Automation index
How much of a Aid worker’s job can AI already do?
A logframe comes earlier. Being there when it goes wrong, and doing no harm, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Aid worker
23%
AI can already do 23% of this job.
#292 / 633
View rankingAid worker
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 |
|---|---|---|---|
Forms, logframe and the indicators.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 44% | +7 |
The field, local partners and what the spreadsheet misses.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Donor reports and the paperwork.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Security, evacuation and the call to stay.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Community, conflict and not replacing the local.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Do no harm and owning the project.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 23% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- logframe
- field work
- justify projects
- local partners
- mission security
- aid indicators
- donor reports
- do no harm
Sources for this job· O*NET 21-1093.00· ESCO/ISCO 2635
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