Contract Redlining With ChatGPT: Limitations, Liability, and Alternatives

The promise of using generative AI to overhaul complex legal tasks like contract review is highly compelling.

For B2B organizations seeking efficiency, the ability to instantly redline a lengthy agreement sounds like a game-changer.

However, the definitive answer requires important nuance: Yes, ChatGPT can suggest changes and assist with redlining, but it cannot safely or reliably perform independent, professional redlining that replaces human legal judgment.

The Core Distinction: Co-Pilot vs. Attorney

The difference lies in functionality versus reliability.

ChatGPT is, at its core, a powerful co-pilot—a tool that excels at pattern matching, summarization, and generating plausible text. It is not an attorney, and its output comes with no legal warranty.

Treating it as a full redlining solution uploading entire contracts onto ChatGPT introduces immediate and unacceptable risks:

1. Hallucination Risk: It can generate confident, yet legally incorrect, suggestions.
2. Context Blindness: It lacks the knowledge of your organization's specific negotiation playbook or risk tolerance.
3. Liability Gap: If an AI-generated error leads to a lawsuit, your company is 100% liable.

To leverage AI safely and effectively in this space, you must understand what it can achieve and, more importantly, where its current limitations demand human supervision.

The AI Co-Pilot: Where ChatGPT Excels in Contract Review

While public, general-purpose ChatGPT cannot be trusted for final redlines, it offers substantial utility when used strategically to augment human effort. Its greatest strengths lie in speed and basic analysis:

1. Summarization, Extraction, and Comparison

ChatGPT is excellent at breaking down contract complexity and performing rapid data extraction:

• Plain English Summary: It can generate a fast, simplified summary of a complex section, helping non-legal stakeholders grasp the core intent.

• Extraction: It can quickly flag and pull out key non-variable clauses, such as renewal dates, governing law jurisdiction, or indemnity provisions.

• Comparison: It can assist in basic analysis, such as comparing two contracts to identify differences in specific clauses or general scope, saving time on initial manual checks.

2. Simple Logic and Boilerplate Generation

For low-stakes text and foundational document creation, the AI is a strong starting point:

• Logical Check: It can be used to check the basic logical flow and consistency of a simple sentence or paragraph structure.

• Explanatory Comments: It is highly effective at drafting succinct, professional commentary to accompany a legal professional's proposed redlines, which speeds up internal communication.

The Redlining Deficit: Why ChatGPT Lacks Legal Judgment

Despite its analytical power, public, general-purpose AI cannot be relied upon to perform accurate legal redlining. The technology has fundamental limitations that create unacceptable financial and legal risk for B2B transactions.

1. Context Blindness and Policy Violation

Contracts are negotiated based on highly specific business contexts, risk tolerance, and historical precedents (your negotiation playbook). ChatGPT lacks access to this proprietary knowledge.

• Generic Suggestions: It suggests generic, statistically probable changes that may look correct but often violate your organization's core business policies (e.g., agreeing to a liability cap that is too low for your internal risk tolerance).

• Negotiation Strategy: It cannot interpret the current status of a negotiation or strategically position a counter-redline based on relationship history. It operates on a clean slate with every prompt.

2. Failure to Process Real-World Documents

The ideal input for ChatGPT is clean, simple text. Real-world legal documents are often complex and messy, posing a technical hurdle the AI cannot overcome:

• Inconsistent Formatting: Contracts often contain numerous formatting errors, inconsistent numbering, and complex tables. ChatGPT struggles to accurately process these documents, leading to faulty extraction and misplaced suggestions.

• Tracked Changes: The AI can become confused when trying to interpret documents filled with multiple rounds of tracked changes, which is a common state for high-value B2B agreements.

3. The Risk of Hallucination and Legal Nuance

This is the ultimate liability risk. The AI's inability to grasp deep legal meaning results in dangerous inaccuracies.

• Legal Inaccuracy: It can easily miss subtle, jurisdiction-specific legal distinctions (e.g., how "indemnification" is interpreted in one state versus another), leading to unenforceable or disadvantageous clauses.

• Fabricated Output: As a predictive text model, its goal is to be plausible, not factual. Relying on its redlining means accepting the risk that it will "hallucinate"—generating confident, yet fabricated, legal conclusions or suggestions that could result in litigation costs far outweighing any time savings.

From General AI to Precision: The Future of Contract Redlining

Given the high liability associated with public LLMs, the future of contract redlining for B2B lies in secure, AI-powered contract review platforms that augment, rather than replace, human expertise.

1. The Necessity of Secure, Specialized Platforms

True redlining requires secure, private environments that adhere to strict data security and compliance standards.

• AI-Native CLM: Specialized Contract Lifecycle Management (CLM) incorporate AI models trained exclusively on vast libraries of legal and contractual data, not the general public web.

• Zero Data Retention: These platforms offer guaranteed zero-data-retention policies, ensuring your private contracts are never used for external model training.

2. Customization for Precision

To achieve the "precise redlining" required in B2B, the AI must be customized to your firm.

• Playbook Encoding: The most effective solutions allow your organization's legal playbook, preferred clause language, and risk tolerance to be formally encoded into the AI's review parameters, ensuring suggestions align with your business strategy.

• Integration: True redlining integrates with the tools lawyers use daily (e.g., MS Word API), a feat that general-purpose chatbots cannot achieve alone.

3. Human Supervision is Non-Negotiable

The lawyer remains the central authority. AI should be viewed as a quality control and acceleration tool.

• Augmentation, Not Replacement: Use AI to flag and extract key information (e.g., renewal dates, governing law) and to assist with [redlining contracts and] drafting explanatory comments for review.

• The Final Say: Every suggestion, every redline, and every proposed clause must be reviewed and accepted by a legal professional before final execution.

Final Thought: Policy and Strategy

The question is no longer whether AI can be used for contracts, but whether your organization has the policy and platform to use it safely. The risk of error, hallucination, and data leakage associated with general-purpose tools like ChatGPT is simply too high for high-stakes B2B agreements.

To truly gain efficiency and minimize liability, the focus must shift to purpose-built, secure AI-powered contract redlining tools that augment, rather than replace, human legal expertise.

If you have questions, feel free to book a demo with us!