Key Takeaway: AI enables legal teams to build negotiation playbooks faster and at a lower cost by shifting work from senior counsel to legal engineers. This resource arbitrage reduces bottlenecks, accelerates Contract Lifecycle Management (CLM) adoption, and preserves expert oversight. The result is scalable playbooks that evolve in line with business needs, delivering tangible operational impact.

A common misconception among legal operations leaders is that AI primarily facilitates faster building of contract playbooks. However, the breakthrough isn’t just speed; it’s resource arbitrage. For the first time, legal teams are creating comprehensive negotiation playbooks without burning out their most expensive talent or breaking their budgets on external counsel.

Seasoned Lawyers Traditionally Build Playbooks

Corporations follow a predictable pattern in the traditional approach to playbook creation. They either task their most experienced lawyers with the painstaking work of creating every playbook, or they engage an external provider (e.g., Alternative Legal Service Providers (ALSPs) or law firms) to build playbooks from scratch.

Even when organizations commit the resources, playbooks quickly become stale. Regulations change, new technologies emerge, and business policies are updated. Without regular review and upgrades, playbooks become expensive shelfware. 

The US$50,000 Playbook Problem Crippling CLM Implementations

Contract playbooks are not just nice-to-have training materials. They’re the strategic foundation that transforms CLM from workflows and filing systems into a competitive advantage. Yet, most legal departments continue to operate without them. It’s a matter of bandwidth, budget, or both.

Building a playbook the traditional way requires an estimated 160 hours of seasoned lawyer time. That’s beyond the capacity for an in-house lawyer to take on. Creating a quality playbook requires sustained concentration, not 15-minute intervals between calls. 

For in-house teams, this means pulling senior lawyers away from critical business matters for weeks. Alternatively, at $300 – $500 per hour for external counsel, that’s $48,000 – $80,000 per playbook. The first playbook is the most expensive. After that, the price per playbook decreases because the work you do for specific provisions will be reusable across contract types.

Given the sticker price, companies launch their CLM systems without negotiation playbooks. Negotiators then default to escalating issues to senior counsel, creating bottlenecks that reduce deal velocity and frustrate business stakeholders.

AI Is Rewriting the Legal Role

AI fundamentally changes the equation, not by eliminating human expertise, but by shifting who provides it and when.

Using AI to analyze contract templates and executed agreements, legal engineers produce first-draft playbooks in fifty to seventy-five percent of the time it typically takes seasoned lawyers to draft a playbook. More importantly, this work doesn’t require a legal expert. A legal engineer handles the heavy lifting of identifying negotiable concepts, extracting language variations, and categorizing positions, alongside first-line quality control. These legal engineers are often qualified lawyers with a strong interest in technology.

This realization hit home this year with two notable playbook projects. AI completed the analysis of the template and executed around twenty contracts for each contract type. The executed contracts were on both client own paper and third-party paper. AI identifies standard and fallback language, as well as the frequency with which different positions appear in a sample of executed deals. 

When enabled to pull frequency data, ideally from a significant sample size, you spot the preferred positions that rarely survive negotiation and can consider replacing the preferred language in the template with fallback language to reduce unnecessary back and forth.

AI also creates sensible explanatory guidance for negotiators as part of the playbook. 

Quality Control and Client Judgment Still Matter

Before getting carried away with visions of fully automated playbook creation, let’s address the practical realities. AI playbooks require significant quality control from the provider, as well as input from the client.
AI hallucination remains a primary concern among clients. When asked to capture exact fallback language, AI tools may summarize or subtly modify the terms. The same prompts can produce inconsistent results across different contract types. Neither AI nor ALSP consulting teams can distinguish between language in executed contracts that represents a universally acceptable fallback position.

Language that results from an escalation process that the client accepts for a given deal may not be acceptable for a different one. This distinction is crucial when building playbooks. As a result, clients must weigh in to clarify the difference between a fallback and an escalation.

The Value of Human Review 

Once you have that first draft playbook, you still need humans in the loop. Legal engineers refine prompts to reduce errors and implement comprehensive quality control processes. But they also recognize that every AI output requires human validation. The goal isn’t perfection from AI. It eliminates the blank page problem and provides an initial structured foundation for legal experts to refine.

Learn more about Epiq Contracts Solutions.
 


Lindsey Pitt, Vice President, Epiq Advisory for Corporate Legal Departments
Lindsey Pitt brings more than 25 years of experience in legal operations and consulting to Epiq Advisory for Corporate Legal Departments. Over the past 15 years, she has been responsible for consulting, designing, and launching some of the legal industry’s most innovative technology contracts management solutions for clients that included Fortune 50 and Fortune Global 250 companies.