Automation index

How much of a Ice-cream maker’s job can AI already do?

A base recipe comes earlier. Creamy in the mouth without turning to ice do not.

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

Task comparison

IS WORK OVER?

22%

AI can already do 22% of this job.

#338 / 633

Ice-cream maker

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
Base, overrun and the recipe. Estimate No public source provides a precise figure. This is our estimate.	16%	44%	+7
Churn, age and the one that crystals. Estimate No public source provides a precise figure. This is our estimate.	18%	12%	+2.2
List, season and the flavour that misses. Estimate No public source provides a precise figure. This is our estimate.	16%	28%	+4.5
Workshop, cold and the broken chain. Estimate No public source provides a precise figure. This is our estimate.	16%	22%	+3.5
Summer, the queue and not running out of vanilla. Estimate No public source provides a precise figure. This is our estimate.	18%	18%	+3.2
Creamy in the mouth, and not selling ice. Estimate No public source provides a precise figure. This is our estimate.	16%	12%	+1.9
Total	100%		22%

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

IS WORK OVER?

25%

#230 / 633

Biomedical engineer

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
Design, validate and document clinical devices and software. Estimate No public source provides a precise figure. This is our estimate.	18%	48%	+8.6
Tests, MDR and risk. Estimate No public source provides a precise figure. This is our estimate.	16%	32%	+5.1
Hospital, clinicians and maintenance. Estimate No public source provides a precise figure. This is our estimate.	16%	22%	+3.5
Vendors, quality and traceability. Estimate No public source provides a precise figure. This is our estimate.	16%	18%	+2.9
User training and post-market watch. Estimate No public source provides a precise figure. This is our estimate.	18%	16%	+2.9
Not putting a half-built prototype in theatre. Estimate No public source provides a precise figure. This is our estimate.	16%	14%	+2.2
Total	100%		25%

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