Automation index
How much of a Information specialist’s job can AI already do?
Filing a PDF and a thesaurus come earlier. Retrieving what the house does not know it has do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Information specialist
31%
AI can already do 31% of this job.
#33 / 623
View rankingInformation specialist
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 |
|---|---|---|---|
Classify, index and describe holdings.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Thesauri, metadata and the internal search.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 52% | +8.3 |
Answer queries and retrieve what nobody can find.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Records policy, retention and destruction.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 22% | +4 |
Train people who file badly and people who do not file.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
The organisation’s memory, and not losing it.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 30% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- manage records
- index holdings
- classify archives
- retrieve information
- metadata
- thesauri
- retention policy
- internal search
Sources for this job· O*NET 25-4013.00· ESCO/ISCO 2622
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