Name: Navigating the AI Transition: A Roadmap for Public Sector Teams
Uploaded: 2026-05-13T18:06:09.609Z
Duration: 50 min 8 s
Description: Navigating the AI Transition: A Roadmap for Public Sector Teams

Transcript for "Navigating the AI Transition: A Roadmap for Public Sector Teams": Well, welcome everyone to our next session of navigating the AI transition, a road map for public sector teams as they evolve in their usage of AI. We have a couple special guests here with us today, from the Ohio Attorney's General Office and the North Carolina Department of Justice as along with Everlaw's own Eddie Kim to talk about the varying stages that, these offices are in, as they explore their use of AI, at their agencies. Before we get too far along, on this journey, I do wanna give a disclaimer that, this presentation slides, and any answers to to questions, are the opinions of the presenters and, not formal opinions of the North Carolina Department of Justice, or the attorney general. Every situation is unique, and the information, contained herein offered or constructed as legal advice. Now for, the more fun conversation, an introduction to our panelists. Great. Kicking things off, Pam has been a paralegal since 2008, working with the North Carolina attorney general's office since 2011 as a state bar certified paralegal. Pam has been actively working in litigation, discovery, and ediscovery throughout her career at the attorney general's office with a focus on state agency defense litigation. She received her bachelor's degree from the State University of New York College at Oswego and her paralegal certificate from Syracuse University. Kate Lee Spadke is a senior litigation support list with the Ohio attorney general's office, bringing more than a decade of paralegal and ediscovery experience to her role. She supports complex multi district litigations, leads emerging AI pilot programs, and leverages a strong legal foundation built through her NALA certification and education at The Ohio State University and Columbus Community State Community College. Finally, Eberlaz's own Eddie Kim is a senior strategic AI, adviser and Eberlaz's AI evangelist. Eddie pairs over fifteen years experience in ediscovery and, forensics with his passion for leveraging emerging technology to transform legal workflows. Prior to joining Everlaw, he was the head of ediscovery analytics at an AMLO fifty firm where he led the initiative of applying machine learning into litigation and investigation processes. Before our panel kicks off, we want to get started with a brief, poll for the group, to kinda get a baseline of where everybody's at in in their individual AI journey. That poll should pop up on your screen now. And the question is, how would you describe your agency's current AI usage, as it relates to legal matters? Okay. Thanks for everybody for providing your perspective. With that, and the the wide range of AI use that that we're seeing from folks, want to formally welcome, Pam from the North Carolina Department of Justice. And, the the first question for, our our panelists and for you, Pam, is, you know, where are you on your your AI journey and how, can you give us a sense as to where your office is today and if you have a policy or or not, and, what has been most top of mind, for you in this AI age? Yeah. Well, thank you so much, Lauren. I'm happy to be here. And, yeah. I would say the thing that is top of mind for me is that AI is everywhere. Literally, you you see it on TV. You see it, on your phones. I mean, it it's literally everywhere. And the thing that's most top of mind for me and my agency, here in North Carolina is, getting a policy. For us, we are, you know, our office is in works of getting a formal policy, and we have, you know, an overarching larger branch of a a state AI policy, but we're looking for one specifically for agency and how to govern, where we need to point, our directives to and, like, something that we can use for our our current workflows. So, yeah, that is really, like, AI is moving so fast. It's really hard to stay on top of things. So the sooner that we can get a policy in place would be, the best for for us, and and I'm assuming that there are a lot of other agencies out there that are still, either, you know, in that very early on stage of, like, getting a policy or, you know, somewhere in between. But, yeah, what's top of mind for us is making sure that we have, the tools that we need and a policy that kinda governs where we need to go. Right. So when when you think about, you know, what once that that policy is is in place, you know, what are what are the next steps from from there? Right? Is it something that your, that there are experimentations or or pilots happening, in the interim? Kind of what is your approach so that you're you feel that you're ready once a policy is in place? Yeah. So that's a great question, and it's kind of like a chicken or egg situation. Right? Like, we need the policy to be able to implement things going forward, but these tools and this technology is so new that a lot of people are are not familiar with them. So what we're trying to do here in North Carolina is is try, like you said, to do a little bit of pilot projecting, having smaller, like, sandbox ish cases. So, for my specific case, we have, or for my specific, what I'm gonna be speaking on is a specific case that I've worked on, where I've got several levels of approval, throughout the agency for us to kind of pilot, certain areas and just kind of test it out. It's very, small, case for lack of a better term. And what we're using is trying to understand the tool and see what it can do and then try to develop workflow. So that way, once we get that policy in place, we will have kind of, like, a