Redlining with AI: The New Playbook for Contract Review

Redlining Contracts: A Late-Night Revelation

I’ll never forget one particular night early in my legal career as a summer associate. It was past midnight, and I was hunched over a hefty contract, manually comparing it to an earlier term sheet. The office was silent except for the rustling of paper and my muttering every time I spotted a subtle change. I wanted to be a litigator, but here I was negotiating a settlement agreement line by line, eyes burning from toggling between documents. I remember thinking: there has to be a better way. Years later, as the CEO of Gavel, I often hear similar stories from small firm lawyers. Whether it’s a 50-page commercial lease or a startup’s financing agreement, the grunt work of redlining – spotting changes, enforcing your playbook, and revising language – can consume hours.

What if an AI could handle the busywork, and hand you a nearly-finished markup by the time you finish your coffee? In this whitepaper, I’ll share how that’s not just wishful thinking, but an everyday reality with tools like Gavel Exec. I’ll walk through the basics of legal AI in plain English, give you ten ready-to-use prompts for contract review, and even recount a realistic deal scenario showing how an AI “associate” works in practice. The goal is to help you redline contracts faster and more effectively – with less risk and zero loss of control. Let’s dive into the new AI-assisted playbook for contract review.

Primer: Legal AI Terms You Should Actually Know

Before we get to the fun stuff, let’s cover a few key terms and concepts. This isn’t a theoretical tech lecture – it’s a human-centered primer on how AI actually fits into our contract review workflow:

• LLMs (Large Language Models): These are the engines behind tools like ChatGPT and Gavel Exec. They generate text based on patterns in legal language—great at drafting, summarizing, and editing when given clear instructions.
• Prompting: This is how you tell the AI what to do. Precise prompts get better output—think “Redline this clause from the tenant’s perspective” instead of “Fix this.”
• Hallucinations: When AI makes things up. It might invent a clause or misstate a rule if it doesn’t have enough context. Always double-check critical sections.
• Context Injection: Giving the AI more info—like an LOI or prior contract—to improve accuracy. Gavel Exec lets you upload docs so the AI isn’t guessing.
• Playbooks: Your firm's rules—like standard fallbacks or deal breakers—turned into AI-checkable logic. Gavel Exec can flag anything outside your playbook in seconds.
• Token Limits: AI models can only “read” so much at once. Most tools handle 10–15 pages fine; long agreements may need to be broken into sections.
• RAG (Retrieval-Augmented Generation): This combines AI with real data from your documents. Instead of relying just on what the model was trained on, it pulls in relevant info (like your playbook or deal terms) live. That’s what powers Gavel Exec’s accuracy when comparing docs or enforcing standards.

That’s it – the ABCs of AI for contract review: LLMs that generate text, prompts that you control, the risk of hallucinations (managed by careful context injection and verification), playbooks to encode your expert knowledge, and token limits to keep in mind for document size. Now, let’s get practical and look at how you can put this to use today.

10 Gavel Exec Prompts You Can Use Right Now

One of the best ways to “level up” your AI use in day-to-day work is to start with a library of proven prompts. Below are ten real examples tailored to corporate and real estate contract redlining. I’ve focused on scenarios that small firm lawyers encounter regularly – and each prompt is written in plain language as you might input it into Gavel Exec’s chat sidebar in Word:

