Automation index
How much of a Panel beater’s job can AI already do?
A quote and a part come earlier. Pulling a wing so it does not show does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Panel beater
18%
AI can already do 18% of this job.
#569 / 643
View rankingPanel beater
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 |
|---|---|---|---|
Assess, order parts and quote.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Pull, weld and set gaps.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Fill, sand and prep for paint.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
Client, insurer and deadlines.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Shop, benches and tools.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Not delivering a car that looks bent.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
- Assess, order parts and quote
- Pull, weld and set gaps
- Fill, sand and prep for paint
- Client, insurer and deadlines
- Shop, benches and tools
- Not delivering a car that looks bent
- chapista
- panel beater
- chapa y pintura
Sources for this job· O*NET 49-3021.00· ESCO/ISCO 7213
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