Automation index
How much of a Urban planner’s job can AI already do?
A plan and a written report come faster. Getting a neighbourhood to accept it, and a council to vote it, is still politics.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Urban planner
24%
AI can already do 24% of this job.
#285 / 633
View rankingUrban planner
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 |
|---|---|---|---|
Draft reports, sheets and a first zoning plan.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 40% | +8.8 |
Analyse population, mobility and land data.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 35% | +6.3 |
Review objections and legal compatibility.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Tables with residents, councillors and city staff.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Field visits and reading the site.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Political negotiation and defending the plan.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 8% | +1 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Analyse population, mobility and land data
- Review objections and legal compatibility
- Field visits and reading the site
- urbanista
- urban planner
- planeamiento
- ordenacion del territorio
Sources for this job· O*NET 19-3051.00· ESCO/ISCO 2164
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