Automation index
How much of a Meteorologist’s job can AI already do?
A model and a map come earlier. Saying whether a village should evacuate does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Meteorologist
25%
AI can already do 25% of this job.
#246 / 623
View rankingMeteorologist
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 |
|---|---|---|---|
Models, maps and a first forecast.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Observation, sondes and satellite.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Warnings, civil protection and media.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Shift, uncertainty and not over-scaring.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Verification, archives and research.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Not sitting on a warning that saves someone.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Models, maps and a first forecast
- Observation, sondes and satellite
- Warnings, civil protection and media
- Shift, uncertainty and not over-scaring
- Verification, archives and research
- meteorologo
Sources for this job· O*NET 19-2021.00· ESCO/ISCO 2112
IS WORK OVER?
Data architect
28%
#83 / 623
View rankingData architect
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 |
|---|---|---|---|
Models, lineage and the catalogue.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Pipelines, quality and the data contract.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The analyst who finds nothing and the mess.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Privacy, retention and the field you cannot keep.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The business, shadow IT and not building another lake.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it gets used, and not letting raw data rot.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- model data
- lineage
- data catalogue
- data contracts
- pipeline quality
- retention
- data lakes
- privacy
Sources for this job· O*NET 15-1243.00· ESCO/ISCO 2511