IS WORK OVER?

Automation index

How much of a Appliance technician’s job can AI already do?

An error code comes earlier. Diagnosing in someone else’s kitchen and leaving it washing do not.

This percentage does not predict how many people will lose their jobs.

Task comparison

Appliance 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
Code, history and the spare.
Estimate

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

16%40%+6.4
Strip, test and the built-in one.
Estimate

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

18%12%+2.2
Order the part, second visit and the client who is out.
Estimate

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

16%28%+4.5
Warranty, misuse and what it does not cover.
Estimate

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

16%22%+3.5
The tight kitchen and not marking the unit.
Estimate

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

18%18%+3.2
Leave it washing, and not swapping the board blind.
Estimate

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

16%12%+1.9
Total100%22%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • diagnose appliances
  • error codes
  • strip built-ins
  • spares
  • warranty
  • second visit
  • test boards
  • service call

Sources for this job· O*NET 49-9031.00· ESCO/ISCO 7412

Station master

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
Graph, orders and the station log.
Estimate

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

16%40%+6.4
Platforms, the crossing and the late train.
Estimate

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

18%14%+2.5
Staff, shifts and the ticket-office gap.
Estimate

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

16%32%+5.1
Incident, passengers and the PA.
Estimate

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

16%22%+3.5
Works, closures and leaving nobody on the platform.
Estimate

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

18%18%+3.2
That they do not meet, and signing the graph.
Estimate

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

16%12%+1.9
Total100%23%

Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low

Skills and activities

  • train graph
  • orders
  • platforms
  • ticket office
  • PA
  • line crossings
  • station shifts
  • station logs

Sources for this job· O*NET 53-4031.00· ESCO/ISCO 4323

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