1. Compare a Draft to an Original (Lease vs. LOI)
   • ‍Prompt: “Compare the following lease to the attached Letter of Intent. Highlight any terms in the lease that deviate from the LOI and suggest appropriate redlines.”
   • This prompt tells the AI to do a side-by-side review, which is incredibly useful in transactions where you start with a term sheet or LOI. Gavel Exec’s reference document feature shines here: you can upload the LOI and literally ask, “Redline this lease based on the uploaded LOI”. The AI will output a list of changes or even mark up the lease for you, flagging rent amounts, security deposit terms, timelines, etc., that don’t match. No more manually cross-checking every clause – the discrepancies pop out instantly.
2. Rewrite an Indemnity Clause (Make it Balanced):
   • ‍Prompt: “The current indemnification clause is one-sided. Rewrite this clause in a more mutual way that protects both parties.”
   • Indemnity clauses are ripe for AI assistance because the language can be complex and there are known “market” versions. With this prompt, you’ll get a rewritten clause that, for example, extends indemnification obligations to the other party or adds carve-outs like fraud or gross negligence. It’s still crucial you decide what balance is acceptable, but the AI can do the heavy lifting of producing the alternate language. For instance, I’ve prompted Gavel Exec with “Make the indemnity clause mutual (each party indemnifies the other) and exclude indirect damages” and gotten a great first draft of a clause that would have taken me much longer to write from scratch. Tip: you can also ask the AI to explain the revised clause to ensure it captured the nuance – e.g., “Summarize the changes you made in plain English.”
3. Simplify an Assignment Provision:
   • ‍Prompt: “Rewrite the assignment clause in simpler, plain language, without changing its legal effect.” – Lawyers sometimes inherit contracts with archaic or convoluted language. This prompt uses AI as your translator from Legalese to English (or at least to clearer legal prose).
   • After running this prompt, you might get a cleaned-up clause that says, “Neither party may assign this Agreement without the other’s prior written consent, except that either party may assign to an affiliate or in a merger/acquisition,” instead of three sentences of whereas/wherefores. Gavel Exec is particularly good at this kind of rewrite because it has been trained on tons of legal text and knows how to preserve meaning. In fact, a built-in example is: “Rewrite this section in simpler terms.” – exactly what you’d use for a dense assignment clause.
4. Apply an Internal Playbook Check:
   • ‍Prompt: “Review this contract based on [Your Firm’s Name] playbook and mark any non-compliant clauses or missing provisions.”
   • This prompt assumes you’ve set up your playbook in the AI (which could be as simple as pasting in a checklist of items or having a trained model of your preferences). The AI will scan the agreement against those rules. For example, if your playbook says “governing law must be New York,” and the contract has Delaware, you’ll get a flag. Or it might insert a comment, “Assignment clause doesn’t include required change-of-control exception per playbook.” At the basic level, you can do this by providing bullet points of your policy as context. At a more advanced level, Gavel Exec actually lets you convert a checklist or term sheet into an automated playbook that will instantly redline the document according to your rules. This is like having a personalized QA engineer for your contracts. You remain the final judge – the AI just ensures nothing slips through the cracks according to your own standards.
5. Summarize Key Obligations for a Client: ‍
   • Prompt: “Summarize the key obligations and deadlines in Sections 3-5 in plain English bullet points. Assume the reader is a client, not a lawyer.”
   • Here you’re using AI to generate a client-friendly summary. Maybe your client doesn’t want to read a 30-page lease; you can quickly get a highlights reel. Gavel Exec can, for example, take an indemnification clause and output: “- Indemnity: Tenant must repay Landlord for any losses if something the Tenant does causes Landlord to be sued by someone else. However, the Tenant is not responsible for Landlord’s own negligence or misconduct.” That summary (which came from the prompt “Summarize the indemnification clause in client-friendly terms.”) is something you can then double-check and hand to a client to improve their understanding. It’s a great way to sanity-check complex language as well – if the AI summary seems off, that’s a clue to scrutinize the original text.
6. Identify Deviations from Market Standards:
   • ‍Prompt: “Review this purchase agreement and let me know which provisions are unusual compared to market standards for similar deals.”
