Automation index
How much of a Export salesperson’s job can AI already do?
An Incoterm comes earlier. Closing the order so it actually crosses do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Export salesperson
27%
AI can already do 27% of this job.
#142 / 623
View rankingExport salesperson
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 |
|---|---|---|---|
Book, trade fair and the lead.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Quote, Incoterm and the one who wants cheap CIF.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Order, credit and the one who does not pay.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Customs, the delay and not promising the date.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The agent, the territory and not stepping on each other.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Close it, and that it crosses.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- trade fairs
- Incoterms
- export book
- export credit
- agents
- territories
- CIF
- export orders
Sources for this job· O*NET 41-4012.00· ESCO/ISCO 2434
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