IS WORK OVER?

Automation index

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

A night of telescope time comes earlier. Reducing the data without selling the find early do not.

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

Task comparison

Astronomer

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
Proposal, time and the instrument.
Estimate

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

18%48%+8.6
Observe, calibrate and the one that clouds over.
Estimate

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

16%40%+6.4
Reduce, the pipeline and the artefact.
Estimate

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

16%22%+3.5
Paper, the referee and not jumping.
Estimate

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

16%28%+4.5
The facility, the queue and not losing the night.
Estimate

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

18%14%+2.5
Reduce, and not selling the find early.
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

  • telescope time
  • calibrate
  • pipelines
  • papers
  • artefacts
  • nights
  • proposals
  • instruments

Sources for this job· O*NET 19-2011.00· ESCO/ISCO 2111

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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