Automation index
How much of a Wall tiler’s job can AI already do?
A joint set-out comes earlier. No ugly cut and holding the water do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Wall tiler
19%
AI can already do 19% of this job.
#476 / 633
View rankingWall tiler
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 |
|---|---|---|---|
Set-out, level and the tile format.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Adhesive, set and the tray fall.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Cut, drill and the tile that splits.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Grout, seal and the bath junction.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The client, the model change and the stock that is late.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
No ugly cut, and no leak.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 19% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- tile walls
- set out joints
- bed tiles
- tray fall
- seal junctions
- cut tiles
- drill tile
- grout
Sources for this job· O*NET 47-2044.00· ESCO/ISCO 7122
IS WORK OVER?
Data architect
28%
#83 / 633
View rankingData 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.
| Task | Share | AI | Points |
|---|---|---|---|
Models, lineage and the catalogue.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Pipelines, quality and the data contract.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The analyst who finds nothing and the mess.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Privacy, retention and the field you cannot keep.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The business, shadow IT and not building another lake.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
That it gets used, and not letting raw data rot.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 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