Automation index
How much of a Audiologist’s job can AI already do?
An audiogram and a curve tweak come earlier. Fitting an aid so the person actually hears does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Audiologist
23%
AI can already do 23% of this job.
#288 / 633
View rankingAudiologist
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 |
|---|---|---|---|
Audiograms, thresholds and a first fit.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 40% | +7.2 |
Trial, mould and adapt the aid.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Explain, habituation and follow-up.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Reports for ENT and notes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Repair, clean and prototype stock.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Children, older people and not overselling.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 23% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Audiograms, thresholds and a first fit
- Trial, mould and adapt the aid
- Explain, habituation and follow-up
- Reports for ENT and notes
- Repair, clean and prototype stock
- Children, older people and not overselling
- audiologo
- audiologist
Sources for this job· O*NET 29-1181.00· ESCO/ISCO 2266
IS WORK OVER?
Last-mile manager
26%
#211 / 633
View rankingLast-mile manager
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 |
|---|---|---|---|
Zones, capacity and route cuts.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 44% | +7 |
KPIs, first attempt and incidents.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
The courier, the lobby and the one who will not open.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Peaks, rain and hiring too many or too few.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The angry customer and not dressing delivered.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Cover the peak, and not leaving the neighbourhood orphaned.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 26% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- delivery zones
- first attempt
- delivery KPIs
- last-mile peaks
- lobbies
- delivery incidents
- courier capacity
- cut routes
Sources for this job· O*NET 11-3071.00· ESCO/ISCO 1324