Automation index
How much of a NDT technician’s job can AI already do?
An NDT procedure comes earlier. Seeing the defect without signing it sound do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
NDT technician
22%
AI can already do 22% of this job.
#386 / 633
View rankingNDT technician
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.
| Task | Share | AI | Points |
|---|---|---|---|
Procedure, technique and the part.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Test, the indication and the one that hides.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Report, the size and not inflating.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Field, the scaffold and the access.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The welder, the rush and not signing it sound.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
See the defect, and a true report.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- NDT
- ultrasound
- indications
- procedures
- scaffold
- test reports
- defect size
- access
Sources for this job· O*NET 51-9061.00· ESCO/ISCO 3119
IS WORK OVER?
ML engineer
29%
#64 / 633
View rankingML 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.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511