Role library · aec-roles-v1 · public-sector
AI Exposure Check
Citizen Support Agent
Illustrative task-level Exposure from the offline sample model — not a market survey.
Exposure 69% average exposure · CHF 65274 illustrative time value · 5 tasks
Tasks
Exposure and reasoning
| Task | Exposure % | Reasoning |
|---|---|---|
| Triage inbound tickets | 83% | High generative AI exposure: “Triage inbound tickets” is largely pattern-matching, drafting, or structured data work that models can assist with human review. |
| Draft first-response replies | 89% | High generative AI exposure: “Draft first-response replies” is largely pattern-matching, drafting, or structured data work that models can assist with human review. |
| Update knowledge-base articles | 78% | High generative AI exposure: “Update knowledge-base articles” is largely pattern-matching, drafting, or structured data work that models can assist with human review. |
| Escalate complex cases | 29% | Low exposure: “Escalate complex cases” is relationship-, safety-, or judgment-critical; keep a human owner and use AI only as a narrow aid. |
| Log CRM notes | 66% | Moderate exposure: “Log CRM notes” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
Sample actions
- Train the team on prompt libraries tied to existing templates.
- Pilot generative drafting on the two highest-exposure tasks with human review.
- Bundle similar low-judgment tasks into one assisted workflow queue.
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.