Key Takeaways:
- Define your specific use case and build a comprehensive requirements scorecard before evaluating any AI redlining tools.
- Create your own accuracy test with a challenging contract since vendor-reported accuracy percentages vary widely in methodology.
- The best tools offer white-glove service, lawyer-supported playbook creation, and flexible one-year terms to drive user adoption.
- Plus, a free Sample AI Redlining Requirements List is included!
According to a recent poll, 56% of in-house attorneys regularly use AI during the contract review process. Some of them are using a base LLM model while others are using dedicated AI redlining tools. What’s clear is that AI is becoming increasingly integrated into the attorney review workflow. But is the attorney workflow being streamlined or disrupted? That depends on what you’re trying to solve for you and what solution you choose.
Contract redlining has long been the most manual, time-intensive phase of the contract lifecycle, with Microsoft Word’s Track Changes as the only tool in a lawyer’s arsenal. While AI redlining solutions have proliferated over the past two years, confusion persists about when to deploy these tools, how to implement them effectively, and how they integrate with existing technology stacks.
Recently, I evaluated 12 different AI redlining tools over the course of four weeks, conducted in-depth testing with three finalists, and ultimately selected a solution that met my specific requirements. While I cannot disclose which vendor I ended up going with for confidentiality reasons, I can share my approach to the evaluation and selection process.
Different Types of AI Redlining Solutions
Before initiating your vendor evaluation, it’s essential to understand the AI redlining landscape. There are many different options out there, but which one is best for you and your team?
LLM Base Models (OpenAI, Copilot, Claude, etc.)
If your organization maintains an enterprise license for these platforms, they excel at issue spotting and negotiation preparation but are not optimized for redlining. While Microsoft Copilot integrates with Word, these tools aren’t purpose-built for contract redlining workflows. Users still engage in substantial copy-pasting, reformatting, and manual comparison to incorporate AI recommendations into redline drafts. These tools inadvertently return lawyers to the administrative work we’re seeking to eliminate.
Do-It-All AI Platforms for Lawyers (Harvey, GCAI, etc.)
According to contract management pro Lucy Bassli, “Legal AI generalist platforms all have one thing in common: contract automation is their top use case.”
These platforms offer broad legal AI capabilities across multiple functions, including research, drafting, and contract review, typically integrated with Word, Outlook, and SharePoint. They’re well-suited for General Counsels and legal leaders who oversee diverse legal functions without deep specialization in any single area, as well as first legal hires and solo practitioners managing comprehensive legal responsibilities. While these platforms provide extensive features, they typically don’t match the depth of contract redlining capabilities found in standalone tools. The optimal choice depends on your organization’s needs and the balance between contract work and other legal functions.
Standalone AI Redlining Tools (Spellbook, LegalOn, Wordsmith, etc.)
These solutions specialize in contract redlining with sophisticated features including custom playbooks, clause libraries, issue spotting, and risk grading. They’re ideal for contract-focused legal teams managing high agreement volumes. These tools concentrate exclusively on contracts and typically don’t include intake forms, workflow management, or e-signature capabilities—features that would classify them as CLMs. Most function as Word Add-Ins, displaying a sidebar interface for AI interaction alongside the document.
CLM Native AI Redlining Modules
Some (though not all) CLMs now offer AI redlining capabilities comparable to standalone tools. If you currently use a CLM, begin by inquiring about existing or planned AI redlining features. Some CLM providers may be pursuing acquisitions of AI redlining technology. Given the substantial value of integrating your AI redlining tool with your CLM, this should be your starting point if you have an existing CLM platform. If your CLM lacks AI redlining capabilities or won’t deploy them within six months, seek standalone AI redlining tools with CLM integration capabilities via API or other methods. Most AI redlining vendors offer one-year terms with renewal options, allowing you to transition to your CLM’s native solution if it becomes available and competitive.
Prepare for Your Evaluation
Before scheduling a single demo, you should understand your specific use case and build a comprehensive requirements list. This preparation work will save you significant time and ensure you’re evaluating AI redlining tools against criteria that actually matter to your team.
Benefits of Using AI Redlining Tools
AI redlining tools solve different problems for different legal teams. These are some common benefits addressed by AI redlining tools.
- Third-Party Paper Review: This was our primary use case at Franklin Templeton. Our legal contracting team manages a high volume of buy-side commercial agreements, ranging from non-disclosure agreements and MSAs to DPAs and AI addenda. We rarely get to use our own templates because we’re usually reviewing counterparty paper. AI redlining tools excel at this use case by allowing you to create AI-generated playbooks from your templates and then automatically redlining third-party paper against those playbooks.
- Template Refinement and Standardization: If your organization uses its own templates most of the time, AI redlining tools can help ensure consistency across your template library. The AI can identify deviations from your standard language, suggest updates when clauses become outdated, and help maintain uniformity as multiple lawyers make template edits over time.
- Cross-Border Contract Review: For legal teams handling international agreements, AI redlining tools with multilingual capabilities can accelerate contract reviews in multiple languages while flagging jurisdiction-specific issues and ensuring compliance with regional regulations.
