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

Mobile developer

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
Screens, API and offline state.
Estimate

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

18%52%+9.4
Store review, crashlytics and ANRs.
Estimate

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

16%40%+6.4
Real device, the notch and fragmented Android.
Estimate

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

16%22%+3.5
CI, tests and the week's release.
Estimate

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

16%28%+4.5
Product, the redesign and not breaking what people use.
Estimate

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

18%14%+2.5
That it does not crash, and not ignoring the one-star.
Estimate

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

16%12%+1.9
Total100%28%

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

Skills and activities

  • build apps
  • ship to stores
  • crashlytics
  • offline state
  • ANRs
  • mobile tests
  • fragmented Android
  • store review

Sources for this job· O*NET 15-1252.00· ESCO/ISCO 2514

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