   • This is like asking an AI second opinion: “Is there anything in here that jumps out as not market?” Maybe the AI notices that most leases of this type don’t allow the landlord to terminate at will, or that usually in a Series A financing, the investors wouldn’t get 3 board seats. Gavel Exec has been trained on vast amounts of transactional data, so it can often flag a clause that seems off. For instance, Gavel’s AI might note, “The non-compete duration of 5 years is longer than typical; market standard is 1-2 years.” This gives you a heads-up on points of potential pushback. Of course, “market standard” isn’t gospel – but having that intel at your fingertips is like instantly consulting a dozen colleagues who’ve done similar deals. (Fun fact: Gavel Exec’s built-in playbooks include market benchmarks for many deal types, so it can actually auto-markup a doc to move it toward typical terms.)
7. Generate a Missing Clause (from Examples):
   • ‍Prompt: “Insert a standard ‘Limitation of Liability’ clause here, based on typical tech service contracts, capping liability at 1x fees and excluding indirect damages.”
   • We’ve all encountered an agreement that inexplicably has no clause for something important. Rather than hunting through your old files for a precedent, you can ask the AI to draft it on the fly. The prompt above will yield a solid starting clause (which you can then tweak). The remarkable part is the AI can blend your specifics (1x fees cap, no indirect damages) with the style of the document at hand. Gavel Exec, for example, will draft in the same tone and formatting as the rest of your contract. This is a huge time-saver when you realize “Oh no, they forgot the arbitration clause” or when a client says “Can we add a confidentiality section?” You get a professionally phrased clause instantly rather than writing it from scratch.
8. Make an Aggressive/Conservative Redline Pass:
   • ‍Prompt: “Redline this agreement to be extremely [buyer-friendly].”* (Or seller-friendly / tenant-friendly / investor-friendly, etc.)
   • This prompt is like telling the AI, “Give me a markup as if I were representing the most aggressive client on my side of the deal.” It will go through and suggest a suite of changes favoring your side. For a lease on the tenant side, an “aggressive” redline might, for example, insert an early termination right for the tenant, strike any mention of automatic rent escalators, cap the indemnity, etc. You might not accept all of these, but it’s a quick way to see the full spectrum of asks. It ensures you’re aware of what an aggressive stance could be. In Gavel Exec’s chat, you could literally say “Apply redlines for a strong investor preference.” or “Make this clause more buyer-friendly.” and it will do it. This can be educational too – lawyers early in their career can learn what “buyer-friendly” vs “seller-friendly” looks like by seeing the AI’s redlines suggested for each. And if you think it went too far, you remain the boss: simply dial it back by telling the AI, “That’s a bit much; keep the liability cap but drop the request for personal guarantee.”
9. Spot Inconsistencies or Undefined Terms:
   • ‍Prompt: “Check the document for any defined terms that are never used, or terms that are used but not defined, and list them.”
   • This is more of a cleanup or quality-control task. It’s the kind of thing an associate might do at 2 AM when polishing a draft – ensure all definitions line up, section references aren’t broken, no exhibit is missing, etc. The AI can do this far faster. With Gavel Exec in Word, I can ask something like the prompt above and get a quick report: e.g., “‘Effective Date’ is defined in section 1 but never appears later; ‘Applicable Law’ is used in section 12 but not defined; Sections 5.3 and 5.4 reference an Exhibit C, but no Exhibit C is attached.” It’s like running a spell-check, but for contract consistency. While not a traditional “redline” function, it’s absolutely part of a solid contract review, and it’s a task AI excels at (pattern recognition across a long document).
10. Add Explanatory Comments for Client/Opposing Counsel:
    • ‍Prompt: “For each major change in the redlined document, add a brief comment explaining the reason for the change in plain language.”
    • This prompt leverages AI to do some of the tedious explanatory work. Often when we send marked documents to the other side or to a client, we add comments: “Added for clarity”, “Standard carve-out, as discussed”, “This term is non-standard; seeking to align with market practice”, etc. You can have the AI draft these comments for you after it generates redlines. Gavel Exec can not only mark up the document, but also draft accompanying comments to opposing counsel or to your client. For example, if it redlined a warranty clause, it might add a comment: “Revised to limit the warranty to 12 months, which is standard in similar agreements to prevent indefinite liability.” These AI-generated comments are a great starting point – you can refine tone or specifics, but it saves you from writing the same justification over and over. And it ensures your client understands why a change was made, enhancing transparency. (Internally, it also forces the AI – and you – to double-check that each redline has a rationale you’re comfortable with.)