- High-Volume Contract Review: Legal teams managing hundreds or thousands of similar agreements (like vendor contracts, employment agreements, or sales contracts) can use AI redlining to handle first-pass review at scale, allowing lawyers to focus on outliers and high-risk terms.
- Training and Knowledge Transfer: Some teams use AI redlining tools to help junior attorneys or contract administrators learn what to look for in agreements. The AI’s explanatory comments serve as real-time teaching moments about why certain clauses matter and what alternatives exist.
Understand Your Use Case
As stated by legal tech expert, Colin Levy, “Legal tech rarely fails because of missing features. It fails earlier than that. It fails when teams buy solutions before agreeing on the problem.” I wholeheartedly agree with this statement. Which is why I recommend that you spend time meeting with your teammates, colleagues, leadership, and cross-functional counterparts to understand problem areas.
Laura Belmont, General Counsel, also recommends: “Be ruthless about diagnosing your problem. Don’t even think about the solution (or “a” solution) until you truly understand what your problems are.” I love this advice. For example, where is there friction in your workflows, which agreement reviews are causing bottlenecks, what are some areas that attorneys wish they weren’t spending their time on, etc? Then, and only then, you can begin to build your requirements list.
Create Your Requirements List – Sample Requirements List Provided
Before scheduling demos, talk to your users. What challenges do they face? Which contract types dominate their workload? How frequently do they review third-party paper? These insights will inform your requirements framework.
You can access a Sample Requirements List here. Remember that this is just a starting point and you should customize it based on your team’s needs.
These are some essential categories to evaluate when developing your requirements list:
- Integration Capabilities: How does the tool integrate with Microsoft Word? Does it function as a Word Add-In? Can it integrate with your CLM or other systems via API? How does it handle various document formats, including scanned documents, native PDFs, and Word files?
- Playbook Features: Can the tool convert your contract templates into playbooks? Does it support precedent-based learning from previously executed agreements? Can you create custom playbooks with AI assistance? Does the vendor provide playbook creation support? Are lawyer-created starter playbooks included?
- Redlining Capabilities: Does it perform word-level edits rather than wholesale clause replacements? Can it detect subtle variations in standard clauses and defined terms? Does it support internal commenting and collaboration? Can users customize AI-suggested redlines before document insertion?
- Drafting: Can the tool draft new clauses and edits corresponding to identified issues? Does it draft complete agreements? Can you customize and override AI suggestions?
- Negotiation Support: Does it generate explanatory comments that sound natural and human-like? Can you insert these comments into documents with a single click? Does it provide contract summarization? Can the AI learn from negotiations and user feedback?
- Risk Management: Does it provide issue spotting with pass/fail indicators? Does it prioritize issues by risk level (high, medium, low)? Are issue justifications clear and actionable? Does it detect missing clauses?
- User Experience: Is the interface intuitive with minimal training requirements? How quickly can users achieve productivity? Can it handle various document formats effectively?
- Support Features: Does it offer multilingual support? Can it extract metadata from executed agreements? Does it include a chatbot for contract queries? What workflow management capabilities does it provide?
- Simple Implementation: What are the implementation costs and timelines? How many custom playbooks are included? Is user training included? What’s the pricing model—per seat or per contract?
- Customer Friendly Contract Terms: What are the contract terms—can you execute one-year agreements without auto-renewal?
- Security: Does the vendor train on customer data? What compliance certifications does it maintain (SOC, ISO)? Does it offer data masking for sensitive information?
- Additional Capabilities: What unique features does this vendor offer? How does it differentiate itself in the market? Can it handle high volumes consistently?
Once you have a good idea of the requirements you’re looking for, build your scorecard using these categories, prioritizing areas based on your team’s specific needs. For example, my non-negotiable requirements were:
- Microsoft Word Add-In
- Auto playbook creation from my template
- AI-generated redlines based on my playbook
- AI-generated explanatory comments
- Distinguishing between internal and external redlines
- White glove service with playbook creation support
The Evaluation Process
Once you’ve built your requirements list, it’s time to see these tools in action and test them against your real-world needs.
Set Up Demos Strategically
I completed all 12 demos within four weeks, conducting approximately three per week. I recommend condensing your timeline so demos remain fresh for comparative analysis, and you evaluate all tools within the same product development cycle. This market evolves rapidly.
That said, I don’t recommend evaluating as many tools as I did. Most solutions offer substantially similar capabilities. Begin with three to five demos and narrow your selection from there.
- Using Your Requirements List During Demos: Bring your requirements scorecard to every demo. I found it most effective to fill out my scorecard either during the demo or immediately afterward while everything was still fresh. For each requirement category, I used a simple rating system: “Yes” if the tool met the requirement fully, “Partial” if it met it with limitations, and “No” if it didn’t have the capability. I also added brief notes about standout features or concerns. This approach allowed me to compare vendors objectively rather than relying on memory or subjective impressions.
- 30-Minute Initial Demos: Don’t accept vendors’ standard presentations. Specify what you want to see, what you don’t want to see, and your time constraints. I recommend 30-minute initial demos. I conducted these independently as head of legal operations to maintain evaluation velocity without coordinating multiple calendars.
