Automation index
How much of a Civil engineer’s job can AI already do?
Calcs and drawings speed up. Being on site, stamping, and owning ground that is not the model do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Civil engineer
25%
AI can already do 25% of this job.
#231 / 623
View rankingCivil 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 |
|---|---|---|---|
Structural calcs and code checks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 42% | +9.2 |
Drawings, quantities and cost estimate.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 38% | +6.8 |
Geotech and simplified impact reports.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Coordination with contractor, council and suppliers.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Site execution and safety control.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Site authority and responsible signature.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 10% | +1.2 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Structural calcs and code checks
- Drawings, quantities and cost estimate
- Geotech and simplified impact reports
- Site execution and safety control
- Site authority and responsible signature
- ingeniero civil
- civil engineer
- obras publicas
- caminos
Sources for this job· O*NET 17-2051.00· ESCO/ISCO 2142
IS WORK OVER?
ML engineer
29%
#64 / 623
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