Automation index
How much of a Risk manager’s job can AI already do?
A risk map and a VaR come earlier. Stopping a product and convincing the committee do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Risk manager
29%
AI can already do 29% of this job.
#60 / 623
View rankingRisk 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 |
|---|---|---|---|
Maps, models and risk reporting.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 52% | +11.4 |
Policies, limits and stress tests.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 45% | +8.1 |
Escalate an incident and talk to desks.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Committee, exceptions and negotiation.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Data quality and model assumptions.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
Stop the risk and sign the opinion.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Maps, models and risk reporting
- Policies, limits and stress tests
- Escalate an incident and talk to desks
- Committee, exceptions and negotiation
- Data quality and model assumptions
- Stop the risk and sign the opinion
- risk manager
- gestor de riesgos
- riesgo operacional
Sources for this job· O*NET 13-2099.02· ESCO/ISCO 2413
IS WORK OVER?
Data architect
28%
#83 / 623
View rankingData architect
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 |
|---|---|---|---|
Models, lineage and the catalogue.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Pipelines, quality and the data contract.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The analyst who finds nothing and the mess.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Privacy, retention and the field you cannot keep.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The business, shadow IT and not building another lake.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it gets used, and not letting raw data rot.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- model data
- lineage
- data catalogue
- data contracts
- pipeline quality
- retention
- data lakes
- privacy
Sources for this job· O*NET 15-1243.00· ESCO/ISCO 2511