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

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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