — and how fresh it is
AI's theoretical reach data: 2023
From GPTs-are-GPTs (Eloundou et al.), where GPT-4 rated how much of each task an AI tool could meaningfully speed up. This is the most recent open, commercially-usable occupation-level potential dataset — it dates to 2023. Newer multi-model re-runs exist but swing wildly (one 2026 study saw "high-risk" jobs range 2.7%–51.5% by model) and aren't openly licensed, so we show the stable 2023 baseline and pair it with newer real-world data.
Real-world AI use 2026 report
From the Anthropic Economic Index, which observes how real Claude conversations map onto each occupation's tasks. Published in Anthropic's March 2026 labor-market report, based on usage measured in Aug & Nov 2025 (Sonnet 4 / 4.5).
Task list & ratings O*NET 30.3
Tasks come from O*NET 30.3. Each task's "AI can do / speeds up / still on you" tier uses the real task-level exposure scores from GPTs-are-GPTs (E1 / E2 / E0) — not a guess from keywords.
Sources: O*NET 30.3 (CC BY 4.0) · GPTs-are-GPTs (MIT, 2023) · Anthropic Economic Index (CC BY, Aug & Nov 2025). Page compiled June 2026. "O*NET" is a trademark of the U.S. Department of Labor.
This page is for general informational purposes only and is not career, financial, or employment advice. AI exposure reflects research estimates of task overlap, not predictions about any individual's job, employer, or future employment.