Automation index
How much of a Intercultural mediator’s job can AI already do?
A translated leaflet comes earlier. Standing between the form and the person who does not get the system do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Intercultural mediator
24%
AI can already do 24% of this job.
#272 / 633
View rankingIntercultural mediator
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 |
|---|---|---|---|
Translate forms, appointments and what the paper says.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Go with them to the desk, the doctor and the school.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
Mediate the cultural clash and the misunderstanding.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Reports, networks and reception resources.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
The family, the child and what you cannot impose.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Trust, and not speaking over anyone.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- intercultural mediation
- accompany paperwork
- translate procedures
- cultural misunderstandings
- reception
- medical appointments
- school and family
- immigration resources
Sources for this job· O*NET 21-1012.00· ESCO/ISCO 2635
IS WORK OVER?
ML engineer
29%
#64 / 633
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