IS WORK OVER?

Automation index

How much of a Aid worker’s job can AI already do?

A logframe comes earlier. Being there when it goes wrong, and doing no harm, do not.

This percentage does not predict how many people will lose their jobs.

Task comparison

Aid worker

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
Forms, logframe and the indicators.
Estimate

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

16%44%+7
The field, local partners and what the spreadsheet misses.
Estimate

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

18%14%+2.5
Donor reports and the paperwork.
Estimate

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

16%32%+5.1
Security, evacuation and the call to stay.
Estimate

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

16%22%+3.5
Community, conflict and not replacing the local.
Estimate

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

18%18%+3.2
Do no harm and owning the project.
Estimate

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

16%12%+1.9
Total100%23%

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

Skills and activities

  • logframe
  • field work
  • justify projects
  • local partners
  • mission security
  • aid indicators
  • donor reports
  • do no harm

Sources for this job· O*NET 21-1093.00· ESCO/ISCO 2635

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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