Automation index
How much of a Firefighter’s job can AI already do?
A fire plan and a report write faster. Walking into a burning flat, cutting and carrying someone out do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Firefighter
12%
AI can already do 12% of this job.
#643 / 643
View rankingFirefighter
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 |
|---|---|---|---|
Logs, intervention plans and classroom training.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Prevention inspections and hydrant checks.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 22% | +3.1 |
Fighting urban or wildland fire.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 4% | +0.9 |
Rescue at height, crash or collapse.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 6% | +1.1 |
Emergency care at the scene.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 8% | +1.3 |
Kit maintenance and station watch.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 10% | +1.4 |
| Total | 100% | 12% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- put out fires
- rescue
- enter a fire
- Prevention inspections and hydrant checks
- Fighting urban or wildland fire
- Rescue at height, crash or collapse
- Emergency care at the scene
- Kit maintenance and station watch
- bombero
- firefighter
Sources for this job· O*NET 33-2011.00· ESCO/ISCO 5411
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