Automation index
How much of a Diplomat’s job can AI already do?
A cable and a briefing come earlier. Sitting across from another state, and not over-improvising, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Diplomat
22%
AI can already do 22% of this job.
#329 / 633
View rankingDiplomat
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 |
|---|---|---|---|
Draft cables, notes and briefings.EstimateNo public source provides a precise figure. This is our estimate. | 20% | 42% | +8.4 |
Analyse press, law and positions.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 35% | +5.6 |
Negotiate, represent and keep protocol.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
Report to capital and take instruction.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 10% | +1.6 |
Contact net, receptions and crises.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
Decide the margin and not bind the State.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Draft cables, notes and briefings
- Analyse press, law and positions
- Negotiate, represent and keep protocol
- Report to capital and take instruction
- Contact net, receptions and crises
- Decide the margin and not bind the State
- diplomatico
- diplomat
- embajador
Sources for this job· O*NET 19-3094.00· ESCO/ISCO 1112
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