Daniel Lewis joins us this week to trace a path from Ravel Law to LexisNexis to LegalOn, with a throughline of data-driven thinking and practical outcomes for lawyers. Stanford roots shaped early work on judicial analytics, then a front-row view inside a global publisher broadened focus to content, guidance, and the daily reality of in-house teams. That experience pointed straight at contract review as a top pain for corporate counsel, which led to LegalOn’s product mission and global push.
Data access still shapes progress. Case law digitization advanced through projects like Harvard’s archive, yet comprehensive coverage, secondary sources, and news remain guarded by incumbents. Daniel explains why large datasets give scale, why startups face steep hurdles, and why thoughtful product scope matters. The lesson, build where data, workflow, and user value intersect.
LegalOn’s hybrid approach blends large models with attorney-built playbooks, practice notes, and suggested clause language. Consistency matters more than clever one-offs, so reviews align to standards, not model whimsy. Daniel shares a memorable demo from a rival where a phantom “California Code section 17” alert appeared, a cautionary tale that underscores the need for guardrails, verification, and explainability.
Conversation turns to multi-step agents and matter management. Picture an intake email from sales, missing key fields. An agent requests what is needed, opens a matter, applies a tailored playbook, highlights non-negotiables and fallbacks, then keeps stakeholders informed as work progresses. LegalOn also converts existing playbooks and prior redlines into AI-ready guidance, reducing setup chores while preserving organizational risk preferences.
Finally, Daniel outlines new muscles for legal teams. Daily AI usage shifts time from line-by-line edits to judgment, negotiation strategy, and process leadership. Tech fluency, business orientation, and change leadership rise in importance, along with a steady diet of outside-legal analysis from voices like Ben Thompson and Benedict Evans. The message, free lawyers from sludge, raise the ceiling on strategic work, and build for long-term improvement across the legal function.
Listen on mobile platforms: Apple Podcasts | Spotify | YouTube
[Special Thanks to Legal Technology Hub for their sponsoring this episode.]
Email: geekinreviewpodcast@gmail.com
Music: Jerry David DeCicca
Transcript:
Marlene Gebauer (00:00)
Hi, I’m Marlene Gabauer from the Geek in Review. I have Nikki Shaver here from Legal Technology Hub. And Nikki’s going to give us a recap of the recent conference on ⁓ September 18th. So Nikki, please take it away.
Nikki Shaver (00:15)
Thank you, Marlene. Hi,
everyone.
Yes, so on September 18th, we held our annual flagship conference in New York. It’s the fourth year we’ve done it, the LTH Innovation and Legal Tech Conference. Although heads up, there’s a new name for this conference coming. You heard it here first. We had a fabulous lineup of speakers and content, including our keynote speaker, Jae ⁓ who presented on pricing in the legal market and how it will change, but perhaps not in the way people think. We had a fireside chat with Damien Riehl on AGI.
and the impact on law. We had an AI governance workshop to help people identify gaps in their firms’ AI policies and get them thinking about policy enforcement. And we had sessions on how AI can accelerate the progression of data to knowledge, to wisdom, and on the real ROI of gen AI. And the takeaways? There is change afoot. We all know this and firms have been rolling with it, but we’re still in the relatively early stages of this change. Whether it’s
it’s AI governance and policies firms have policies in place, but perhaps haven’t yet thought about enforcement. it’s rolling out tools, they have solutions in place, but they’re still in the process of driving adoption. it’s ROI, they’re certainly thinking about it and tracking, but also that’s evolving. So our next event is our Midlaw event in Boston on October 15th. And then we have our annual
event for corporate legal departments and GC’s which we co-host with EY in New York on November Find out more about these and our other events by visiting LegalTechnologyHub.com and as usual, just go to the events drop down on the top menu and click on LTH. Thanks Marlene.
Marlene Gebauer (02:07)
Thank you, Nikki. That sounds like a very full calendar.
Nikki Shaver (02:10)
Indeed.
Marlene Gebauer (02:16)
Welcome to The Geek in Review, the podcast focused on innovative and creative ideas in the legal industry. I’m Marlene Gabauer.
