Automation index
How much of a Varnisher’s job can AI already do?
A colour card comes earlier. A finish that does not show the grain the wrong way does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Varnisher
18%
AI can already do 18% of this job.
#563 / 643
View rankingVarnisher
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 |
|---|---|---|---|
Colour cards, ranges and orders.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Sand, stain, apply and dry.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Booth, filters and dust.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
Cabinetmaker, deadlines and touch-up.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Repairs in the home and on site.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Not delivering a piece with orange peel.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
- Colour cards, ranges and orders
- Sand, stain, apply and dry
- Booth, filters and dust
- Cabinetmaker, deadlines and touch-up
- Repairs in the home and on site
- Not delivering a piece with orange peel
- barnizador
- lacador
- varnisher
Sources for this job· O*NET 51-9123.00· ESCO/ISCO 7522
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