a basic road map of where to go. You know, it's it's almost like, you know, AI for for an analogy purpose. AI is kind of like a bit a blank word document or a break blank spreadsheet. Like, once you get it, like, it's great. It can do all these things, but unless you have a template or a go by or some kind of workflow already developed for your organization, it's it's really hard and can be overwhelming as to where to start. So for our office, specifically, what we're doing is taking those little baby step steps and making sure that we have the right, approvals that, you know, and the right checks and balances in place so that we can understand the tool. So when we get the policy ready and the policy is approved, we can hit the ground running and and set up our our office for success. So you you mentioned the the, sandbox and or those small experiments, that you've done to prepare once you have an AI policy. Are there any examples that you can give of, like, what's been most successful or what or even, like, what's been more informative of those experiments, in your use of AI and what you're anticipating, the benefits will be when you get approval? Yeah. Absolutely. So for my specific examples, we're use we're we have a very large case that's moving rapidly through discovery. And so when we're getting these documents in, you know, discovery happened, we have a very short turnaround time and depositions were noticed. We've got, you know, hundreds of thousands of documents to review. And then with depositions being noticed, we trying to get the documents into the system, Everlaw, and trying to get them out, you know, produced out of Everlaw and also, like, get the attorneys up to speed. So one of my thought processes was, you know, we're moving at such a high rate of speed, for some of these cases that, you know, for state employees specifically, time is money. And I know that's the case for for both public and private sectors. Excuse me. But we are, you know, we're always overworked and underpaid in state government specifically. So anything that we can do to save time. So my thought process when we're getting these documents in and we're trying to, you know, best use our resources to get the attorneys, up to speed, get them really queued in and and, like, pointed in the right direction is what we're we do is when I pulled in these documents, I was able to do some quick batch summarizations of some of these documents, put them in almost like a story builder area for the attorneys when they're getting to defend these depositions. So we're ingesting documents in, kind of getting auto summaries or batch summarizations for small areas of documents. It allows that attorney to pick up those documents, go to the story builder feature, kind of look over the summarization, and, you know, of course, do their own QC and and everything that they need to do on their end. But I I think it helps them to organize their, their workflow and their priorities when they kind of already know where the buckets of data are and how it's loosely organized with the with the assistance of an AI tool on the way in. And because this case is working so quickly, I really haven't had a chance to get any feedback from my attorneys on how this process is working. But I think holistically going forward, that could be some kind of, if not that, but some kind of, like, directive on where we will would wanna take some of our, like, incoming discovery and outgoing discovery to make sure that, you know especially with these fast moving, documents sets. Yeah. That's a a great example, and I think, definitely something that we hear over and over again. Right? Kate, you you shared a story recently, about using AI to to get one of your lead attorneys up to speed. Do you wanna share a little bit more about that? Yeah. Absolutely. So it was, you know, a little different than just going forward and and using it as the review platform that we usually do. We had an attorney, one of our deputy AGs, and she got called in to be basically second chair in a trial that was already five weeks in. And it was a really large case. It ended up being an eight wheel eight week trial. But she, you know, didn't know the nuts and bolts of everything. She had a large overview, but she went in and went through the corpus of documents with deep dive and basically learned what she needed to know in a fraction of the time. So she was then able to actually sit sit in chair and do what she needed to do with that tool, and it helped obviously substantially. And she's been preaching about it ever since too. That's great. That's great. We love to hear that. Appreciate that, Kate. I only have a a couple more questions here for you, Pam, to round out this part of this session. But, I think one of the the biggest hurdles or or fears that, our customers feel about using AI is what case they need to make or what they need to say in order to get approval. Right? I know you're developing a policy, and you had these, you know, small test cases. What was the argument that you made to get approval? It sounds like time savings. Was there anything else that, you used to make your case? Lauren, that is exactly, you know, the time saving you know, us as paralegals doing, you know, a lot of the groundwork for the attorneys. The time saving thing, especially when we're already short staffed, sometimes there's a lot of turnover in offices. That is exactly what we needed, in this specific instance to give us the clearance to say, yes. Go forward and try these tests, test cases and sample and and, like, you know, just kind of figure it out as you go, and and trial and error, essentially, because we we don't have any options. Time saving for us, and I'm sure that a lot of other agencies