IS WORK OVER?

Automation index

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

A load plan and a video analysis come earlier. Competing, getting hurt, and coming back do not.

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

Task comparison

Professional athlete

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
Load plans, nutrition and video analysis.
Estimate

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

16%35%+5.6
Social, contracts and travel diary.
Estimate

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

14%28%+3.9
Train and compete the craft.
Estimate

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

26%4%+1
Physio, sleep and prevention.
Estimate

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

16%8%+1.3
Wear the shirt, press and the dressing room.
Estimate

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

14%10%+1.4
Manage an injury and the end of a career.
Estimate

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

14%12%+1.7
Total100%15%

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

Skills and activities

  • Load plans, nutrition and video analysis
  • Social, contracts and travel diary
  • Train and compete the craft
  • Physio, sleep and prevention
  • Manage an injury and the end of a career
  • deportista
  • atleta profesional
  • futbolista
  • professional athlete

Sources for this job· O*NET 27-2021.00· ESCO/ISCO 3421

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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