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
IS WORK OVER?
Medical sales rep
22%
AI can already do 22% of this job.
#401 / 633
View rankingMedical 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.
| Task | Share | AI | Points |
|---|---|---|---|
Route, leave-behind and the call.EstimateNo 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.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Trial, the SmPC and not over-claiming.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Pharmacy, stock and the one who will not order.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The code, compliance and not over-hosting.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Get in for real, and not selling smoke.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 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
IS WORK OVER?
Prompt engineer
34%
#21 / 633
View rankingPrompt 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.
| Task | Share | AI | Points |
|---|---|---|---|
Design prompts, tools and chains.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 58% | +13.9 |
Evals, datasets and regressions.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Integrate with product and safety limits.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to the business about a good output.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Watch hallucinations, bias and cost.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 20% | +2.8 |
Decide what the model gets, and what it does not.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 16% | +1.9 |
| Total | 100% | 34% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Design prompts, tools and chains
- Evals, datasets and regressions
- Integrate with product and safety limits
- Talk to the business about a good output
- Watch hallucinations, bias and cost
- prompt engineer
- ingeniero de prompts
- llm engineer
Sources for this job· O*NET 15-1252.00· ESCO/ISCO 2512