Role library · aec-roles-v1 · legal
AI Exposure Check
Paralegal
Illustrative task-level Exposure from the offline sample model — not a market survey.
Exposure 54% average exposure · CHF 112560 illustrative time value · 5 tasks
Tasks
Exposure and reasoning
| Task | Exposure % | Reasoning |
|---|---|---|
| Redline standard NDAs | 67% | Moderate exposure: “Redline standard NDAs” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Extract clause risk summaries | 62% | Moderate exposure: “Extract clause risk summaries” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Draft privacy-notice updates | 61% | Moderate exposure: “Draft privacy-notice updates” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Track regulatory change digests | 58% | Moderate exposure: “Track regulatory change digests” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Advise on contested disputes | 20% | Low exposure: “Advise on contested disputes” is relationship-, safety-, or judgment-critical; keep a human owner and use AI only as a narrow aid. |
Sample actions
- Pilot generative drafting on the two highest-exposure tasks with human review.
- Bundle similar low-judgment tasks into one assisted workflow queue.
- Set an audit sample rate for AI outputs before customer or regulator exposure.
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.