Automation index
How much of a Payroll clerk’s job can AI already do?
A payroll engine comes earlier. Paying right without leaving anyone at zero do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Payroll clerk
27%
AI can already do 27% of this job.
#183 / 623
View rankingPayroll clerk
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Starters, variables and the engine.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Incidents, the missed day and overtime.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Social security, tax and the month’s return.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Query, the payslip and the one who does not get it.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The close, the rush and not paying twice.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Pay right, and not leaving anyone at zero.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- payroll starters
- variables
- income tax
- social security
- payslips
- overtime
- payroll close
- incidents
Sources for this job· O*NET 43-3051.00· ESCO/ISCO 4313
IS WORK OVER?
ML engineer
29%
#64 / 623
View rankingML engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511