Each of these prompts is like a power tool in your toolbox. They’re not pie-in-the-sky ideas; they’re things you can literally copy-paste into Gavel Exec (or a similar LLM-powered assistant) and get results today. The more you practice crafting prompts, the more you’ll develop your own playbook of go-to AI moves. Remember, you can always refine the prompt and run it again if the output isn’t spot on. It’s an iterative conversation. Next, let’s see how this actually plays out in a real-life scenario – from receiving a contract to sending out a revised draft – with AI as part of the team.

A Day in the Life: Using Gavel Exec on a Real Estate Deal

To illustrate the workflow, let’s follow a small firm lawyer, “Alice,” through a realistic transaction. Alice represents a startup tenant, and they’re negotiating a commercial office lease. They signed a Letter of Intent a few weeks ago, and now the landlord’s attorney has sent over a 40-page lease draft. Alice could manually redline it, but instead she fires up Gavel Exec in Word to help. Here’s how the process unfolds:

1. Opening the Document with AI at Hand: Alice opens the landlord’s lease in Microsoft Word. She has the Gavel Exec add-in installed (it lives in Word’s sidebar). With one click, she pulls up the Gavel Exec panel next to her document. She’s essentially got a smart co-pilot ready to go on the same screen. The interface looks like a chat window where she can ask questions or give instructions, and a menu for running her playbooks. (If you’ve seen modern Word plugins, you can picture it – a clean sidebar with a prompt box at the bottom, and options for uploading reference docs or selecting playbooks.)

2. Context Injection – Uploading the LOI: Before asking the AI anything, Alice provides context. She uses the “Reference Document” feature to upload the signed LOI that outlined the key deal terms. In the chat, it now shows that an LOI.pdf is attached as context. By doing this, Alice ensures the AI knows the “deal” and can catch any discrepancies. This is a crucial step – rather than trusting the AI’s general training, she’s feeding it the exact term sheet both parties already agreed on.

3. “Find What’s Missing” – Prompting a Comparison: Alice types: “Redline this lease based on the attached LOI. List any terms that don’t match the LOI or are missing, and suggest edits.” She hits send. Under the hood, Gavel Exec’s AI quickly reads both the LOI and the lease. Within about 30 seconds, the chat populates with a response. It’s a rundown of differences, essentially an issues list:

• “Rent: LOI stated $5,000/month, but lease has $5,500/month – suggest changing rent to $5,000.”
• “Security Deposit: LOI said two months’ rent ($10k) but lease lists $15k – consider revising deposit to $10k.”
• “Lease Term: LOI: 3 years; Lease: 5 years – possibly an error, flagging for review.”
• “Option to Renew: LOI included one 3-year renewal option; lease draft has no renewal clause – suggest adding a renewal clause per LOI.”

And so on. Alice is impressed – in one go, the AI spotted four or five substantive inconsistencies. It also suggested language for adding the missing renewal option, pulling phrasing from common practice. Essentially, it gave her a redline markup: inserted a new section with the renewal terms from the LOI and highlighted the rent and term for correction. Alice clicks “Apply All,” and Gavel Exec inserts those changes into the Word document (with Track Changes on). Just like that, major LOI alignment issues are handled. Alice, of course, double-checks each against the LOI – and they all align perfectly. She’s still in control, but saved a ton of time.

4. Running a Built-In Market Playbook: Next, Alice wants to ensure the lease is reasonable by market standards, not just matching the LOI. She switches to the Playbook tab in the Gavel Exec sidebar. There, she selects the “Tenant-Favorable Commercial Lease” playbook – one of the built-in options Gavel provides, developed with experienced real estate attorneys. (This playbook knows common tenant asks and typical market clauses for office leases.) She clicks “Run Playbook.”

In moments, the AI produces a set of redlines and comments throughout the lease, all following the playbook’s rules. For example:

• It inserted a cap on CAM (Common Area Maintenance) charges at 5% annual increase, with a comment: “Added cap on CAM per tenant playbook (protects tenant from large year-over-year increases).” This is something many tenants negotiate, and the playbook knew to apply it.
• It flagged the assignment clause with a comment: “Playbook: allow assignment to affiliates without consent. Suggest adding that exception.” The lease had a blanket consent requirement, which is slightly unfavorable to the tenant, and the playbook rule spotted it.
• It struck a sentence in the indemnity clause that made the tenant indemnify the landlord for the landlord’s own negligence (a big no-no). The playbook automatically removed that and added a note: “Removed Landlord negligence from Tenant’s indemnity (not standard for tenant to cover Landlord’s negligence).”

