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

Cartographer

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
Sources, OSM, satellite and generalisation.
Estimate

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

18%52%+9.4
Symbology, type and visual hierarchy.
Estimate

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

16%48%+7.7
Projections, scales and getting the heights right.
Estimate

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

16%28%+4.5
Review with the client, the field and the lie in the data.
Estimate

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

18%18%+3.2
Print maps, web maps and updates.
Estimate

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

16%22%+3.5
Credit, metadata and owning the map.
Estimate

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

16%14%+2.2
Total100%31%

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

Skills and activities

  • make maps
  • generalise cartography
  • symbology
  • choose projection
  • visual hierarchy
  • update OSM
  • web maps
  • maps that do not lie

Sources for this job· O*NET 17-1021.00· ESCO/ISCO 2165

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