Greg Lambert (02:23)
And I’m Greg Lambert and this week we are welcoming Daniel Lewis, Global CEO of LegalOn Technologies. Daniel, thanks for joining us.
Marlene Gebauer (02:32)
Daniel, it’s great to have you on the Geek in Review. Really appreciate your time.
Daniel Lewis (02:32)
Okay. ⁓
Thanks for having me.
Greg Lambert (02:36)
So Daniel, to say that your careers had a kind of a fascinating arc, think, is a little bit of an understatement. A lot of us know you from when you co-founded Ravel Law, where you were challenging some of the legal research giants that were out there. And now you’re at what may not be as well known at leading LegalOn, which is a major player in AI power.
contract review. So tell us a little bit about this journey, what your kind of core interests were and what you were trying to solve and how you’ve kind of taken that over into the transactional work of in-house counsel with LegalON.
Daniel Lewis (03:19)
Sure. So Ravel started in 2012. I was just finishing law school at Stanford and felt like the legal technology for research at the time felt out of date, felt like it was falling behind what was happening with the rest of the web. And it didn’t feel data driven. And so Ravel, the core idea was how can we make research more data driven and how can we provide a new perspective on entities like judges to help people understand how they make decisions.
So that experience was deeply informed by my law school experience and the problems that you encounter as a law student and thinking about the first few years of research heavy life at a law firm. And Ravel focused on selling into law firms. And Marlene was one of our early supporters and boosters and feedback providers. And so that experience of building a product that focused on giving litigators a competitive edge.
and competing with very well established incumbents and selling into law firms. Those were sort of like very core pieces of the next five years of my life with Ravel. And then we got acquired by LexisNexis, one of the companies that we set out to compete with after sort of realizing that competition was not really in the cards. Like it was not going to be, I think we learned a lot about what Ravel could and could not do and what.
advantages Westlaw and Lexis had that make competing with them very difficult. So we joined Lexis and then I had a great next five years of integrating Ravel into Lexis and seeing the machinery of a large organization and the many connections and relationships that they have to ship product that then touches hundreds of thousands of users. And I got to, in my last few years, lead the practical guidance team.
which was a pretty different experience because up until that point, my work had really focused on technology driven product development and a practical guidance that, that was a new opportunity for me because content is such a core part of that product. Yeah. It’s a team of hundreds of lawyers writing content about how to do a wide variety of tasks and the value that that creates for users I could see and feel every day. And the product was growing really well.
Marlene Gebauer (05:24)
Yeah, it’s more about knowledge, yeah.
Daniel Lewis (05:37)
Um, and I was spending a lot more time talking with in-house teams and those two experiences got me really interested in contract review because in almost every conversation I’d have with an in-house lawyer, contract review came up as one of the top one, two, three pain points that they were describing. And I was curious at that point. this was 2020, 2021. And I was thinking to myself, well, CLM and this sort of contract life cycle management space has been around for a while.
Why has this not solved it? But it was clearly unsolved. was clearly like everybody was still having problems once they actually sat down to review a contract, doing that faster, more confidently. So I started to think about launching a new business. And in the course of doing that exploration, I got introduced to LegalOn, which had already launched a product focus on software enabled contract review in Japan. And I was pretty blown away by the success and traction that they were having.
and ended up joining them as they were looking to take that offering and go global with it.
Greg Lambert (06:39)
I want to go back to something that you said early on, and that was, you graduated from Stanford Law and you were interested in these data-driven decisions and how you should be able to make those decisions based on reviewing the data. What’s in the water at Stanford that really invigorates the grads that come out of there to
to really focused in on the data stuff. Is there some professor that really pushes this? Because I’ve seen a number of grads that come out of there that are really data-driven.
Daniel Lewis (07:16)
Yeah, it’s funny. don’t know. Obviously Lex Machina came out and that was the brainchild of Mark Lemley, a professor who’s in the IP space and very well known. Ravel came out. Case Text came out of Stanford with Jake as the founder. For me, there’s definitely something in the water about being able to launch companies. That’s for sure. I was so lucky to have that network and that community to
Greg Lambert (07:31)
Yeah, there’s definitely something in the water there.
