Automation index
How much of a Dental technician’s job can AI already do?
A scan and a first design come earlier. A tooth that actually occludes does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Dental technician
22%
AI can already do 22% of this job.
#372 / 633
View rankingDental 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 |
|---|---|---|---|
Scan, design and nest the prosthesis.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Wax, cast, mill and stain.EstimateNo public source provides a precise figure. This is our estimate. | 20% | 12% | +2.4 |
Try-in with the dentist.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Orders, deadlines and lab charts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Fit, occlusion and remakes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 16% | +2.6 |
Dust, oven and not delivering a crooked tooth.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Scan, design and nest the prosthesis
- Wax, cast, mill and stain
- Try-in with the dentist
- Orders, deadlines and lab charts
- Fit, occlusion and remakes
- protesico dental
- dental technician
Sources for this job· O*NET 51-9081.00· ESCO/ISCO 3214
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