Automation index
How much of a Plasterboard fitter’s job can AI already do?
A setting-out comes earlier. Partition plumb and not hearing the neighbour do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Plasterboard fitter
19%
AI can already do 19% of this job.
#514 / 633
View rankingPlasterboard 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 |
|---|---|---|---|
Setting-out, tracks and the board cut list.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Studs, boards and the screw to the noggin.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Insulation, joints and hearing the neighbour.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Jointing, corners and the painter behind.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Services, pipe runs and not chopping extra.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Plumb, and not leaving the joint proud.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 19% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- fit plasterboard
- set out partitions
- tracks
- jointing
- acoustic insulation
- boards
- corners
- service runs
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