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.

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

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