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?

25%

#237 / 623

Electronics 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
Schematic, simulation and PCB layout. Estimate No public source provides a precise figure. This is our estimate.	18%	48%	+8.6
Firmware, tests and bench. Estimate No public source provides a precise figure. This is our estimate.	16%	32%	+5.1
Vendors, prototypes and certifications. Estimate No public source provides a precise figure. This is our estimate.	16%	22%	+3.5
Debug EMC and field faults. Estimate No public source provides a precise figure. This is our estimate.	16%	18%	+2.9
Documentation, versioning and quality. Estimate No public source provides a precise figure. This is our estimate.	18%	16%	+2.9
Not sending a blind board to manufacture. 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