Automation index
How much of a Hearing aid technician’s job can AI already do?
An earmould and a gain curve come earlier. Getting the patient to hear and live with it do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Hearing aid technician
24%
AI can already do 24% of this job.
#281 / 633
View rankingHearing aid 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 |
|---|---|---|---|
Audiometry, thresholds and the fitting.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Take the mould, trial and the one who hates the whistle.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Program, verify and retune the aid.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Repair, batteries and aftercare.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Explain, family and the stigma of the device.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That they actually hear, and not taking the aid back.EstimateNo 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
Skills and activities
- fit hearing aids
- take earmoulds
- audiometry
- program aids
- verify gain
- repair aids
- hearing thresholds
- aftercare
Sources for this job· O*NET 29-2092.00· 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