Automation index
How much of a Customs agent’s job can AI already do?
A declaration and a classification come earlier. Opening a container, and smelling that something is off, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Customs agent
27%
AI can already do 27% of this job.
#128 / 623
View rankingCustoms agent
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 |
|---|---|---|---|
Declarations, tariff class and documents.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Database hits, risk and selectivity.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 40% | +7.2 |
Physical inspection of goods and container.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Interview the operator and write the record.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
Fines, appeals and police coordination.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
Judgment on fraud and sensitive goods.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Declarations, tariff class and documents
- Database hits, risk and selectivity
- Physical inspection of goods and container
- Fines, appeals and police coordination
- Judgment on fraud and sensitive goods
- aduanas
- customs
- despachante de aduanas
Sources for this job· O*NET 13-1041.00· ESCO/ISCO 3351
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