Automation index
How much of a Façade technician’s job can AI already do?
A panel list comes earlier. Hanging the façade without letting water in do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Façade technician
21%
AI can already do 21% of this job.
#418 / 633
View rankingFaçade 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 |
|---|---|---|---|
Set-out, anchors and the panel list.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Hang, adjust and the panel that will not close.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
Joints, water and the weathertightness test.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Scaffold, cradle and façade wind.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The architect, the sample and the shade that misses.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
No water in, and not forcing the anchor.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 21% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- hang façades
- anchors
- panel lists
- weathertightness
- cradle
- façade joints
- colour samples
- anchor set-out
Sources for this job· O*NET 47-2081.00· ESCO/ISCO 7123
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