Daniel Lewis (07:41)
to build a business from. I suspect that like if I’d been anywhere else, I wouldn’t have started a business most likely. I give a lot of credit to
Greg Lambert (07:48)
Yeah, let me pull, I was gonna
say, let me pull on one more thing while we’re on data. Because I think the Ravel story is pretty common in the startups because you kind of reach this kind of almost a ceiling when it comes to the data and it’s, we’re at a point where it was either, I always tell the story, was like.
Well, we could raise $50 million to get the data or we could be acquired by a company that already has the data. And so I’m just wondering in this new age of AI, is that ceiling, has that ceiling kind of opened up now in your opinion? Or are we still kind of at the mercy of all of the data that a lot of the big companies have?
Marlene Gebauer (08:40)
behind
the walls. Yeah.
Daniel Lewis (08:41)
Yeah, ⁓
I think there’s still a pretty big barrier to entry. So one of the things that we started Ravel with, my co-founder Nick and I was the idea that case law was becoming more publicly available. Courts were becoming more digital. They were starting to release things more digitally native. And we thought, hey, we can ride this wave and we’ll ride this wave to overcome this moat, essentially, which was like only a couple of companies have.
the collections of case law that you need to power a research platform. And one of the things that we fairly quickly discovered is that wave of court digitization was not happening nearly fast enough for a startup like us to survive. And so within the first 12 to 18 months, we were out there looking for new data sources. And that led us to create a partnership with Harvard to digitize all that case law collection.
Greg Lambert (09:32)
Great project.
Daniel Lewis (09:33)
Yeah. so that is, you know, that is maybe now you could say that’s a partially solved problem. And we released it, you know, publicly, Harvard did a whole like case law access public unveiling. I think it was last year now. And at the time when we were talking with the Harvard folks, they’re like, yeah, all of the big LLMs have this already. Like they’ve been knocking on our door for a while. So, you know, like that’s now baked into OpenAI and Anthropic and anybody else. And yet at the same time,
Marlene Gebauer (09:54)
Mm-hmm.
Daniel Lewis (09:59)
It doesn’t necessarily cover everything that’s come out since then. So the full sort of comprehensive collection of case law post 2019, 2020, 21 is I’m not sure anybody could fully, fully vouch for it. And at the same time, turn to Lexis for a lot more than just case law. They turn to news. They turn for secondary publications. And none of that is publicly available. So.
The sums involved in going after a really robust legal collection. It’s really, I think our lesson learned was case law matters, but it’s certainly not the only thing. And you’re talking about not just tens of millions of dollars, you’re talking about a lot more than that at this point. And places like Bloomberg have invested hundreds of millions or billions and they’re still not, they’re not really making the dent that they probably wanted to make in the world.
Greg Lambert (10:48)
Yeah, it’s kind of a never-ending problem. When I was at the Oklahoma Supreme Court, we thought we were on the verge of all the courts coming out with all their decisions, making them publicly available in the 90s. We’re still We’re only 25 years later, so hey.
Daniel Lewis (11:01)
Yeah. Oklahoma was one of the good states, but they were all like,
Marlene Gebauer (11:02)
How’s that going?
So Daniel, legal on’s approaches built on what you call a hybrid intelligence model. that combines AI with, with playbooks and content curated by attorneys. So in a market where, you know, many competitors are sort of leaning on the raw power of the large language models. Can you walk us through the strategic thinking of.
sort of combining that with the knowledge curated.
Daniel Lewis (11:37)
Yeah, so when you think about something like contract review, one of the most important things is are you reviewing contracts consistently? Are you spotting the right issues? And are you making changes that are the right changes substantively? And do you have guidance that explains to you why these things matter? And with LegalOn, our approach has been to say, look, if you just turn to ChatGPT for contract review, you are going to get different answers every single time.
If you ask it to review a contract, it’ll review it. It’ll give you issues. And then if you ask it to review the same contract again, it’ll give you different issues, some of which will overlap with the first set, some of which are new. If you ask it to make changes two different times, it’ll make two different changes in different directions. And so that’s a consistency problem. Even if it’s all accurate, it’s a consistency.
