Automation index
How much of a Avionics technician’s job can AI already do?
A BITE comes earlier. Calibrating the kit and signing that it flies do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Avionics technician
22%
AI can already do 22% of this job.
#382 / 633
View rankingAvionics technician
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 |
|---|---|---|---|
BITE, manual and the fault tree.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Bench, the pin and the oxidised connector.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
Calibrate, software and the config.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Line, the aircraft on stand and the slot.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The pilot report and not swapping the box blind.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Sign that it flies, and not leaving BITE red.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- BITE
- calibrate avionics
- connectors
- fault tree
- equipment software
- the stand
- LRUs
- config
Sources for this job· O*NET 49-2091.00· ESCO/ISCO 7421
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