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
IS WORK OVER?
Translator
52%↓
AI can already do 52% of this job.
#1 / 623
View rankingTranslator
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.
| Task | Share | AI | Points |
|---|---|---|---|
Follow ethical codes that protect the confidentiality of information.EstimateNo 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. | 14% | 72% | +10.1 |
| Listen to speakers' statements to determine meanings and to prepare translations, using electronic listening systems as necessary. | 11% | 72% | +7.9 |
Identify and resolve conflicts related to the meanings of words, concepts, practices, or behaviors.EstimateNo 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. | 8% | 72% | +5.8 |
| Refer to reference materials, such as dictionaries, lexicons, encyclopedias, and computerized terminology banks, as needed to ensure translation accuracy. | 7% | 72% | +5 |
| Compile information on content and context of information to be translated and on intended audience. | 7% | 72% | +5 |
| Adapt translations to students' cognitive and grade levels, collaborating with educational team members as necessary. | 7% | 72% | +5 |
Other less frequent tasks (weight estimated from frequency).EstimateNo public source provides a precise figure. This is our estimate. | 21% | 28% | +5.9 |
| Total | 100% | 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
IS WORK OVER?
ML engineer
29%
#64 / 623
View rankingML engineer
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.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511