Automation index
How much of a Ship agent’s job can AI already do?
An ETA comes earlier. Clearing the ship without extra demurrage do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Ship agent
26%
AI can already do 26% of this job.
#195 / 623
View rankingShip 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 |
|---|---|---|---|
ETA, berth and port services.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 44% | +7 |
Clearance, crew and the crew change.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
The master, the terminal and the window clash.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Invoices, disbursements and the owner’s account.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The inspection, the delay and not losing the slot.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
No extra demurrage, and the ship cleared.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 26% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- ETAs
- berths
- ship clearance
- crew changes
- disbursements
- terminal windows
- owner’s accounts
- port inspections
Sources for this job· O*NET 43-5011.00· 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