feel the same way. It's especially when you're under the gun from, the court when you have to get something done, and you just literally don't have enough resources, you have to start looking at other options. So, we were put in a position that, you know, with the help of these tools, we're we're most likely going to be able to meet our our upcoming dents, with, you know, this this assistance where, otherwise, we would have to probably go outside, you know, spend additional money, hire additional reviewers, like that kind of, you know, bigger picture things where we we just don't have the resources to to do that in this specific, like, this specific case. So, really, what got us over that hurdle was this the position that we were put in. You know? The the rest of the world is advancing with AI and, you know, if we don't jump on, you know, on the last, you know, part of this train as it's leaving the station, you know, we're gonna be far behind. So I just, you know, it's it came out of necessity. Well, I'm glad you were able to make the case and and get approval. And we're definitely looking forward to hearing the the feedback from your attorneys once things slow down and, you can get a little bit more insight into their. One final question, before we we transition. I guess, what, words of wisdom, advice, learnings, right, from your early experiments, what would you share with your colleagues, at other agencies and other AG offices that, advice for as they begin their journey for those folks who answered the poll question about being very early on in their journey. What advice would you have for them? Yeah. I would say just don't be afraid to, like, go forward. Like, don't be afraid to fail. Like, all of this is new to everybody. We're all kind of figuring this out as we go. And the the, you know, the more you use a tool, the better you'll get at it. And so I I feel like a lot of people get frustrated at the very beginning. They don't understand it, then they abandoned it. They're like, oh, that wasn't for me. But but just don't be afraid to continue that process, you know. That there are rewards on the other end of the learning curve, and albeit AI is a very steep learning curve because I feel like the goal post is continually moving. Like, you know, I I don't think that we're going to get within the next five years to a point where we can say comfortably, you know, like, AI is done with this massive transition. It's gonna keep continuing to balloon at least until it starts to stabilize. Right? Right now, we're on that upward trajectory of constant new features and things happening, but it will eventually stabilize. But we can't be afraid to continue to, you know, just move forward and not be afraid to, you know, stumble a couple times before we get it right. Great. I'm sure a lot of folks appreciate that advice and that there there is so much to learn as, for for AI as holistically, much less right in just in one platform like we're talking about with with ever. But, appreciate that, Pam, and all the work that you've done, to advocate for for the use of this technology at North Carolina DOJ. As we transition to, our next, part of the panel, do you wanna put up one additional, polling question that will should appear on your screen? That question, which barrier, best reflects your biggest blocker for AI adoption today? Awesome. Alright. Transitioning, over to another part of this spectrum, not necessarily the end, but another part of this spectrum. Kate, in your role, with the Ohio Office of the Attorney General, you're more in in in exploratory journey, and you've come a lot further in that journey for actual implementation of workflows. But where where did you start? When did you start? And, you know, where how long have you been on this journey? So it's I mean, it's funny. So we do have a policy, like Pam was talking about, and I just looked at the date today, and they put the policy in effect in, October 2023. So I think they were trying to really CYA early on. But, you know, it was the same thing. We didn't really know. Everyone was kinda scared of what AI was and what it does and what it means. So, actually, like, as a team, we really started our journey. It was at Everlost Summit. I was in a session and, they were discussing about how predictive coding and, all these different tools that Everlaw has can, like, bring, you know, multitude of things together. And I'm sitting there listening and I go, I have a case that is a complex, multi state, and opposing counsel has served us with over 600 RFPs. So, like, we're trying to separately code all these different RFPs, and it just clicked with me. I'm like, this is gonna work for us. I think we can get this to work for us. Because this case is, like, one of, like, 20, and they're almost like cookie cutter cases because it's a robocall case. So I immediately pretty much just accosted Eddie and said, please, like, the whole value AI team, I was Eddie and Cam, we just I was, like, I'm pretty sure we can make this work. How can you help me? How can we make this work? This is too much. We had so many codes that I was trying to put into Everlaw. I'd actually maxed out the amount of codes that you could put into Everlaw, and I had to have them add for that. So I'm sure that Eddie can expand a little further on the beginning of our relationship, basically. Yeah. Yeah. Eddie was was the use case that Kate came to you with, was that something that you guys had, you know, cooked up before? Or when she came to you, we're like, this is a problem I didn't even know that we were gonna need to solve, but, lucky for us, we can help. Oh, it was definitely unique. I still remember it like it was yesterday when, she talked about, like, the entire crew of four