IS WORK OVER?

Automation index

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

A script comes earlier. Them not hanging up in ten seconds, and not lying, do not.

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

Task comparison

Telemarketer

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
Script, queue and the shift.
Estimate

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

18%48%+8.6
The call, the no and the one who insults.
Estimate

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

16%40%+6.4
Data, GDPR and not pushing extra.
Estimate

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

16%22%+3.5
Target, the bonus and not dressing contacts.
Estimate

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

16%28%+4.5
The supervisor, quality and not skipping the no.
Estimate

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

18%14%+2.5
That they do not hang up, and not lying.
Estimate

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

16%12%+1.9
Total100%27%

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

Skills and activities

  • scripts
  • call queues
  • GDPR
  • targets
  • call quality
  • shifts
  • contacts
  • the no

Sources for this job· O*NET 41-9041.00· ESCO/ISCO 4222

Data architect

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
Models, lineage and the catalogue.
Estimate

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

18%52%+9.4
Pipelines, quality and the data contract.
Estimate

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

16%40%+6.4
The analyst who finds nothing and the mess.
Estimate

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

16%22%+3.5
Privacy, retention and the field you cannot keep.
Estimate

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

16%28%+4.5
The business, shadow IT and not building another lake.
Estimate

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

18%14%+2.5
That it gets used, and not letting raw data rot.
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

  • model data
  • lineage
  • data catalogue
  • data contracts
  • pipeline quality
  • retention
  • data lakes
  • privacy

Sources for this job· O*NET 15-1243.00· ESCO/ISCO 2511

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