IS WORK OVER?

Automation index

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

Today an AI can already do 52% of this job. Literary or close legal translation still needs someone willing to stand behind every word.

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

Task comparison

Translator

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
Follow ethical codes that protect the confidentiality of information.
Estimate

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

15%28%+4.2
Translate messages simultaneously or consecutively into specified languages, orally or by using hand signs, maintaining message content, context, and style as much as possible.
Public

WMT 2024

See evidence

14%72%+10.1
Listen to speakers' statements to determine meanings and to prepare translations, using electronic listening systems as necessary.
Public

WMT 2024

See evidence

11%72%+7.9
Identify and resolve conflicts related to the meanings of words, concepts, practices, or behaviors.
Estimate

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

10%28%+2.8
Check translations of technical terms and terminology to ensure that they are accurate and remain consistent throughout translation revisions.
Public

WMT 2024

See evidence

8%72%+5.8
Refer to reference materials, such as dictionaries, lexicons, encyclopedias, and computerized terminology banks, as needed to ensure translation accuracy.
Public

WMT 2024

See evidence

7%72%+5
Compile information on content and context of information to be translated and on intended audience.
Public

WMT 2024

See evidence

7%72%+5
Adapt translations to students' cognitive and grade levels, collaborating with educational team members as necessary.
Public

WMT 2024

See evidence

7%72%+5
Other less frequent tasks (weight estimated from frequency).
Estimate

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

21%28%+5.9
Total100%52%

Modelled workweek: 100% · Public evidence: 54% · Estimate: 46% · Confidence: Medium

Skills and activities

  • translate texts
  • interpret
  • proofread translations
  • information confidentiality
  • spelling
  • office software
  • grammar
  • speak different languages
  • master language rules
  • develop a translation strategy

Sources for this job· O*NET 27-3091.00· ESCO/ISCO 2643

Ice-cream maker

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
Base, overrun and the recipe.
Estimate

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

16%44%+7
Churn, age and the one that crystals.
Estimate

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

18%12%+2.2
List, season and the flavour that misses.
Estimate

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

16%28%+4.5
Workshop, cold and the broken chain.
Estimate

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

16%22%+3.5
Summer, the queue and not running out of vanilla.
Estimate

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

18%18%+3.2
Creamy in the mouth, and not selling ice.
Estimate

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

16%12%+1.9
Total100%22%

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

Skills and activities

  • churn
  • overrun
  • age bases
  • ice-cream list
  • cold chain
  • ice crystals
  • vanilla
  • ice-cream season

Sources for this job· O*NET 51-3099.00· ESCO/ISCO 7512

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