Automation index
How much of a Stockbroker’s job can AI already do?
An order and a note come earlier. Calling someone when the market is sinking does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Stockbroker
25%
AI can already do 25% of this job.
#219 / 623
View rankingStockbroker
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 |
|---|---|---|---|
Orders, notes and portfolio screens.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Analysis, theses and market notes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Call, persuade and not over-push.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Compliance, KYC and recordings.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Desk, hours and execution issues.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 16% | +2.6 |
Not putting a client into something they do not understand.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
- Orders, notes and portfolio screens
- Analysis, theses and market notes
- Call, persuade and not over-push
- Compliance, KYC and recordings
- Desk, hours and execution issues
- corredor de bolsa
- broker
- agente de bolsa
Sources for this job· O*NET 41-3031.00· ESCO/ISCO 3311
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