IS WORK OVER?

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

Risk 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.

TaskShareAIPoints
Maps, models and risk reporting.
Estimate

No public source provides a precise figure. This is our estimate.

22%52%+11.4
Policies, limits and stress tests.
Estimate

No public source provides a precise figure. This is our estimate.

18%45%+8.1
Escalate an incident and talk to desks.
Estimate

No public source provides a precise figure. This is our estimate.

16%18%+2.9
Committee, exceptions and negotiation.
Estimate

No public source provides a precise figure. This is our estimate.

16%15%+2.4
Data quality and model assumptions.
Estimate

No public source provides a precise figure. This is our estimate.

14%16%+2.2
Stop the risk and sign the opinion.
Estimate

No public source provides a precise figure. This is our estimate.

14%14%+2
Total100%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

Last-mile 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.

TaskShareAIPoints
Zones, capacity and route cuts.
Estimate

No public source provides a precise figure. This is our estimate.

16%44%+7
KPIs, first attempt and incidents.
Estimate

No public source provides a precise figure. This is our estimate.

16%36%+5.8
The courier, the lobby and the one who will not open.
Estimate

No public source provides a precise figure. This is our estimate.

18%16%+2.9
Peaks, rain and hiring too many or too few.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The angry customer and not dressing delivered.
Estimate

No public source provides a precise figure. This is our estimate.

18%18%+3.2
Cover the peak, and not leaving the neighbourhood orphaned.
Estimate

No public source provides a precise figure. This is our estimate.

16%14%+2.2
Total100%26%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • delivery zones
  • first attempt
  • delivery KPIs
  • last-mile peaks
  • lobbies
  • delivery incidents
  • courier capacity
  • cut routes

Sources for this job· O*NET 11-3071.00· ESCO/ISCO 1324

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