IS WORK OVER?

Automation index

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

A speech engine comes earlier. Leaving a faithful text without inventing do not.

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

Task comparison

Transcriber

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
Audio, markers and the shift.
Estimate

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

18%48%+8.6
Transcribe, the one who mumbles and the name.
Estimate

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

16%40%+6.4
Check, timestamps and not inventing.
Estimate

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

16%22%+3.5
Deadline, the urgent and not delivering dirty.
Estimate

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

16%28%+4.5
Confidentiality, the hearing and not leaking.
Estimate

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

18%14%+2.5
Faithful text, and not inventing.
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

  • transcribe audio
  • timestamps
  • proper names
  • mumbles
  • transcription deadlines
  • confidentiality
  • markers
  • review

Sources for this job· O*NET 43-9022.00· ESCO/ISCO 4131

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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