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

IS WORK OVER?

27%

AI can already do 27% of this job.

#164 / 633

Hydraulic engineer

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
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
Total	100%		27%

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

IS WORK OVER?

26%

#211 / 633

Last-mile manager

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
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
Total	100%		26%

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