Automation index
How much of a Financial advisor’s job can AI already do?
A risk profile and a model portfolio come earlier. Sitting with the client and signing what actually fits do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Financial advisor
30%
AI can already do 30% of this job.
#46 / 623
View rankingFinancial advisor
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 risk, goals and loss capacity.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Propose portfolios, funds and scenarios.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 52% | +8.3 |
Client meeting, objections and the yes or no.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Periodic reports, rebalancing and basic tax.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
MiFID rules, suitability and trace of the advice.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Sign-off, follow-up and liability for the advice.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 30% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- advise on investments
- profile risk
- build portfolios
- rebalance funds
- plan retirement
- MiFID suitability
- explain financial products
- own the advice
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