Automation index
How much of a Surveyor’s job can AI already do?
A digital set-out and a drawing come earlier. Driving stakes in the wind, and the building lining up, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Surveyor
24%
AI can already do 24% of this job.
#284 / 633
View rankingSurveyor
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 |
|---|---|---|---|
Process stations, GNSS and drawings.EstimateNo public source provides a precise figure. This is our estimate. | 20% | 45% | +9 |
Reports, volumes and deliverables.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 35% | +5.6 |
Measure in the field, stake and set out.EstimateNo public source provides a precise figure. This is our estimate. | 20% | 12% | +2.4 |
Coordinate with works and the site authority.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Read the ground when the model lies.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
Owning a 3 cm error.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Process stations, GNSS and drawings
- Reports, volumes and deliverables
- Measure in the field, stake and set out
- Read the ground when the model lies
- Owning a 3 cm error
- topografo
- surveyor
- aparejador topografia
Sources for this job· O*NET 17-1022.00· ESCO/ISCO 2165
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