Automation index
How much of a Flight dispatcher’s job can AI already do?
A flight plan comes earlier. Signing the fuel without leaving the aircraft short do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Flight dispatcher
28%
AI can already do 28% of this job.
#94 / 623
View rankingFlight dispatcher
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, NOTAM, METAR and the fuel.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Slot, alternates and weight.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The crew, the change and the late aircraft.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
MEL, restrictions and the go or no-go.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The storm, the diversion and recalculating in time.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Not leaving it short, and signing the release.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- flight plans
- NOTAM
- METAR
- fuel calc
- slots
- MEL
- alternates
- operational release
Sources for this job· O*NET 53-2022.00· ESCO/ISCO 3154
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