Automation index
How much of a Wildland firefighter’s job can AI already do?
A fire-front map comes earlier. Going onto the slope with the wind against you do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Wildland firefighter
18%
AI can already do 18% of this job.
#564 / 643
View rankingWildland firefighter
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, map and the day’s attack plan.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Line, hose-lay and the spot fire.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 6% | +1.4 |
Tools, pump and the water that does not arrive.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Night watch, lookout and the flare-up.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Coordinate aircraft and the command post.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 28% | +3.9 |
Get out whole and leave nobody behind.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 10% | +1.4 |
| Total | 100% | 18% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- fight wildfires
- cut line
- lay hose
- night watch
- portable pump
- air assets
- fire-front map
- spot fires
Sources for this job· O*NET 33-2022.00· 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