IS WORK OVER?

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

Hearing 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.

TaskShareAIPoints
Audiometry, thresholds and the fitting.
Estimate

No 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.
Estimate

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

18%18%+3.2
Program, verify and retune the aid.
Estimate

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

16%32%+5.1
Repair, batteries and aftercare.
Estimate

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

16%28%+4.5
Explain, family and the stigma of the device.
Estimate

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

18%14%+2.5
That they actually hear, and not taking the aid back.
Estimate

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

16%12%+1.9
Total100%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

Logistics analyst

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
Costs, OTIF and the dashboard.
Estimate

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

18%52%+9.4
Simulate network, warehouses and the what-if.
Estimate

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

16%40%+6.4
Ops, dirty data and going down to the dock.
Estimate

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

16%22%+3.5
Report, the board and the cut that hurts.
Estimate

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

16%28%+4.5
The haulier, the contract and not breaking the service.
Estimate

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

18%14%+2.5
A real saving, and not dressing OTIF.
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

  • logistics costs
  • OTIF
  • simulate network
  • what-if
  • dirty data
  • haulage contracts
  • dashboards
  • network cuts

Sources for this job· O*NET 13-1081.00· ESCO/ISCO 2421

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