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 / 633

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?

24%

#282 / 633

Job insertion technician

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
Employer prospecting and the agreements. Estimate No public source provides a precise figure. This is our estimate.	16%	40%	+6.4
Go with them to the job and the first day. Estimate No public source provides a precise figure. This is our estimate.	18%	18%	+3.2
Follow-up, incidents and the one who quits. Estimate No public source provides a precise figure. This is our estimate.	16%	32%	+5.1
Reports, indicators and justifying the grant. Estimate No public source provides a precise figure. This is our estimate.	16%	28%	+4.5
The hard candidate and the employer who pulls out. Estimate No public source provides a precise figure. This is our estimate.	18%	14%	+2.5
That they stay, and not dressing the figure. Estimate No public source provides a precise figure. This is our estimate.	16%	12%	+1.9
Total	100%		24%

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