Automation index
How much of a Boilermaker’s job can AI already do?
A drawing and a plate cut come earlier. Marking out, welding and holding pressure do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Boilermaker
25%
AI can already do 25% of this job.
#216 / 623
View rankingBoilermaker
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 drawings, parts lists and material lists.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Mark out, cut, roll and assemble plate.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Weld, fit and erect in shop or on site.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 22% | +4 |
Leak, pressure and finishing tests.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Repair boilers, tanks and ducts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Thickness, codes and liability for the weld.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- mark out plate
- weld boilers
- roll plate
- assemble tanks
- pressure tests
- erect on site
- read boilermaker drawings
- repair ducts
Sources for this job· O*NET 47-2011.00· ESCO/ISCO 7213
IS WORK OVER?
ML engineer
29%
#64 / 623
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