or five additional people, and really just laying out, you know, their, you know, their story is unique because it's not about perfect AI maturity. Right? It's about practical maturity and understanding that they need this to stay above the water. They're drowning, and this is an active matter that needs a solution now. So, like, really being able to assess and qualify which of those parts can AI actually help and in what way, you know, really gave us the ability to, flex our, like, AI muscles, so to speak, and provide, a working solution that's hopefully repeatable because it's as she mentioned, it's one of, like, 20. So, yeah, this was a a definitely a unique use case, but it's probably not uncommon is what I'm gonna be guessing across all of the other state agencies who are facing similar, situations. It's probably that Kate was thinking a little bit more open ended than saying like, hey. You know what? Like, I know AI can do this, but can I do this, this, this, and this? She's expanded usage to a much wider scale and really focusing and, you know, urging us to think beyond just the usual or the normal, you know, use cases that we would, you know, typically, encounter. Great. And I know you, you know, you're in a unique position in this conversation, Elliot, that you've kinda seen the gamut. Right? You've worked with our customers, across the spectrum, both private and in public sector, and worked with a number of, offices with their adoption of AI. And in fact, your team is going to be on-site, in Ohio, and, I believe tom tomorrow, on-site in Ohio or, in Everlaw AI Lab. Kate, can you give us a little bit of color as to how you ended up going from, you know, accosting Eddie's team at Everlot in October to here we are and it's only May and you have a whole crew of Everlawyers in, two days of AI, you know, enablement and and discovery that are scheduled with with your team. Well, I'm actually gonna give a big shout out to Cassie. She's kinda the one who proposed this initially because, you know, we came back from the summit with, like, a list of things that we wanted to get done, and we just needed, like, front office to fully buy in. We were just trying to show every single person who would listen for thirty minutes, the ever like, our AI demo, and we basically, like, we're just out there beating a dead horse. So Cassie was like, well, what do you think about doing a big training? And I'm like, I think that's great. I think they'll listen to someone else more than they'll listen to me for once. You know, it's kinda that thing where it's like, you know, you're not really listening to your but you're listening to your grandma and grandpa. It it's that situation. Like, you're gonna have way more fun with grandma and grandpa than you will with me. So we are super excited about this. It's actually, today and tomorrow. And, I mean, so we've got three, different trainings happening at the same time. We've got AI labs running, so like a generic AI lab. We've got specialized AI labs for specific teams on specific cases, and then we have generalized, education, Everglow education. So day one is, like, basic education, searching, how to do public records requests. What else do we have? Story builder. And then day two is more it's the same thing we're having again, AI labs running for specific cases, and then we also have more advanced things. So we're doing advanced searching, advanced story builder, just, like, literally trying to get the office to really buy in. And as of earlier this morning, all of our labs are I'm super excited about this, and I'm hoping that it's gonna go as great as I hope it will. That's awesome. That's awesome. Well, we look forward to hearing about that. Part of your, I guess, how you ended up getting so excited about, Arolot AI from Summit, and then working with with coding suggestions, I guess, how did how did you build up that excitement internally, over, you know, these six months or so to, you know, mitigate apathy from folks to get people to show up? How did you tell help tell that story, internally? So like I said, our biggest like, we tried to figure out a way to really start it. And our biggest thing was to get the front office to buy in to it. So we had, you know, we talked immediately with our boss who is the deputy, director, and she then is trying to talk she's talking to the AG. We are trying to get everybody involved. We have an AI committee that we were talking to them as well. We were just doing a lot of different trainings. Like, I personally do a story builder training where I make people come in and, like, we go through their cases. I show them, like, what's going on with story builder, how you can use it, and how it's good for your team. I like to sell it as almost like a like a Google Docs that we're actually allowed to use because your whole team can be working at the same time in one. So we just are constantly pushing it and, like, hey. Do you know that you can do this? Like, we'll sit with an attorney for an hour and let them know what they can do. There's only three of us right now. There's usually five, but we're always, like, let me know how we you. We absolutely will. Contact us early. That's one of our big things is contact your nerds early. We're here to help you. And with these tools, we can get so much more done so much faster. And that's literally what we're preaching every single day. That's great. That's great. And and, Kate, is coding suggestions, is that something that you and Eddie, that's what you guys built? Because I've seen that inside Everlaw, but I was a little intimidated on it because I was like, I am so very entry level. I was like, coding suggestion sounds way over my head, but if it's something that you, like I need to take a