IS WORK OVER?

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

Payroll 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.

TaskShareAIPoints
Starters, variables and the engine.
Estimate

No public source provides a precise figure. This is our estimate.

18%48%+8.6
Incidents, the missed day and overtime.
Estimate

No public source provides a precise figure. This is our estimate.

16%40%+6.4
Social security, tax and the month’s return.
Estimate

No 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.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The close, the rush and not paying twice.
Estimate

No public source provides a precise figure. This is our estimate.

18%14%+2.5
Pay right, and not leaving anyone at zero.
Estimate

No public source provides a precise figure. This is our estimate.

16%12%+1.9
Total100%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

Last-mile manager

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.

TaskShareAIPoints
Zones, capacity and route cuts.
Estimate

No public source provides a precise figure. This is our estimate.

16%44%+7
KPIs, first attempt and incidents.
Estimate

No public source provides a precise figure. This is our estimate.

16%36%+5.8
The courier, the lobby and the one who will not open.
Estimate

No public source provides a precise figure. This is our estimate.

18%16%+2.9
Peaks, rain and hiring too many or too few.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The angry customer and not dressing delivered.
Estimate

No public source provides a precise figure. This is our estimate.

18%18%+3.2
Cover the peak, and not leaving the neighbourhood orphaned.
Estimate

No public source provides a precise figure. This is our estimate.

16%14%+2.2
Total100%26%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • delivery zones
  • first attempt
  • delivery KPIs
  • last-mile peaks
  • lobbies
  • delivery incidents
  • courier capacity
  • cut routes

Sources for this job· O*NET 11-3071.00· ESCO/ISCO 1324

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