IS WORK OVER?

Automation index

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

A lab panel and a batch come earlier. Tasting, blending and signing the vintage do not.

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

Task comparison

Winemaker

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
Lab work, densities and fermentation control.
Estimate

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

16%40%+6.4
Tasting, blending and the call on the vintage.
Estimate

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

18%18%+3.2
Vineyard, ripeness and when to pick.
Estimate

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

18%22%+4
Ageing, barrel, filtration and bottling.
Estimate

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

16%32%+5.1
Cellar, records and the board’s visit.
Estimate

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

16%28%+4.5
Signing the vintage and owning the bottle.
Estimate

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

16%14%+2.2
Total100%25%

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

Skills and activities

  • make wine
  • taste musts
  • blend wines
  • control fermentation
  • decide harvest
  • age in barrel
  • bottle
  • sign the vintage

Sources for this job· O*NET 19-1012.00· ESCO/ISCO 2145

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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