Greg Lambert (12:22)
⁓ It’s a feature,
not a bug, right?
Daniel Lewis (12:26)
Well,
if you’re a business that cares about knowing what you’ve agreed to, it’s a bug, not a feature. And, you know, still too often it’ll make changes that are just not right or it’ll spot issues that are made up. And I’ve seen our competitors like run live demos where they’re like showing here’s an alert on an issue and it says, this section violates, you know, California code section 17. I was like, that’s curious. I’ll look up California code section 17. You’re like, that is not.
Marlene Gebauer (12:29)
Hehehehehe
Daniel Lewis (12:54)
That is not relevant at all. there’s not a thing. Yeah. So you’re like, God, this just, I wouldn’t want a doctor making decisions based on this kind of approach of inconsistency and the occasional error. That’s very hard to spot. So with legal, and we say, look, we’re going to be able to ground this review in attorney drafted content. We hire experienced in-house attorneys and they sit down and say, when you’re reviewing a DPA or a services agreement, here’s the hundred plus issues that we think you should look for.
Marlene Gebauer (12:55)
It’s not thing.
Greg Lambert (12:56)
Amazing how does it.
Daniel Lewis (13:23)
we’re going to guide the AI to look for those issues. When we spot a problem, we’re looking for those issues for a reason, right? It’s like language that’s missing or language that’s not favorable to you. And we’re going to offer a suggestion of real language that we’ve drafted that we think corrects that. Obviously we’ll be able to then personalize that with AI and adapt it to the contract to fit very precisely into a sentence, match the defined terms, but it’s grounded in trustworthy language. And it comes with a practice note that explains why we think this matters.
how to mitigate the risk, maybe how to negotiate it with fallback options, things like that. And so it provides folks with a ready to use and trustworthy set of playbooks from day one to review 80 % plus of their daily run the business contracts. Everything from NDAs to MSAs to office leases to services contracts of various types. And that
that means that they can get value right away. They are not left kind of with big projects to create their own playbooks, keep them updated, try to track the laws, the law changes. And they can feel confident that when issues are being spotted and solutions are being proposed, that those are grounded in trustworthy content. Because, you know, I might be the only one out there, I’m curious whether anybody else is. I have a Google alert set up, so I get…
notifications every day of new cases that mention attorneys being sanctioned for hallucinations. And every day, there’s an email full of alerts, honestly. And I thought this would be a bad
Marlene Gebauer (14:47)
You must be deluged.
Greg Lambert (14:49)
Yeah, there’s actually a guy in
France that has been keeping a list online. I think it’s, you know, it’s, I think we’re in the 300s, maybe more than that now.
Daniel Lewis (14:56)
Yeah.
You’d think the volume would be going down, but it seems to be going up instead. People have not learned over the last three years that…
Marlene Gebauer (15:08)
theories on this that’s about the I’m not gonna get caught mentality versus the I didn’t realize mentality so I think that’s what it is.
Greg Lambert (15:17)
I think this week there was a Cosen O’Connor lawyer that was fired by the firm for submitting that. yeah, it seems to the message has not seemed to have sunk in.
Marlene Gebauer (15:23)
Yeah.
That the,
message of, know, basically having a human, you know, review and, and, and, know, evaluate just doesn’t seem to have, have gotten through to some people, but yet it’s, it’s clearly. You know, foundational. mean, you have to do in order to make it work correctly, you have to do that. And I know Daniel, ⁓ you provide playbooks, but I think you also.
allow firms to have their own playbooks in there as well and to allow firms to basically have their own lawyers and the expertise ⁓ guiding the system.
Daniel Lewis (16:14)
Yeah, and that’s really important too, because there’s a wide spectrum of organizations out there, right? Some don’t have any playbooks of their own and they’re a two person team that’s just never had the time to build these things out. And there’s much more mature organizations that have very thorough playbooks for, you know, their sales agreements or NDAs or whatever their high volume contracts are. And there’s folks who are in between who have some playbooks for certain things and don’t have playbooks for others. And so our goal is to meet people wherever they’re at.
