IS WORK OVER?

Automation index

How much of a Medical sales rep’s job can AI already do?

A leave-behind comes earlier. Getting into clinic without selling smoke to the doctor do not.

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

Task comparison

Medical sales rep

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
Route, leave-behind and the call.
Estimate

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

16%40%+6.4
Clinic, the minute and the one who will not see you.
Estimate

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

18%14%+2.5
Trial, the SmPC and not over-claiming.
Estimate

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

16%28%+4.5
Pharmacy, stock and the one who will not order.
Estimate

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

16%22%+3.5
The code, compliance and not over-hosting.
Estimate

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

18%18%+3.2
Get in for real, and not selling smoke.
Estimate

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

16%12%+1.9
Total100%22%

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

Skills and activities

  • call routes
  • leave-behinds
  • SmPC
  • pharmacies
  • compliance
  • trials
  • clinics
  • not over-claim

Sources for this job· O*NET 41-4011.00· ESCO/ISCO 2433

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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