Automation index
How much of a Aircraft mechanic’s job can AI already do?
A manual and a maintenance log come earlier. Signing an aircraft so it flies whole do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Aircraft mechanic
23%
AI can already do 23% of this job.
#303 / 633
View rankingAircraft mechanic
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 |
|---|---|---|---|
Read the manual, bulletins and the work order.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Inspect, strip, adjust and refit systems.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 12% | +2.6 |
Diagnosis, parts and functional tests.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Logs, CRS and traceability of the job.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Hangar, line and coordination with crew and ops.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Licence, stamp and airworthiness liability.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 10% | +1.4 |
| Total | 100% | 23% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- maintain aircraft
- inspect airframes
- replace components
- sign CRS
- aircraft diagnosis
- functional tests
- read service bulletins
- airworthiness
Sources for this job· O*NET 49-3011.00· ESCO/ISCO 7232
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