Automation index
How much of a Piling operator’s job can AI already do?
A pile plan comes earlier. Driving to level without cracking the neighbour do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Piling operator
19%
AI can already do 19% of this job.
#521 / 633
View rankingPiling operator
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, level and each pile’s log.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Drive, bore and refusal.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Cage, pour and the tube that rides up.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Vibration, the neighbour and stopping in time.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Plant, access and the one that will not fit.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
To level, and not cracking party walls.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
- drive piles
- bore
- cages
- refusal level
- site vibration
- pour piles
- piling set-out
- recoverable tubes
Sources for this job· O*NET 47-2073.00· ESCO/ISCO 7114
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