Automation index
How much of a Development cooperation officer’s job can AI already do?
An agreement and a frame come earlier. Aligning with the southern partner and not imposing the project do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Development cooperation officer
26%
AI can already do 26% of this job.
#212 / 623
View rankingDevelopment cooperation officer
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 |
|---|---|---|---|
Agreements, alignment and the SDGs on the card.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 48% | +7.7 |
Follow-up, missions and reports to the agency.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
The local partner, the counterpart and the disagreement.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Budget, currency and what stays in management.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The context that shifts and reworking the approach.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Not imposing, and accounting on both sides.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 26% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- cooperation agreements
- align with SDGs
- agency reports
- southern partners
- monitoring missions
- hard-currency budget
- counterparts
- accountability
Sources for this job· O*NET 19-3094.00· ESCO/ISCO 2635
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