How to Apply AI in Legal Services Transformation

Being the tech-forward lawyer that I am, of course, always exploring what AI can do. BUT I also use AI with my eyes wide-open because I don't want to redo work and don't want to rely on inaccurate improper content. Here are a few specific use cases that have worked for me and a checklist for getting started responsibly: 1. Drafting Routine Agreements Faster Take a simple NDA or basic MSA. I have a template, but often need to make adjustments based on negotiations or other factors. I use Malbek's AI to generate a new draft by giving it given explicit guidelines. Because the AI integrates with Word, I can make edits directly and align the language with our internal templates. It’s not a final product—but it’s a faster starting point, and it cuts down on manual work without compromising review quality. 2. Creating Plain-Language Legal Summaries When I need to explain a complex regulatory update or court decision to our product team, I’ll often use AI to produce a first-draft summary. Again, I never rely on this output blindly—but it speeds up the initial framing. I rewrite it with the business audience in mind, but I’m no longer stuck staring at a blank screen. 3. Reviewing Vendor Contracts for Key Terms When we onboard new vendors, I often review lengthy agreements under tight deadlines. I’ve used AI to extract core clauses—termination rights, notice periods, auto-renewals—into a structured summary. This lets me validate critical terms quickly and spot potential issues without hunting through 20 pages of dense legalese. It doesn’t eliminate the need for review, but it helps me prioritize my time and focus. What I don’t do (yet): -I don’t let AI write legal arguments or regulatory guidance. -I don’t input confidential or proprietary data unless it’s inside an enterprise or securely managed tool. My Checklist: -Choose legal-trained AI tools like one within a CLM like Malbek's. -Use secure, vetted platforms—or redact sensitive data. -Treat AI like a junior analyst: helpful, but not infallible -Always validate legal conclusions before internal or external distribution The bottom line: AI isn’t replacing legal judgment—but it can extend our reach. If it helps me deliver work faster without cutting corners, that’s a meaningful win. If you're experimenting with AI in your legal function, I'd like to know: Where have you seen real value? Where have the risks outweighed the benefits? #legaltech #innovation #law #business #learning

In-house counsels didn’t go to law school to build systems. But that’s exactly what the role is evolving into. In the AI era, legal teams aren’t just reviewing contracts. They’re guiding automation, managing risk at scale, and building operational systems that touch every function from HR to finance to product. And that shift brings new demands: ➤ You can’t think in legalese anymore. You need to speak data, process, and product. ➤ You can’t just “review.” You need to build workflows that scale decision-making. ➤ You’re not just a subject-matter expert. You’re a cross-functional partner to Sales, Finance, and Procurement. In my latest article for Forbes, I break down what this transformation means for legal leaders and what companies must do to keep up. 𝗜𝘁 𝗯𝗼𝗶𝗹𝘀 𝗱𝗼𝘄𝗻 𝘁𝗼 5 𝗸𝗲𝘆 𝗶𝗱𝗲𝗮𝘀: 1/ Standardize contract templates and negotiation positions to reduce legal turnaround time. 2/ Implement legal intake systems to streamline and triage requests efficiently. 3/ Use AI tools for contract review, summarization, and data extraction to increase productivity. 4/ Track legal team performance using operational metrics like how early legal input on supplier contracts reduced dispute escalations by a certain percentage. 5/ Evaluate legal tech not on features, but on how well it integrates into daily workflows. If you’re a GC, Legal Ops leader, or CEO thinking about how legal can drive business, this one’s for you. Check out the full article from the link in the comments 👇🏼 How are you seeing the role of in-house counsels evolve in your org? #LegalTech #GC #LegalOps #AI #CLM #Forbes #InHouseCounsel #Leadership

How I Use AI as In-House Counsel: RFP Reviews Ever opened a request for a four document, 300 page RFP and feel your soul die a little bit? Yeah, same. RFPs are often dense, repetitive, and packed with questions buried in long paragraphs. But they also require fast turnaround and precise compliance. Here’s how I use AI to review and prep RFPs faster, smarter, and with less caffeine. 📤 Preliminary step: Upload & Organize  Upload the documents to the AI tool. As always you need to apply your own legal judgement as to whether this is safe and ethical to do. I use an enterprise-grade AI tool. RFP docs often do a lot of cross referencing. You may need to ask your model to organize the documents for you: “Review the attached RFP documents thoroughly. Suggest the order in which I should review the documents. Present your suggestion in a numbered list from the first document to review to the last.” 👀 Step 1: Extract the Questions First, I ask my AI tool to extract every question or requirement in the RFP. This turns a giant wall of text into a clean, numbered list. Huge time-saver. I may tell the AI model to particularly note if the RFP responses are binding and if there are instructions on how to present exceptions/redlines. 🧠 Step 2: Identify Legal Review Areas Next, I prompt the model to flag items that may need legal input—like data privacy terms, indemnification clauses, insurance requirements, or contract obligations. You can also keep it general and just prompt the model to list which documents require substantive legal review. ✅ Step 3: Draft First Pass Responses Where appropriate, I’ll have the model generate first-pass answers using a playbook, checklist, drafting guidelines, previous RFP responses, etc. Of course, you still need to perform your own review, but it beats starting from a blank page. 📊 Step 4: Draft a Business Email (optional) I then prompt the AI to draft an email to my business team detailing the issues I have flagged and asking for business review. I may prompt the model to include the flagged items in a table with a column for: 1) Each issue with a section reference 2) An explanation 3) A risk assessment 4) A blank column for Business to fill in with approvals/notes If there are a lot of redlines or flagged issues, including the table in the email to the Business team helps keep everything central and organized. 📝 Step 5: Drafting Exceptions Lastly, I take Business’s responses and upload them to the model, prompting the tool to use the responses to draft an exceptions document according to the RFP instructions. I may have to edit this document, but again, it saves more time than drafting from scratch.  Have you used AI for RFPs? Got a go-to RFP prompt? Drop it below. #ArtificialIntelligence #AIforLawyers #LegalTech #LegalOps #InHouseCounsel

Boosting Client Experience with AI-Powered Legal Chatbots Fielding basic client queries on legal processes, documents, and cases takes time away from higher-value legal work. AI-powered chatbots present a solution by automating simple interactions. Sophisticated natural language processing (NLP) models allow virtual legal assistants to interpret questions, analyze context, and provide instant answers on routine legal matters. For example, a chatbot can explain court procedures, summarize a contract's key clauses, or retrieve a case document when prompted. Unlike rule-based bots, generative AI chatbots can handle nuanced conversations and improve continuously through training on real-world client transcripts. Over time, they take on simpler queries while flagging complex ones for lawyers. The benefits are increased responsiveness for clients and greater productivity for legal teams. Chatbots also enhance accessibility for citizens requiring basic legal assistance. And they create opportunities to serve smaller businesses at lower cost. However, bots have limitations in understanding unique client circumstances and exercising legal judgment. Human oversight of responses is still crucial. Responsible adoption entails transparency on bot usage and combining AI capabilities with lawyer expertise. As algorithms evolve, AI-powered chatbots are primed to transform legal industry workflows and client service models. While promising, maintaining ethical implementation and human partnership remains key to fully realizing the benefits. P.S. Share your comments and thoughts below.