Operationalizing Your Contract Playbook: An In-House Guide to AI-Assisted Review
Tuesday, March 31st • 12:00 PM – 1:30 PM ET
Most legal teams have contract playbooks—but when volume spikes and timelines shrink, those “standards” often live in static documents, personal judgment calls, and inconsistent redlines. The result is slower review, uneven risk decisions, and avoidable back-and-forth with the business. This session focuses on how in-house teams can turn playbooks into an operational system—one that applies your positions consistently during review and helps your team move faster without increasing risk.
Join us on Tuesday, March 31 at 12:00 PM ET / 9:00 AM PT as Shaun Plant (Co-founder & Chief Legal Evangelist, LawVu) and Josie Norris (Head of Legal, Risk and Compliance, LawVu) explore how AI-powered playbooks can reduce manual redlining, standardize negotiation outcomes, and accelerate turnaround times. You’ll leave with a practical framework for structuring playbooks so they work in real workflows—flagging non-compliant clauses, suggesting approved fallbacks, and turning review decisions into reusable intelligence.
Key takeaways:
What makes a modern, scalable contract playbook (and why many fail to drive consistency)
How to structure playbooks for AI-assisted review—clauses, positions, fallbacks, and approvals
How to automatically flag non-compliant terms and route issues for escalation
How to apply approved fallback language quickly while maintaining your standards
How playbooks can feed contract intelligence and reporting across workflows
Who Should Attend:
This program is designed for in-house legal professionals who support contract review and negotiation—especially commercial counsel, legal ops leaders, and team leads responsible for playbooks, intake, or standard positions. It’s particularly relevant for teams facing high contract volume, inconsistent redlining across reviewers, pressure to speed up deal cycles, or a growing need to standardize risk decisions while keeping escalations targeted and efficient.
