Automation index
How much of a Gas fitter’s job can AI already do?
A schematic and a certificate come earlier. No smell of gas when you close up does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Gas fitter
18%
AI can already do 18% of this job.
#577 / 643
View rankingGas fitter
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 |
|---|---|---|---|
Schematics, materials and certificates.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Run, thread and pressure-test.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Appliances, flues and ventilation.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
Client, utility and legalisation.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Faults, leaks and call-outs.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Not leaving a home with a leak.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
- Schematics, materials and certificates
- Run, thread and pressure-test
- Appliances, flues and ventilation
- Client, utility and legalisation
- Faults, leaks and call-outs
- Not leaving a home with a leak
- instalador de gas
- gas fitter
Sources for this job· O*NET 47-2152.00· ESCO/ISCO 7126
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