Automation index
How much of a Airport ramp agent’s job can AI already do?
A load plan comes earlier. Pushing the aircraft, the bags and not getting the trim wrong do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Airport ramp agent
21%
AI can already do 21% of this job.
#404 / 633
View rankingAirport ramp 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 |
|---|---|---|---|
Load plan, LIR and hold allocation.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Load, unload, pushback and ramp assistance.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Bags, ULDs and special cargo.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Coordinate with crew, ops and fuel.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Times, delays and ramp safety.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Trim, papers and liability for the load.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 21% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- load aircraft
- do pushback
- allocate holds
- move bags
- load planning
- ramp assistance
- handle ULDs
- ramp safety
Sources for this job· O*NET 53-6051.00· ESCO/ISCO 9333
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