Alice reviews these playbook-driven changes in the redline. It’s like having a senior real estate lawyer whispering in her ear about what to fix. She doesn’t accept all of them blindly – for instance, she knows this particular landlord never agrees to a CAM cap above 3%, so she tweaks the AI’s inserted 5% to 3%. But the heavy lifting is done. In a few minutes, Alice has advanced the lease to a much more tenant-friendly state and documented the rationale behind each change with comments. Importantly, she’s in control: she reviews each suggestion. The AI’s suggestions were based on standard positions, but Alice applies her client’s specific goals on top.

5. Clause-by-Clause Refinement via Chat: With the broad strokes done, Alice now zeroes in on a few tricky clauses for fine-tuning. One is the “Default and Remedies” section, which is three pages long and full of legalese. Alice highlights a particularly dense paragraph (about five default scenarios) and in the Gavel Exec chat types: “Summarize this default clause in plain English, and suggest if any part of it poses unusual risks to the tenant.” The AI quickly outputs a summary: “If the tenant fails to do X, landlord can do Y... etc.” and notes that one provision is a bit harsh (the landlord can accelerate the entire rent for minor breaches, which is not common). This jibes with Alice’s own impression. She asks a follow-up in chat: “What changes would make it more tenant-favorable without likely rejection?” The AI suggests a softer remedy (no acceleration for non-monetary defaults until 30 days opportunity to cure, for example). Alice likes that and manually incorporates a version of it into her redline, using the AI’s suggestion as a template.

She then scrolls to the insurance clause. It’s full of jargon and specific limits. Alice isn’t an insurance law expert, so she asks the AI: “Is the insurance coverage specified here typical for an office lease of this size? Anything missing or excessive?” The AI responds: “The required liability coverage ($1M per occurrence, $3M aggregate) is typical. Requiring the tenant to waive subrogation is standard. However, the lease lacks mutual waiver of subrogation by the landlord – consider adding that so landlord also waives claims covered by insurance.” Great point – a mutual waiver of subrogation benefits her client. Alice adds a comment in the document to request that change from the landlord. (She could have the AI insert the exact clause, but she decides a comment is sufficient for this point.)

Finally, Alice tackles the rent escalation clause. It’s a bit confusing in wording. She types: “Rewrite the rent escalation clause in clearer language, keeping the 3% annual increase, and add a cap that it never exceeds 3% even if CPI is higher.” In a flash, the AI revises the clause, simplifying some convoluted formula and explicitly stating the 3% cap. She inserts that as a redline. It reads much cleaner and her client will appreciate the predictable cap.

6. Quality Check and AI Double-Check: Now the lease is heavily marked up with changes (in Track Changes) and sprinkled with comments explaining them. Before sending it out, Alice does a final review. She uses one more AI prompt as a safety net: “Scan the document and list any places where the tenant might still be over-exposed or any clause that a tenant-side attorney would typically push back on that we haven’t addressed.” Essentially, “did I miss anything?” Even after all that, the AI flags one item: “The guarantee clause makes the startup founder personally liable. Tenant attorneys often try to limit or remove personal guarantees. Consider if this aligns with your client’s risk tolerance.” Alice had indeed overlooked the personal guarantee section because she was focusing on the main lease. Now, she discusses with her client and they decide to propose a limited guarantee (maybe just for the first year’s rent). She adds that to her markups as well. It’s a prime example of how the AI, with its wide lens, can act as a backstop so nothing important is missed. The AI essentially acted like a diligent associate doing a second review of her work.

7. Delivering the Marked Draft – With Confidence: Within a couple of hours, Alice has taken a landlord-friendly lease to a balanced document ready to send back. She’s used the AI to compare against the deal memo, enforce her client’s playbook preferences, translate and refine complex clauses, and ensure consistency. All the while, she has exercised her own judgment on which suggestions to keep. The final product is her work product – just accelerated and augmented by AI. When she emails it out, she includes a note to the opposing counsel highlighting a couple of major changes (the AI even helped draft those explanations).

