IS WORK OVER?

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

Materials engineer

Where the time goes

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.

TaskShareAIPoints
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
Total100%27%

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

Skills and activities

  • materials tests
  • metallography
  • choose alloys
  • corrosion
  • fatigue
  • heat treatment
  • failure analysis
  • batch certificates

Sources for this job· O*NET 17-2131.00· ESCO/ISCO 2149

Data architect

Where the time goes

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.

TaskShareAIPoints
Models, lineage and the catalogue.
Estimate

No public source provides a precise figure. This is our estimate.

18%52%+9.4
Pipelines, quality and the data contract.
Estimate

No public source provides a precise figure. This is our estimate.

16%40%+6.4
The analyst who finds nothing and the mess.
Estimate

No public source provides a precise figure. This is our estimate.

16%22%+3.5
Privacy, retention and the field you cannot keep.
Estimate

No public source provides a precise figure. This is our estimate.

16%28%+4.5
The business, shadow IT and not building another lake.
Estimate

No public source provides a precise figure. This is our estimate.

18%14%+2.5
That it gets used, and not letting raw data rot.
Estimate

No public source provides a precise figure. This is our estimate.

16%12%+1.9
Total100%28%

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

Skills and activities

  • model data
  • lineage
  • data catalogue
  • data contracts
  • pipeline quality
  • retention
  • data lakes
  • privacy

Sources for this job· O*NET 15-1243.00· ESCO/ISCO 2511

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