IS WORK OVER?

Automation index

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

A load curve comes earlier. Cutting the bill without leaving the process in the dark do not.

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

Task comparison

Energy 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
Energy audit, curves and the saving potential.
Estimate

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

18%48%+8.6
Contracts, tariffs and the power market.
Estimate

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

16%36%+5.8
Plant, meters and what the meter misses.
Estimate

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

16%22%+3.5
Efficiency projects and the real payback.
Estimate

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

16%28%+4.5
The board, the CAPEX and the no to the project.
Estimate

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

18%14%+2.5
Save for real, and not selling green smoke.
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

  • energy audit
  • load curves
  • power tariffs
  • energy efficiency
  • CAPEX payback
  • plant metering
  • power market
  • real savings

Sources for this job· O*NET 17-2199.00· ESCO/ISCO 2149

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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