From the outside, what the landlord’s lawyer sees is a thorough, well-reasoned redline that looks like Alice spent days crafting. In reality, it was one afternoon, and Alice was able to make a soccer game with her kids that evening instead of pulling another late night.

This story shows AI in a supporting role. The AI didn’t magically produce a perfect lease on its own – it needed Alice’s prompts and guidance at each step. But it dramatically reduced the manual effort and enhanced the quality of the review. It’s like she had a tireless junior attorney plus a market research guru plus an editor all in one. The result: a faster turnaround for the client, a stronger negotiating position, and a less stressed lawyer.

(As a side note, imagine the competitive advantage here: a solo or small firm lawyer can go toe-to-toe with BigLaw speed when they leverage these tools. The AI helps “level the playing field, especially when a small firm is negotiating against a larger team” by handling the rote work and surfacing issues. And because Gavel Exec is built into Word, you’re not having to export documents to some external system – you stay in the zone where you’re comfortable, with Track Changes and Word comments like you’ve always used.)

Checklist: How to Review AI Outputs (Without Losing Control)

Using AI to draft and redline is powerful, but don’t let the novelty distract you from the fundamentals of good lawyering. Here’s a practical checklist to make sure you, the lawyer, remain in the driver’s seat:

• 🔍 Always Verify Critical Edits: Treat AI suggestions as you would a junior associate’s work – trust, but verify. If the AI redrafted an indemnity clause or added a new section, read it closely. Make sure it accurately reflects the deal and doesn’t introduce legal issues. The AI can save you a first draft, but it’s on you to give the final “OK.”
• 🎯 Check for Accuracy (No Hallucinations): If the AI output references laws, definitions, or facts, double-check them. Ensure that any clause it “pulled in” actually exists in the context (or is standard in the jurisdiction) and isn’t something made-up. In our example, if Gavel Exec had cited a specific statute for insurance or used an unusual legal term, Alice would verify that before sending out the draft. Generative AI can sometimes fabricate specifics, so maintain a healthy skepticism for anything that you didn’t explicitly provide in the prompt.
• ⚖️ Make Judgment Calls on Strategy: AI doesn’t know your client’s business priorities or what’s a “throwaway” vs. a “dealbreaker.” So review the AI’s redlines in light of the client’s goals. Maybe the AI flagged a minor point that your client truly doesn’t care about – feel free to drop it. Conversely, the AI might not flag something that your client did mention was important. Your human judgment overrides AI priorities every time. Use the AI as a sieve to catch issues, but then apply your strategic lens to decide which battles to fight.
• ✂️ Edit the Tone and Wording: Sometimes AI-drafted text, while technically sound, may need a tweak in tone. Perhaps the suggestion came off a bit too harsh, or not firm enough. Before finalizing, edit the AI’s language to match your (or your firm’s) style. For instance, if a comment to opposing counsel sounded preachy, you might soften it. Maintaining consistency in voice ensures the other side doesn’t feel a robot jumped in – it’s all coming from you, the attorney.
• 🔑 Protect Confidentiality & Sensitive Info: Make sure you’re using AI in a secure way (this is more of a process check). Gavel Exec and similar legal-specific tools are designed with confidentiality in mind – they often run on secure servers or allow on-prem solutions. But as a rule, never paste highly sensitive or privileged info into a consumer-grade AI tool that isn’t vetted. Use trusted platforms (or features like Gavel’s secure environment) for client data. Basically, apply the same common sense you would with any cloud software: keep client data safe and within approved tools.
• 💾 Keep a Human-Readable Trail: After using AI, you should be able to explain every change in the document. Either via comments or a quick summary memo, document the key alterations. This isn’t because the AI might vanish – it’s to ensure you internalize and remember the rationale. If a partner or client asks “why did we delete that section?” you want to answer from your own understanding, not “the computer told me so.” Reviewing the AI’s work and making notes solidifies that you agree with it. In fact, I often use the AI’s own explanations as a starting point for these notes, as they tend to be clear and neutral.
• 🗑️ Know When to Toss and Try Again: Not every AI attempt will be gold. Sometimes you’ll get an output that’s off-base or not useful. Don’t be afraid to hit delete and re-prompt with a refined question. For example, if you ask for a summary and it’s too verbose, you can say “summarize in 3 bullet points” and run it again. Or if a rewrite missed the mark, provide a bit more guidance (“make it one paragraph” or “use simpler terms”) and try once more. Iteration is part of the game. You’re not wasting paper or a secretary’s time – the AI is happy to oblige until it’s right.

