IS WORK OVER?

Automation index

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

An occupancy forecast comes earlier. The house running without breakfast collapsing do not.

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

Task comparison

Hotel 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.

TaskShareAIPoints
Occupancy, rates and the forecast.
Estimate

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

18%48%+8.6
Shifts, complaints and the one who leaves staff.
Estimate

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

16%40%+6.4
Owner, the budget and not cutting breakfast.
Estimate

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

16%22%+3.5
Incident, the VIP and the one who floods the room.
Estimate

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

16%28%+4.5
The inspection, insurance and not dressing the log.
Estimate

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

18%14%+2.5
That the house runs, and breakfast standing.
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

  • occupancy
  • hotel forecast
  • shifts
  • complaints
  • VIP
  • breakfast
  • inspection
  • rates

Sources for this job· O*NET 11-9081.00· ESCO/ISCO 1411

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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