Role library · aec-roles-v1 · people
AI Exposure Check
People Operations Partner
Illustrative task-level Exposure from the offline sample model — not a market survey.
Exposure 62% average exposure · CHF 77862 illustrative time value · 5 tasks
Tasks
Exposure and reasoning
| Task | Exposure % | Reasoning |
|---|---|---|
| Screen CV keyword matches | 82% | High generative AI exposure: “Screen CV keyword matches” is largely pattern-matching, drafting, or structured data work that models can assist with human review. |
| Draft offer-letter templates | 76% | High generative AI exposure: “Draft offer-letter templates” is largely pattern-matching, drafting, or structured data work that models can assist with human review. |
| Summarise engagement surveys | 68% | Moderate exposure: “Summarise engagement surveys” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Schedule interview panels | 60% | Moderate exposure: “Schedule interview panels” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Advise on sensitive employee cases | 22% | Low exposure: “Advise on sensitive employee cases” is relationship-, safety-, or judgment-critical; keep a human owner and use AI only as a narrow aid. |
Sample actions
- Set an audit sample rate for AI outputs before customer or regulator exposure.
- Instrument time saved for 30 days before expanding automation scope.
- Protect relationship-critical and judgment-heavy tasks from full automation.
What these figures are not
- Illustrative role library from the offline AEC sample model (aec-ai-v1), not a surveyed occupation census.
- Task packs and exposure scores are deterministic samples for self-serve triage — Faethm-style ontology breadth, not Pearson proprietary data.
- CHF time-value estimates use illustrative Swiss mid-market rates and annual hours.