look at that. So Yeah. I mean, it's not really similar than it, you know, it sounds. I think, you know, for the most part, you just have to decide what categories and codes you want to, basically build criteria for. And these criteria are nothing more than copying, pasting what you would normally write down in a, you know, in a, in a review protocol, give to a set of reviewers or attorneys to to to use. And using that criteria, you basically have to decide how many documents or which group of documents you want the AI to learn. And then after that, you have suggestions that are broken down into either yes, soft yes, soft no, and no. And then from that, you could decide to prioritize your review on your efforts on, like, maybe I always wanna pay attention to all the ones where the AI has suggested, you know, a yes to certain issues a, b, and c. Right? So just really nothing more than telling the AI what you wanted to assess and then give you a yes or no score on each of those documents. That way that you can decide to prioritize, which documents to look at next rather than just looking at a gigantic pile of documents and worrying. That sounds like an amazing time saver, and I'm probably gonna have to get with you, Eddie, because I I've the case I'm working on right now, we can definitely use that. So, yeah, I'll probably get with you after this session to try we can make that work for us because that's exactly what we're trying to do. We just we just don't know how to get there. So, yeah. This is awesome. Yep. That's what our team has been doing. It's definitely intimidating at first, but I promise, like, once you figure it out and you can explain it to someone else, it goes so much better. I'm excited. It's a lot of, creativity, right, to, you know, identifying the the problem that you need to solve and then, you know, getting all the the brilliant minds together to talk through how we may or may not, be able to help. So I'm glad that, we're able to help and hopefully be able to assist with your case, Pam. So for Eddie, you like I mentioned earlier, right, you're you kinda straddle across the various, markets and customers that Everlast supports, and love to hear just a little bit more from you of of what you're seeing in in the market and in in general, and where you've seen, your team add value. I mean, Kate sings your praises, obviously, for, these AI labs and how you guys have helped, with their exploration and adoption of using coding suggestions. But I'm curious if you're seeing similar, in other states. Like, what are you seeing in the market today? Yeah. So, you know, you've heard stories both from Pam and from Kate in terms of where they are at on their AI journey. And that spectrum, it kinda runs the gamut across all the segments that I work with. So, you know, they are not alone and you are not alone, if you're listening right now. So, you know, just to cover some, you know, quick quick pointers, you know, Pam had mentioned about, you know, not having an AI policy readily available. So a missing AI policy leads to two risky extremes. You have shadow AI where people quietly use tools with no oversight, or you have complete paralysis, like, no one is allowed to experiment. You know, Pam is at a very unique speak, sweet spot where she's allowed to do structured experimentation that will eventually feed into a real policy. And when you think about your, like, first AI policy, think of it as, like, you know, version one point o. Slightweight, focus on do's and don'ts, minimum data security and approval paths so that you refine what you learn, and it continues to build and grow and change as you learn and, as you apply, these different, methods and techniques. And then in terms of, you know, really focusing on the experimentation part. Right? A simple structure that we've seen work that we, usually, employ with most of our customers is that we try to focus on one use case, one legal team, one metric, all done within one month. Right? Like, for example, like, can we cut, like, para paralegal, like, document organization time by 30% this month using AI assistance, you know, without compromising quality or security? Something that's manageable and doable is far more, efficient and important because but being able to, measure that impact and doing it in a way where you're gonna be able to use that success to get buy in or additional buy in is gonna be far more, impressive than just doing, you know, rogue, you know, kicking the tires type of experimentation, so to speak. In terms of what, you know, our team provides, like, you know, I lead our value AI team here at Everglow, and we're basically a group of strategic advisers that help our customers be successful with AI. And we handle everything from strategy all the way up until implementation and even optimization as well. So we really get into the weeds of our customers' workflow, understanding where their needs are, what their objectives are, and then trying to come up with solutions that help them get to that end goal. Right? So, yeah, I mean, you know, we're, always available for a for a quick meeting or even a longer session like some of these onsites that you're hearing about with Ohio. These are the ways that we provide support and help to ensure that our customers stay successful with AI. Great. We that's a good tee up to, our final polling question, which it should appear on your screen. How interested, would you be in a structured, in person and hands on AI workshop for your office. Okay. Appreciate everyone's participation. I I asked, Pam this question, Kate, but I I forgot to ask you earlier. If you had any advice for, folks who are, you know, anywhere on their journey or even more mature on their journey. What what advice, would you