Automation index
How much of a Civil protection volunteer’s job can AI already do?
A protocol and a log come earlier. A flood and getting someone out do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Civil protection volunteer
18%
AI can already do 18% of this job.
#588 / 643
View rankingCivil protection volunteer
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 |
|---|---|---|---|
Protocols, logs and radios.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Deploy, rescue and emergency logistics.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Public, evacuation and calm.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Kit, stores and maintenance.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Shifts, fatigue and 112 coordination.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
Not getting in the firefighters’ way or leaving someone.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 18% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Protocols, logs and radios
- Deploy, rescue and emergency logistics
- Public, evacuation and calm
- Kit, stores and maintenance
- Shifts, fatigue and 112 coordination
- proteccion civil
- civil protection
Sources for this job· O*NET 33-2022.00· ESCO/ISCO 5419
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