Playbooks overview

How LOIS for Word playbooks codify your team’s contract review expertise into rules the AI applies automatically, with fallbacks, precedent, and analytics.

Playbooks capture how your team negotiates: what issues to flag, how to redline them, what fallback positions to offer, and when to escalate. Once configured, LOIS for Word applies these rules consistently across every review.

Why playbooks matter

Without playbooks, every contract review starts from scratch. With them, you build on accumulated wisdom:

• Institutional memory - Years of negotiation expertise encoded and accessible
• Consistent quality - Same high standards applied regardless of reviewer or time pressure
• Faster reviews - Skip the obvious, focus on what needs attention
• Continuous improvement - Every negotiation makes your playbook stronger

Think of playbooks as your team’s collective brain for contract review.

The review workflow

1. Run the playbook

Open a contract and select your playbook.

Before running the playbook, add context that the AI should have before review. You can select quick context options like “high-value deal” that help the AI adjust its review approach. (For example, the review would be more conservative on high-value deals or focus only on critical issues for light-touch reviews.) Then, choose whether to focus the playbook on the entire contract or only on the redlined sections.

LOIS for Word scans the document and returns:

• Provisions that fail your standards
• Missing required terms
• Unusual language needing attention
• Suggested redlines with fallback positions

This first pass handles the routine issues so you can focus on what needs judgment.

2. Refine with Composer

Use Composer to adjust for context:

Adjust for leverage

This is a strategic deal with limited leverage. 
Which of these playbook positions can we soften while maintaining core protections?

Consider industry context

They're a regulated financial institution. 
How should we modify our data protection fallbacks to acknowledge their compliance requirements?

Test alternative approaches

They rejected our 12-month liability cap. 
Suggest three alternatives that achieve similar protection.

3. Handle exceptions

Playbooks cover standard situations. Composer handles the edge cases:

Multi-party dynamics

This involves three parties with different risk profiles.
How should we adjust our indemnification approach?

Regulatory overlays

New GDPR guidance just came out.
Does our playbook position still work or do we need updates?

Business trade-offs

If we accept their payment terms, what should we ask for in return?
Our playbook says 30 days, they want 90.

4. Capture what you learn

Save successful approaches back to your playbook using saved prompts or precedents.

Advanced techniques

Layer multiple playbooks

Apply playbooks in sequence for complex deals:

1. Base commercial terms
2. Industry-specific overlay (healthcare, financial services)
3. Deal-size exceptions

Ask Composer to flag conflicts between playbook positions.

Compare to market

Test your positions against standards:

Compare our playbook to market terms for Series B SaaS companies.
Where are we outside normal ranges?

Mine your precedents

Learn from past negotiations:

Show me the last 5 times we deviated from playbook on termination.
What were the business reasons?

Keeping playbooks effective

Add context with changes

When you save new fallback language, include why:

• When to use it (deal size, counterparty type, leverage situation)
• Why it works (business rationale)
• What you traded for it

Watch for drift

If you’re constantly overriding the same rule, update the playbook. Ad-hoc exceptions shouldn’t become the hidden standard.

Review quarterly

Market conditions change. Regulations change. Your risk tolerance changes. Compare your playbook to recent deals and update positions that no longer reflect reality.

Measuring impact

Track these to see if your playbook is working:

• Review speed: Time from document receipt to first markup
• Consistency: How much positions vary across reviewers
• Acceptance rate: How often counterparties accept your positions
• Issue detection: Problems caught by playbook vs. found manually
• Knowledge capture: New fallbacks added monthly