Automation index
How much of a Mixer-truck driver’s job can AI already do?
A route and a slump come earlier. Arriving fresh and pouring without blocking the pump do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Mixer-truck driver
21%
AI can already do 21% of this job.
#412 / 633
View rankingMixer-truck driver
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 |
|---|---|---|---|
Order, mix and mixing time.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Drive, site and the tight access.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Pour, pump and the one who wants more water.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Wash, leftovers and not leaving a hard drum.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The jam, the delay and the concrete that sets.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Arrive fresh, and not adding extra water.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 21% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- drive mixers
- batch
- pour concrete
- wash the drum
- slump
- concrete pump
- mix order
- site access
Sources for this job· O*NET 53-3032.00· ESCO/ISCO 8332
IS WORK OVER?
Prompt engineer
34%
#21 / 633
View rankingPrompt 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 |
|---|---|---|---|
Design prompts, tools and chains.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 58% | +13.9 |
Evals, datasets and regressions.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
Integrate with product and safety limits.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to the business about a good output.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Watch hallucinations, bias and cost.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 20% | +2.8 |
Decide what the model gets, and what it does not.EstimateNo public source provides a precise figure. This is our estimate. | 12% | 16% | +1.9 |
| Total | 100% | 34% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Design prompts, tools and chains
- Evals, datasets and regressions
- Integrate with product and safety limits
- Talk to the business about a good output
- Watch hallucinations, bias and cost
- prompt engineer
- ingeniero de prompts
- llm engineer
Sources for this job· O*NET 15-1252.00· ESCO/ISCO 2512