Automation index
How much of a Investment analyst’s job can AI already do?
A model comes earlier. Calling buy or not and signing the memo do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Investment analyst
27%
AI can already do 27% of this job.
#131 / 623
View rankingInvestment analyst
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 |
|---|---|---|---|
Model, comps and the memo.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Data, the gap and what is not in the sheet.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Committee, the thesis and the one who falls in love.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Follow-up, the profit warning and not hiding it.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The PM, the rush and not inflating the target.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Buy or not, and signing the memo.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
- valuation models
- comps
- investment memos
- thesis
- profit warning
- target
- investment committee
- follow-up
Sources for this job· O*NET 13-2051.00· ESCO/ISCO 2413
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