IS WORK OVER?

Automation index

How much of a Hydraulic engineer’s job can AI already do?

A floodplain model comes earlier. Sizing the channel so the village does not flood do not.

This percentage does not predict how many people will lose their jobs.

Task comparison

Hydraulic 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.

TaskShareAIPoints
Hydrology, flows and the floodplain model.
Estimate

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

18%48%+8.6
Field, section and what LiDAR misses.
Estimate

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

16%22%+3.5
Drainage works, pumps and the outfall.
Estimate

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

16%36%+5.8
Water permits and the river authority.
Estimate

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

16%28%+4.5
The flood, the village and the yes to the channel.
Estimate

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

18%14%+2.5
That it does not flood, and not trusting the model alone.
Estimate

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

16%12%+1.9
Total100%27%

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

Skills and activities

  • model floodplains
  • flows
  • drainage
  • channel works
  • water permits
  • pumps
  • channel sections
  • river authority

Sources for this job· O*NET 17-2051.00· ESCO/ISCO 2142

Logistics 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.

TaskShareAIPoints
Costs, OTIF and the dashboard.
Estimate

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

18%52%+9.4
Simulate network, warehouses and the what-if.
Estimate

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

16%40%+6.4
Ops, dirty data and going down to the dock.
Estimate

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

16%22%+3.5
Report, the board and the cut that hurts.
Estimate

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

16%28%+4.5
The haulier, the contract and not breaking the service.
Estimate

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

18%14%+2.5
A real saving, and not dressing OTIF.
Estimate

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

16%12%+1.9
Total100%28%

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

Skills and activities

  • logistics costs
  • OTIF
  • simulate network
  • what-if
  • dirty data
  • haulage contracts
  • dashboards
  • network cuts

Sources for this job· O*NET 13-1081.00· ESCO/ISCO 2421

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