If you don’t have playbooks, we’ve got a great collection that’s available to you from day one. If you’re in between and you’ve got some playbooks, we’re going to help you turn those into AI and you can use our playbooks where you don’t have them. And if you have extensive playbooks that you only want to use, we’re going to help you turn that into AI. We’ll spot issues and make red lines based on your playbooks. And it has to be easy to use for any part of that spectrum.
And so that’s our focus. It’s like, how do we make this easy to use? How do we make it deeply accurate? How do we make that review and those revisions that we can offer, whether it’s on your playbooks or our playbooks, as surgical and precise and as high quality as possible so that what the user then gets to experience is a system that’s easy to set up, easy to use, and that elevates their work so that they can see the issues that matter and then focus on the details that matter. Like there’s a lot of satisfaction.
for folks in contract review after they’re able to step away from line by line review to get into the thing which is like, really need to figure out how we’re gonna reach agreement on this tricky indemnification issue. Or I really wanna make sure that nobody snuck something by me. I wanna make sure that I didn’t get a gotcha that I missed. People get a lot of satisfaction from spotting those details, but you risk missing those things and not being able to spend time appropriately on them when you’re stuck.
wading through all of the other mundane, repetitive things that we can help automate with AI.
Greg Lambert (18:05)
Yeah, a long time ago when I was a programmer in college that someone told me, know, making things is really easy, but making things easy is really hard. And so that’s one of the things that you have to kind of remember is, yeah, we can really make something, but making something that’s intuitive and easy, that’s where all the work is.
And so I want to kind of take that and talk about Ligelon’s unique kind of origin story of it, you know, having first been founded and scaled in Japan before it expanded globally. And I know there’s a number of things that, you know, we kind of learn about the Japanese culture, craftsmanship, precision engineering. So I’m curious on how that
⁓ culture and technical background has influenced legal law and product philosophy. Does it give you kind of a different perspective on building the legal technology than you may have from just the Silicon Valley type experience?
Daniel Lewis (19:11)
Yeah, so our founder and group CEO in Japan is a guy named Nizomu Tsunoda. And he’s kind of a Japanese Daffle Ganger for me. I really felt like an alignment of personality and life experience when I first met him. He practiced for a few years, was spending nights late into the early morning reviewing contracts and felt like there had to be a better way.
And that was my journey with Ravel. was like, this is the same thing that led me to start my first company. And around the same time, you know, he was seeing the advances of AI. know, AI had just like beaten the world’s experts on go, right? And it was clearly making breakthroughs. He’s like, this is the technology that’s going to help solve some of these problems. And we can automate a lot of this terrible, like mundane, repetitive work and give time back to thinking.
And I too had felt similarly about the technology is a solution here that’s making such significant improvements that it’s, we’re to be able to build incredible stuff with it. And so what we, what we’ve built in Japan and when I first visited was really exciting to me as a team that is deeply focused on those, those sort of characteristics that you touched on of precision, attention to detail, reliability.
And for me, those line up so well with the needs of building a professional product for lawyers. Like the attention to detail of getting things right, of building something that’s precise, that handles the details well. And these are endless when you’re talking about contract review. Like the variety of formats that contracts can be structured in and the idiosyncrasies of how like styles are set up and tables and formats.
You don’t think about this stuff, but it can trip up a lot of different parts of the process if you don’t handle them well. Like your ability to make a great red line depends on your ability to understand the styles and the formats and the language of the contract in a lot of depth. And the last piece of it that to me really resonates is this notion around continuous improvement. And so when you have those traits that are sort of built into the product and engineering culture, a deep attention to
how customers are using the product, what they’re asking for, building to that with detail and craftsmanship and reliability and security sort of baked in at the core there. And with a mindset that we’re not just in this to like make it through the next week or the next two weeks or like make it to the next quarter. It like, we’re in this to build a big company over the long run. I love all of those elements about what we bring to the table in building a product in this space.
Greg Lambert (21:40)
right?
There was one thing that you had mentioned earlier that I wanted to follow up on and that was you had mentioned about finding some of the things that were missing. So I’m curious when you interact with the clients and they’re telling you how they’re using the product, were there any surprises in some of the ways they either used it or some of the things they were learning?
through the process that you may not have thought was built into the product as you were making, more of an experience or result that your users are finding.