- 60-Minute In-Depth Sessions with Your Short List: Narrow to two or three tools for thorough evaluation. At the session’s start, provide one of your templates (don’t send it in advance to prevent preparation). Watch them go through the demo live. Specify your priorities: playbook creation, playbook-based redlining, and issue spotting. This is when I brought in the attorneys who would actually be using the tool.
- Sign Up for a Free Trial or Pilot: Most vendors offer complimentary trials or pilots for limited terms. You can either access the platform directly to run the tests yourself or request vendor-facilitated screen shares while they run your test case. They can extract and provide redline results for your independent analysis.
Measuring Accuracy – Create Your Own Test
This phase transitions from evaluation to validation. Design a test case addressing your primary needs. You define success metrics and measurement methodology.
Legal tech vendors usually boast a very high accuracy percentage for their AI tools. But when I dug a little deeper, I realized quickly that the accuracy percentages were more of a marketing strategy than a true representation of accuracy. For example, when I asked which part of the functionality it was measuring accuracy for, most of them explained that it was measuring the accuracy of the issue-spotting feature. How good was the AI at spotting issues in an agreement, identifying missing clauses, or clauses that were very one-sided? So rather than measuring the accuracy of the entire tool, it was measuring the accuracy of one feature of the tool. Then I asked how they got to the number, and each of them had a different way of measuring. It was like comparing apples to oranges. So I decided to create my own apples-to-apples method of measuring redlining accuracy for myself.
Here’s my test case, the third-party NDA review test:
- Take your NDA template.
- Request the AI create an NDA playbook using the template.
- Without any playbook modifications or review, request it apply the playbook to review and redline a third-party NDA. For the third-party NDA, I deliberately selected and customized a challenging document. I introduced typos, removed section headings that serve as AI hints, deleted clauses the NDA should contain to test missing clause detection, and added undesirable clauses not addressed in my template (such as a residuals clause).
- Have a team member redline the same third-party NDA and record the time investment. Use their redlines as the benchmark or answer key.
- Compare AI redlines to human redlines. I evaluated:
• Accuracy of issue spotting
• Tone of AI-generated comments (natural and human-like quality)
• Precision of redline markups (word-level edits versus broad clause replacements) - Measure timing. This is a strong selling point for AI redlining tools: reducing attorney review time. It is also a data point that you’ll want to track and report to leadership as a way of capturing success. Remember that AI review time isn’t simply the duration from initiating the AI to receiving first-draft redlines. It’s the complete time from initiation to final redlines ready to send back out.
This test enabled me to narrow the field further and identify my top choice.
What Actually Differentiates These Tools
Approximately 90% of features are the same across these AI redlining tools. After evaluating 12 solutions, here’s my perspective: I believe AI-as-a-service represents the optimal approach as we’re at the beginning of an AI contracting revolution. I don’t recommend DIY implementations—that approach typically results in lower user adoption. The differentiators I’m highlighting share a common theme: they significantly increase the likelihood of achieving total user adoption by the end of year one.
White Glove Customer Service: This element carries more weight than many anticipate, and it’s critical for driving adoption. Seek vendors offering comprehensive onboarding support, reporting dashboards, 24/7 customer service throughout the contract term, and complimentary ongoing user training. When your team encounters obstacles or has questions, immediate support maintains momentum and prevents the tool from becoming shelfware.
Lawyers on the Vendor Team: Having lawyers—not just engineers—on the vendor’s team who assist in building and refining your playbooks is invaluable. They understand legal nuances and can guide you through creating playbooks that authentically reflect how lawyers evaluate risk and approach negotiation. This expertise dramatically accelerates time-to-value and ensures your team trusts the AI’s suggestions from day one.
Implementation Approach: Leading vendors offer streamlined, complimentary implementations of playbooks with flexible contract terms. Seek one-year terms without multi-year commitments or auto-renewals. This flexibility is essential when adopting emerging technology—you need the ability to reassess as the market evolves without long-term commitment constraints.
Free Playbook Creation Support: Some vendors include unlimited playbook creation support within seat licensing. Others charge separately for this service. Given how critical playbooks are to extracting value from these tools—and to ensuring your lawyers actually use them—having this support included can significantly impact adoption rates.
Internal Redlining Capabilities: The ability to distinguish between internal versus external redlines and explanatory comments is valuable, especially if the tool can leverage Microsoft Word’s cloud version functionality for internal collaboration.
The market is evolving rapidly, and these tools are becoming increasingly sophisticated each quarter. What matters most is identifying a solution that aligns with your team’s specific workflow, volume, and use cases. More importantly, find one that your lawyers will embrace and use consistently. Use this framework to evaluate vendors systematically, but trust your team’s hands-on experience during testing. The optimal tool for your organization may not be the one with the most features. It’s the one that achieves full adoption and becomes integral to your team’s daily workflow.
Selecting the right AI redlining tool is just the beginning. In future articles, I’ll continue sharing my journey, including how we approached the playbook implementation process, our change management strategy to drive user adoption, and how we integrated the tool into our existing workflows. The evaluation framework I’ve shared here helped us make an informed decision, but the real work of transformation happens in the implementation phase. Stay tuned!
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