Automation index
How much of a Wealth manager’s job can AI already do?
A model book comes earlier. Sitting with the client without selling them the house product do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Wealth manager
27%
AI can already do 27% of this job.
#156 / 623
View rankingWealth manager
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 |
|---|---|---|---|
Profile, IPS and the book.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Meeting, the fear and the one who wants it all on deposit.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Tax, succession and not leaving a hole.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Report, the market and not dressing the year.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The committee, the product and not pushing the house book.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Sit for real, and not selling the house product.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- IPS
- wealth books
- succession
- wealth tax
- risk profiles
- client reports
- house products
- deposits
Sources for this job· O*NET 13-2052.00· ESCO/ISCO 2412
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