Automation index
How much of a Order picker’s job can AI already do?
Voice picking comes earlier. Hitting the SKU without leaving a lame pallet do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Order picker
22%
AI can already do 22% of this job.
#364 / 633
View rankingOrder picker
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 |
|---|---|---|---|
List, location and the scan.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 48% | +7.7 |
Aisle, height and the parcel that will not fit.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Errors, the short and calling it.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Pace, the shift and the pallet that goes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Peaks, Black Friday and not mixing orders.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Hit the SKU, and not leaving a lame pallet.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- picking
- scan locations
- shorts
- build pallets
- warehouse aisles
- order peaks
- SKUs
- voice picking
Sources for this job· O*NET 53-7062.00· ESCO/ISCO 4321
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