Automation index

How much of a Materials engineer’s job can AI already do?

A tensile curve comes earlier. Picking the alloy so it does not fail in service do not.

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

Task comparison

IS WORK OVER?

27%

AI can already do 27% of this job.

#162 / 623

Materials 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
Tests, metallography and the spec. Estimate No public source provides a precise figure. This is our estimate.	18%	48%	+8.6
Shop, heat treatment and the part that fails spec. Estimate No public source provides a precise figure. This is our estimate.	16%	22%	+3.5
Material choice, corrosion and fatigue. Estimate No public source provides a precise figure. This is our estimate.	16%	36%	+5.8
Supplier, certificates and the doubtful batch. Estimate No public source provides a precise figure. This is our estimate.	16%	28%	+4.5
The field failure and the root cause. Estimate No public source provides a precise figure. This is our estimate.	18%	14%	+2.5
That it does not fail, and not swapping the material blind. Estimate No public source provides a precise figure. This is our estimate.	16%	12%	+1.9
Total	100%		27%

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

IS WORK OVER?

27%

#138 / 623

Category manager

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
Assortment, prices and the shelf. Estimate No public source provides a precise figure. This is our estimate.	18%	48%	+8.6
Supplier, the negotiation and the one that will not list. Estimate No public source provides a precise figure. This is our estimate.	16%	40%	+6.4
Promo, the calendar and the one that clashes. Estimate No public source provides a precise figure. This is our estimate.	16%	22%	+3.5
Data, margin and not killing the category. Estimate No public source provides a precise figure. This is our estimate.	16%	28%	+4.5
Own-label, the clash and not hiding it. Estimate No public source provides a precise figure. This is our estimate.	18%	14%	+2.5
That the category wins. Estimate No public source provides a precise figure. This is our estimate.	16%	12%	+1.9
Total	100%		27%

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