Automation index
How much of a Product owner’s job can AI already do?
A backlog and a user story write fast. Telling engineering no, and a customer yes, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Product owner
27%
AI can already do 27% of this job.
#171 / 623
View rankingProduct owner
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 |
|---|---|---|---|
Write stories, criteria and backlog.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 50% | +11 |
Maps, roadmaps and release notes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Refine with engineering and design.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Talk to users and stakeholders.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Prioritise, cut scope and say no.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Accept the increment and own the value.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Write stories, criteria and backlog
- Maps, roadmaps and release notes
- Refine with engineering and design
- Talk to users and stakeholders
- Prioritise, cut scope and say no
- Accept the increment and own the value
- product owner
- po agil
Sources for this job· O*NET 15-1199.09· ESCO/ISCO 2519
IS WORK OVER?
ML engineer
29%
#64 / 623
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