By following this checklist, you ensure that AI remains a tool, not an actor, in your practice. You maintain control over the substance and quality of the work. The end result should always be something you’re proud to put your name on, with the AI having been the helpful assistant that made it easier.

Scale Yourself, Don’t Replace Yourself

When I talk to fellow attorneys about AI, I often get asked, “Is this thing going to replace lawyers?” Let me be clear: No. What it’s going to do – and is already doing – is replace certain tedious aspects of lawyering. And thank goodness for that! I don’t know any small firm lawyer who woke up excited to compare PDFs line-by-line or re-type the same clause for the 100th time. AI automates those tasks, so you can focus on the higher-value work: advising clients, negotiating business points, crafting deal strategy, exercising judgment.

Think of AI as a force multiplier for lawyers. It’s like having an army of capable (albeit imperfect) interns who work at superhuman speed. You’re still the partner supervising them. With AI, a solo practitioner can review as many contracts in a day as a whole team might have done before. You can take on more deals or close them faster, effectively scaling your impact without needing to sacrifice sleep or hire an army of associates you can’t budget for. For a small firm, that means staying competitive – you can go up against BigLaw in an M&A negotiation and not be the bottleneck on turnarounds. It’s a confidence booster for you and your clients.

Importantly, using AI doesn’t mean ceding control or quality. As we’ve emphasized, you remain the final decision-maker. In our story, Alice used Gavel Exec to augment her work, but every change was ultimately her call. The AI didn’t invent client strategy or cut corners – it followed her lead. In my experience, when lawyers integrate AI thoughtfully, the end product is actually better than it would have been purely by human effort, because the AI catches minutiae we might miss and provides suggestions we might not have thought of. It’s like having a safety net and a springboard at once.

I’ll also note the obvious: AI doesn’t have legal ethics or a practicing license. It won’t appear in court for you, it won’t sign off on a deal, and it won’t shoulder malpractice liability. Those are squarely on us as lawyers. Which is to say – our role isn’t going away. If anything, it’s elevated: we get to apply our human creativity, empathy, and advocacy with fewer distractions. We get to spend more time thinking about how to win for our clients and less time proof-reading schedules or compiling diligence lists.

At the end of the day, “Redlining with AI” means you have a new playbook – one where man and machine collaborate. It’s not sci-fi; it’s here now, and it’s remarkably easy to slot into your existing practice. If you’ve dabbled with ChatGPT, consider Gavel Exec your upgraded, lawyerly version of that experience – one that understands our documents and sits right inside Word, where you already work. My advice is simple: give it a try on your next deal. Even if you start with just one or two prompts from the list above, you’ll get a taste of how much time and sanity it can save you.

As someone who left the grind of big-firm life to build legal tech, I genuinely believe this technology can make practicing law more enjoyable. It automates the rote, reduces risk (through consistency and comprehensive checks), and helps you deliver results faster. And happy clients who get quick, thorough service are a boon to your business.

So, the next time you open a contract and sigh at the thought of redlining it, remember: you’ve got an AI assistant in your corner now. Embrace it. You’re not giving up control – you’re gaining an edge. This is the new playbook for contract review, and you as the lawyer are still the quarterback calling the shots. The AI is just your MVP teammate, ready to execute your game plan and help carry you across the finish line.

— Dorna Moini, CEO of Gavel (and former sleepy contract-reviewing attorney who is very glad AI is here!)

‍