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

Agile coach

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
Boards, metrics and the cadence design.
Estimate

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

16%40%+6.4
The real retro and the elephant in the room.
Estimate

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

18%14%+2.5
Train, unblock and the manager who will not let go.
Estimate

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

16%32%+5.1
Scale, dependencies and fake agile.
Estimate

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

16%28%+4.5
Conflict, power and not selling extra frameworks.
Estimate

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

18%18%+3.2
That they stop faking it, and not inflating velocity.
Estimate

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

16%14%+2.2
Total100%24%

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

Skills and activities

  • retros
  • agile cadence
  • unblock teams
  • flow metrics
  • fake agile
  • agile training
  • dependencies
  • velocity

Sources for this job· O*NET 13-1111.00· ESCO/ISCO 2421

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