Automation index
How much of a Phlebotomist’s job can AI already do?
A queue and a label come earlier. Finding the vein and not sticking twice do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Phlebotomist
18%
AI can already do 18% of this job.
#574 / 643
View rankingPhlebotomist
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 |
|---|---|---|---|
Bookings, labels and tubes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Puncture, draw and not haemolyse.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 8% | +1.9 |
Calm children, fear and faints.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Spin, log and send to the lab.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Waste, needlesticks and hygiene.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
Not mixing up a patient or a tube.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
- Bookings, labels and tubes
- Puncture, draw and not haemolyse
- Calm children, fear and faints
- Spin, log and send to the lab
- Waste, needlesticks and hygiene
- Not mixing up a patient or a tube
- flebotomista
- extractora
- phlebotomist
Sources for this job· O*NET 31-9097.00· ESCO/ISCO 5329
IS WORK OVER?
Data architect
28%
#83 / 643
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