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%

#273 / 633

Internist

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
Cross labs, imaging and a differential. Estimate No public source provides a precise figure. This is our estimate.	18%	48%	+8.6
Ward round, examination and decisions. Estimate No public source provides a precise figure. This is our estimate.	18%	14%	+2.5
Explain to family and ceilings of care. Estimate No public source provides a precise figure. This is our estimate.	16%	22%	+3.5
Discharge reports, consults and boards. Estimate No public source provides a precise figure. This is our estimate.	16%	28%	+4.5
Medical on-call and difficult patients. Estimate No public source provides a precise figure. This is our estimate.	16%	18%	+2.9
Team, residents and not fragmenting the patient. Estimate No public source provides a precise figure. This is our estimate.	16%	14%	+2.2
Total	100%		24%

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