Automation index
How much of a Cabin manager’s job can AI already do?
A briefing comes earlier. Evacuating and the cabin obeying do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Cabin manager
20%
AI can already do 20% of this job.
#467 / 633
View rankingCabin manager
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 |
|---|---|---|---|
Briefing, door assignment and the log.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Cabin, the difficult passenger and turbulence.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Service, allergies and the one who is ill.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Safety, the demo and the kit.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Delay, overbooking and the one who will not sit.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Evacuate for real, and sign the cabin.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 20% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- cabin briefing
- assign doors
- difficult passenger
- safety demo
- onboard allergies
- overbooking
- evacuate
- turbulence
Sources for this job· O*NET 53-2031.00· ESCO/ISCO 5111
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