Automation index
How much of a Boiler technician’s job can AI already do?
A log and a fault code come earlier. Going into a plant room, and not leaving a building without heat, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Boiler technician
20%
AI can already do 20% of this job.
#469 / 633
View rankingBoiler 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 |
|---|---|---|---|
Logs, fault codes and spares.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 35% | +5.6 |
Routes, appointments and permits.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Diagnose, open and repair the boiler.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 12% | +2.6 |
Tune-up, combustion and pressures.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
Clients, porters and plant-room access.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Gas, safety and not leaving the building cold.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 19% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Logs, fault codes and spares
- Routes, appointments and permits
- Diagnose, open and repair the boiler
- Tune-up, combustion and pressures
- Clients, porters and plant-room access
- calderas
- tecnico de calderas
- calefaccion central
Sources for this job· O*NET 49-9021.00· ESCO/ISCO 7126
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