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?

22%

AI can already do 22% of this job.

#364 / 633

Order picker

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. Estimate No public source provides a precise figure. This is our estimate.	16%	48%	+7.7
Aisle, height and the parcel that will not fit. Estimate No public source provides a precise figure. This is our estimate.	22%	10%	+2.2
Errors, the short and calling it. Estimate No public source provides a precise figure. This is our estimate.	16%	28%	+4.5
Pace, the shift and the pallet that goes. Estimate No public source provides a precise figure. This is our estimate.	16%	22%	+3.5
Peaks, Black Friday and not mixing orders. Estimate No public source provides a precise figure. This is our estimate.	16%	18%	+2.9
Hit the SKU, and not leaving a lame pallet. Estimate No 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

IS WORK OVER?

25%

#230 / 633

Biomedical engineer

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, validate and document clinical devices and software. Estimate No public source provides a precise figure. This is our estimate.	18%	48%	+8.6
Tests, MDR and risk. Estimate No public source provides a precise figure. This is our estimate.	16%	32%	+5.1
Hospital, clinicians and maintenance. Estimate No public source provides a precise figure. This is our estimate.	16%	22%	+3.5
Vendors, quality and traceability. Estimate No public source provides a precise figure. This is our estimate.	16%	18%	+2.9
User training and post-market watch. Estimate No public source provides a precise figure. This is our estimate.	18%	16%	+2.9
Not putting a half-built prototype in theatre. Estimate No public source provides a precise figure. This is our estimate.	16%	14%	+2.2
Total	100%		25%

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