How to Leverage AI for Contract Review

Most lawyers are excited about AI because of its potential for saving time while increasing efficiency, and roughly half of those in legal departments already use generative AI multiple times a week, with about 200 hours of time-savings per lawyer projected this year. As of May 2025, the top five most popular legal use cases for generative AI included document review, document summarization, and contract drafting. This article will explore tactical ways to leverage AI’s strengths in order to unlock efficiency and save time with contracts.

Identifying Contract Workflows Best Suited for AI

Here are some techniques to identify elements of contract workflows that are ripe for AI augmentation. The first step is to do a thorough audit of the contract pain points facing your team. For example, if a salesperson sent a contract to legal and it didn’t move forward, what blocked it? Perhaps the contract was incomplete, sent to the wrong person, based on an old template, or contained a controversial provision. Each of these pain points might require a different solution, and those will vary by risk level and complexity. For initial AI integration, one best practice is to identify tasks that are urgent and important, but low risk.

Examples of good candidates for these workflows include: answering questions from the sales team, triaging and routing contracts to the right DRI, and creating first drafts of high volume documents like NDAs or order forms, as these tasks either don’t need extensive attorney review.

Integrating AI Throughout the Contract Lifecycle

AI can boost efficiency and accuracy by taking on tasks throughout the contract lifecycle. Here are some examples of high leverage implementations by general contract stage:

• Commercial Alignment: Recording notes, action items, and summaries of the commercial terms and the business purpose before a contract is drafted

• Intake and Routing: Capturing essential information about the agreement, ensuring that requests are routed to the correct person, and frequently asked questions are promptly answered

• Drafting: Leverage precedents and playbooks to create initial drafts, suggest revised language that strikes a particular tone (e.g., accomplishes a goal with minimal edits to the original language, or the the most collaborative tone)

• Negotiation: Anticipate and prepare responses for the most likely pushback to your proposals, identify different ways of structuring an ask, and brainstorm win-win solutions

• Execution: Creating summaries for legal and business teams, aggregating obligations, and taking an initial pass at analyzing how consistent a potential business decision might be with the contract

Improving Operational Efficiency

Ever find yourself drowning in Slack messages about who should review a particular document? In a recent L Suite webinar, one GC explained how their team used AI to automate this workflow. First, they created a knowledge base to train AI on how to handle legal requests coming from the sales and customer success teams. 

They did this by using Slack’s API to download the last year of messages, then directed ChatGPT to identify the most frequently asked questions, their responses, and the person who usually responded. Then they used a Zapier Zap to review Slack for new requests from the sales or customer success teams, and then route those questions to the right internal stakeholders for review or approval. The team chose not to have their automation answer legal questions directly, but it does answer billing and finance questions, which adds to the significant time savings for the company.

Drafting and Redlining Contracts

My first legal AI use case was contract drafting, and this is where I believe the tool really shines, especially for leaner legal teams. With a seemingly limitless supply of contract precedents and language, generative AI is particularly well-suited to creating first drafts of agreements, and proposing alternate language for hairy provisions. To get the most out of your AI drafting, it is generally a best practice to train the model on a combination of your internal playbooks and precedents from prior contracts. If you don’t already have playbooks for the specific type of contract you are working on, working with AI to review your contracts and draft a playbook can be a great start. It can also be helpful to use AI to identify weak spots in any existing playbooks, either by comparing the playbook against the contract history, or by comparing playbooks generated by different AI models and vendors.

Pro tip: check that your commercial contracts have the latest AI-related language.

Prompting for Contract Use Cases

While prompt engineering is becoming both a vendor-provided tool and a sought-after job description, improving your prompts can significantly improve your outputs. For contracts, it is often best to give the AI a role to play, such as a senior commercial counsel, or the category of counterparty you are negotiating with. Then, include any relevant context, a description of the task, and as much detail about your desired format as possible. When analyzing clauses in contracts, it can be helpful to ask for the AI’s reasoning in IRAC format (Issue-Reasoning-Analysis-Conclusion), and for likely pushback and revisions. Finally, it can be useful to ask AI to help you improve your prompt, or feed the output of one model into a different model and ask the second model to identify weak spots. See here for insights about how to prompt for legal and more information about the L Suite’s prompt library and other AI-related offerings.

Avoiding Common Pitfalls

Three key risk management strategies when using AI in contract review are: establishing appropriate review workflows, prioritizing privilege and confidentiality, and maintaining appropriate documentation.

First, while AI can be quite accurate, its outputs will often need additional review. Identify the risk level of each task being delegated to AI, then match that to the appropriate person to review and be accountable for the work. Higher risk contracts like in hiring and firing employees will often need meaningful attorney review before they can be used, for both legal and ethical reasons.

Second, preserving attorney-client privilege or confidentiality of sensitive data may require more care on encrypting or redacting the data that is fed into the model. Seek out vendors and enterprise agreements that prioritize encryption and confidentiality where possible.

Finally, even the most seamless contract workflows need strong documentation and ongoing checks in the case of litigation, vendors changing their AI practices, and potential model drift. Establishing strong cross-functional oversight and accountability can help ensure AI compliance.

Final Thoughts

One of the most popular AI implementations in legal departments is to assist with contract workflows. AI can help both with automating high volume, low risk contract workflows, and by serving as a thought partner on more strategic analyses. Even small AI implementations throughout the full contract lifecycle can free up significant time, allowing legal teams and their stakeholders to focus on their most impactful work.