Automation index
How much of a Agile coach’s job can AI already do?
A board and a stock retro come earlier. The team stopping the fake sprint do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Agile coach
24%
AI can already do 24% of this job.
#257 / 633
View rankingAgile coach
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 |
|---|---|---|---|
Boards, metrics and the cadence design.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
The real retro and the elephant in the room.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Train, unblock and the manager who will not let go.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Scale, dependencies and fake agile.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Conflict, power and not selling extra frameworks.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
That they stop faking it, and not inflating velocity.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 24% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- retros
- agile cadence
- unblock teams
- flow metrics
- fake agile
- agile training
- dependencies
- velocity
Sources for this job· O*NET 13-1111.00· ESCO/ISCO 2421
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