Automation index
How much of a Geographer’s job can AI already do?
A map and a layer clip come earlier. Reading the territory and signing off the plan do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Geographer
29%
AI can already do 29% of this job.
#59 / 623
View rankingGeographer
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 |
|---|---|---|---|
GIS, layers, clips and working maps.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Impact reports, demography and land use.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Fieldwork, interviews and checking what the map says.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Planning, objections and work with the council.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Explain, map for non-experts and teaching.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The territorial opinion and owning the report.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- read territory
- run GIS
- impact reports
- land use
- fieldwork
- spatial planning
- working maps
- planning objections
Sources for this job· O*NET 19-3092.00· ESCO/ISCO 2632
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