Automation index
How much of a Florist’s job can AI already do?
An order and a moodboard come earlier. A bouquet that holds and does not drop does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Florist
18%
AI can already do 18% of this job.
#575 / 643
View rankingFlorist
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 |
|---|---|---|---|
Orders, suppliers and a first design.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Compose, tie and condition.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Weddings, funerals and difficult clients.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Shop, window and waste.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Cold room, water and the flower calendar.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 16% | +2.6 |
Not delivering a bouquet that dies tomorrow.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 18% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Orders, suppliers and a first design
- Compose, tie and condition
- Weddings, funerals and difficult clients
- Shop, window and waste
- Cold room, water and the flower calendar
- florista
- florist
Sources for this job· O*NET 27-1023.00· ESCO/ISCO 7549
IS WORK OVER?
ML engineer
29%
#64 / 643
View rankingML engineer
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 |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511