Automation index
How much of a Foreign-trade technician’s job can AI already do?
An Incoterm comes earlier. The order crossing the border without sticking on the entry do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Foreign-trade technician
28%
AI can already do 28% of this job.
#121 / 623
View rankingForeign-trade technician
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 |
|---|---|---|---|
Incoterms, origin and the entry.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Licences, certificates and the missing one.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The client, the bank and the letter of credit.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Duty, VAT and the customs value.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The forwarder, the delay and not promising the date.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it crosses, and not sticking on the entry.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Incoterms
- customs entry
- letters of credit
- certificates of origin
- customs value
- duties
- import licences
- import VAT
Sources for this job· O*NET 13-1199.00· ESCO/ISCO 3331
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