share for folks on the phone? So I really think, just the number one thing is to get started. I know Pam said she's, like, kind of doing some experiments. That's the best way to get started. That way you can show what your work is and you can show your front office. Listen. This is what we have. I think we just got our oh, what is it? The EverLaw, like, success? What is it? EverLaw success? Success review? Yes. Sorry. Thank you. I think our success review, like, last week, and I was flipping through it today to see And it literally shows the percentages and how much time you can save using this. So you can provide this information to your front office and say, this is how much time we're saving. This is how much money we're saving. You know? And and when it all comes down to it, it's brass tacks. It's all about the money and how much time you have. And realistically, we don't have either hardly ever. So it's just about stretching it out and doing the best you can with the tools that you have. And if you have these tools, you're gonna be able to go so much further than you previously could. That's great. And you that's part of, I think, the the value of, Everlot for a lot of our our customers is helping you tell that story. Right? Providing you those figures and, that data to help you make the case, for for why, Everlaw AI was the right choice, how you're doing your job better, etcetera. It to round us out, before we wrap up this part of our, forum today, any from all three of you, and we can start with you, Pam. Is there any, you know, final thoughts or or key takeaway that you have, to share from our conversation aside from you would like to talk to Eddie about coding suggestions? Any anything else you'd take away from our conversation? Yeah. I would say a final thought for me would be to just always stay curious. So I'm in this world, like, I am, you know, the one that's forging on it and just continuing to learn. But I was almost discouraged by something that I thought, like, looked a little bit intimidating. And so a takeaway for me is that, you know, just keep exploring. There's gonna be a couple bumps in the road. But just because something's new or something looks intimidating doesn't necessarily mean that it is. So, you know, we're all gonna have the little, growing pains to get there. But after this conversation, I I feel a lot more confident going in and and being able to tackle something new that I had once, you know, before the session was like, oh, coding suggestions. I don't know how I'm gonna get anybody to buy into that if I'm not comfortable with it. But knowing that Katie, or Kate and, Eddie are are very confident, in its capabilities, and just kind of understanding it and, like Kate said, to just be able to to communicate that to someone else, your understanding of it is just the first step, you know, in in moving forward. Right. We love to hear that. And I think it's so valuable to hear from peers rather than, just, you know, myself and and Eddie talking at you. So I appreciate both your time today. Kate, any any final thoughts or key takeaways for the group? My thing is always just, like, go do the extra training. Sit in on the meeting. You know, push what to happen. If you can show what you learned in a training or that you read, let your boss know. Let you you know, these are the tools that we have right now. Might as well use them and, you know, it's always with state agencies. We always seem like we're, like, further behind than everybody else. But for once, we don't need to be. So just get out there and try it. That's right. Eddie, any any final thoughts from you? Yeah. I mean, I I I I wanna agree with you that the most valuable impact to change is peer communication. So I love these type of forums and sessions where peers are able to get together in the same community to be able to share their stories, their journey, their their bumps in the road, and, you know, sharing, you know, their wins and losses and so to speak. And that really, elevates the entire community at large. Right? Knowing that, hey. I'm not alone in this on this path, and there's others, who are taking similar paths as I am. So I encourage everyone to continue to have these conversations with your peers. I'm glad that we're able to facilitate those conversations here, as well as at Everlost Summit, in the fall, which, has more great opportunities to meet with, just in in person, in over in San Francisco. Well, with that, I think we can wrap up our conversation in this part of the session. Good luck, Kate, with your your AI labs and and all the trainings, in the coming day day or so. And, Pam, I look forward to continuing the conversation with you and Eddie about coding suggestions and how that might be helpful on your big case. And, for folks who are interested in and answered that they're interested in in AI Lab, we look forward to, speaking with you about that and setting something up in the near future. Thanks. Thank you, Kate, Pamela, and Eddie. It's not easy to be doing what you're doing right now. It sounds like you're taking on a lot right at this moment, and you're learning so much about the process. And so many people have questions here, I'm sure, that I I hope your experience help share insights and are of value to them. So with Everlaw AI Prime to help you find that key evidence and surface insights quickly, our next session coming up is going to focus on presenting those key facts from your review and building a narrative more easily with tools like story builder. And we're excited for that. We're gonna hear from Gordon and Kayla in just a few minutes in that next session. So, again, sit tight. You can keep your window open, and we will automatically move you when session three begins.