The employment legal team's highest-priority automation target. Generates jurisdiction-specific separation agreements across US, EMEA, and Australia. Validates proposed severance against company policy before sign-off.
The employment legal team handled roughly 100 separation agreements a year across three jurisdictions — US, EMEA, and Australia. Each agreement required manual drafting from templates, jurisdiction-specific language, and severance calculation validated against company policy. A single agreement took 2–4 hours of attorney time. Errors in severance calculation or jurisdiction-specific clauses carried real legal and financial exposure.
This was the team's highest-priority automation target — high volume, high repetition, high consequence for error. Before AI, the only option was more headcount or accepting the risk.
A Glean agent that takes structured inputs — employee tenure, role, location, separation type, salary — and generates a jurisdiction-specific separation agreement with the correct language for US, EMEA, or Australian employment law. A parallel severance calculator validates the proposed severance package against company policy, flagging discrepancies before the agreement reaches the attorney.
The agent's access controls wall employment legal data off from commercial legal (ABA Model Rule 1.6) — the exact confidentiality gap that had blocked adoption of prior contract-management tools. This wasn't a feature add. It was the architecture requirement that made the project possible.
Improvement: 2–4 hours of manual drafting and cross-referencing → 30 seconds of agent generation plus attorney review. The attorney's role shifted from drafter to reviewer — faster, more consistent, lower risk. The confidentiality architecture unlocked a pattern that scaled to 4 more agents across 3 additional departments within 6 months.
ROI: ~190 attorney-hours/year at a ~$250/hr loaded attorney cost — roughly $47K recovered annually.