Automation index
How much of a Economist’s job can AI already do?
Pulling a dataset and a first chart is instant. Deciding which model holds, and explaining it to a ministry, is not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Economist
30%
AI can already do 30% of this job.
#49 / 623
View rankingEconomist
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 |
|---|---|---|---|
Clean data, estimate and produce tables.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 52% | +11.4 |
Draft notes, briefs and a first paper.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 45% | +8.1 |
Choose model, assumptions and robustness.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Present to a client, ministry or board.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Debate, peer-review and defend the numbers.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
Read the political context and data quality.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
| Total | 100% | 30% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Clean data, estimate and produce tables
- Draft notes, briefs and a first paper
- Choose model, assumptions and robustness
- Present to a client, ministry or board
- Debate, peer-review and defend the numbers
- economista
- economist
Sources for this job· O*NET 19-3011.00· ESCO/ISCO 2631
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