Automation index
How much of a Paver’s job can AI already do?
A fall set-out comes earlier. Leaving a pavement that does not pond do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Paver
19%
AI can already do 19% of this job.
#536 / 643
View rankingPaver
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, levels and layer thickness.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Lay, compact and the edge that opens.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Joints, seals and the gully junction.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Plant, mix temperature and the cold day.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Traffic, the closure and not leaving a step.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
No ponding, and not skimping compaction.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
- lay asphalt
- compact
- falls
- pavement joints
- gullies
- mix temperature
- pavement levels
- seals
Sources for this job· O*NET 47-2071.00· ESCO/ISCO 7112
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