Automation index
How much of a Air traffic controller’s job can AI already do?
Labels and alerts already help. Separating two aircraft in fog, and talking to both, is not something you hand off.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Air traffic controller
17%
AI can already do 17% of this job.
#598 / 643
View rankingAir traffic controller
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 |
|---|---|---|---|
Plan sequences and read traffic forecasts.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 32% | +5.8 |
Strips, tickets and inter-unit coordination.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Tactical control of aircraft on frequency.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 6% | +1.3 |
Resolve conflicts, radio failures and weather.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 10% | +1.6 |
Train and supervise a controller under instruction.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
Reports, incidents and shift fatigue.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 15% | +2.1 |
| Total | 100% | 17% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Plan sequences and read traffic forecasts
- Tactical control of aircraft on frequency
- Reports, incidents and shift fatigue
- controlador aereo
- air traffic
- atc
- torre de control
Sources for this job· O*NET 53-2021.00· ESCO/ISCO 3154
IS WORK OVER?
ML engineer
29%
#64 / 643
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