Daniel Lewis (22:24)
Yeah, we’ve certainly we were like learning new things from customers every day in part because I think expectations continue to grow about what AI can do to serve people. so things that were literally impossible two years ago, people would now take for granted, which is fun and challenging. But, recently, for example, we had a customer who was talking about the value of our playbooks and how they mesh with their playbooks. And they, you know, they review clinical trial agreements in the
health pharma space. And they were like, like by looking at legal on playbook, actually it spotted some issues that we hadn’t built out in our own playbook. And it helped us understand where we had some gaps that we could then fill. We have other customers who’ve talked about when they use our assistant to interact with a contract that it’s helped them come up with new ideas for negotiating a position or coming up with a fallback option or spotting.
a detail that they might otherwise have missed. And so, yeah, I think like there’s a constant opportunity here to continue to improve these experiences because this is still not a fully solved problem yet, right? Like there’s no world in which you’re just clicking a button and getting a fully automated contract review that you’re saying, I never need to look at this. I’m just going to send it off. In some ways, the closer you get to contracts, the more you see
the details emerge that you want to put in front of a human for judgment. Like, how do you want to handle this? Do you even want to negotiate it or what’s your risk tolerance? And building tools that can support that process of saying, how do we automate the most tedious mundane tasks to elevate you to focus on making decisions, making judgment calls and handling the details well? We’re still
Very much on that journey. I’m excited about it because the time savings that we’re unlocking for folks are significant, but there’s a lot more to build.
Greg Lambert (24:16)
Yeah, turns out practicing law is pretty hard.
Daniel Lewis (24:20)
Exactly.
Marlene Gebauer (24:21)
Well, maybe part of that journey is that, that, know, you’ve announced a significant investment in creating AI agents, um, that can handle, you know, complex multi-step tasks. So like, you know, managing contract intake, converting a firm’s existing playbook into an automated workflow. So I’m curious, you know, can you kind of paint a picture of what that looks like in practice? You know, what would that look like in real life?
for someone who was doing it.
Daniel Lewis (24:54)
Yeah. So let’s start with the way, like the place where all of this starts, which is a business stakeholder, maybe from the sales team sends an email to the legal team saying, Hey, we’ve got red lines back on our agreement. you review this? And that is the creation of a matter, right? Sort of a work task that the legal team needs to take on. And so when we think about agents, it starts right there, which is that request most likely lacks some key information that the legal team would like to
And so legal teams solve this with things like intake forms where they say, Hey, here are the seven pieces of information we want. It includes, you know, a targeted date for this, maybe information about the size of the deal, things like that. and agents can help in that step. You just got this email. It’s missing some details rather than the lawyer having to take time and respond to that. An agent can respond and be like, great, thanks for the request. Here’s what you’re missing. Share that. Now I’m going to tee up this matter and prepare it.
for the lawyer and they can then click a button and they’re in the review experience. Actually hitting the ground with a playbook that has been created based on red lines to their own sales agreement. So it knows what issues are acceptable when changes have been made and what red lines would come up against a non-negotiable position. Or maybe they come up against a fallback position that’s acceptable.
And so that playbook then elevates the work of saying, okay, we’re going to zoom through these red lines. It’s going to help us understand which things we have to actually focus on and which things are acceptable to us. And it’s going to tee up our fallback positions when those are ready. And when we’re done with that, like all of this is going to get sent back to the stakeholder. They might get automatic updates along the way as the work is progressing so that they know that their, you know, their red line is going to be handled by the end of the week.
which is when the quarter ends, which is when they need this deal done, right? And so they’re not sweating and stressing that has the legal team lost this thing, has it fallen into a black hole. So that agent experience for us starts with handling matters and automating work that lawyers don’t need to be doing. And then it extends into agents that help our customers turn their playbooks or build playbooks for them from a process that can take weeks into something that can take minutes.
And then it extends into that review process where it’s going to elevate issues for them and help them resolve those things with red lines, accept changes, fallback positions, things like that. So I’m super excited about these end to end experiences that we can take on for folks because a lot of lawyers would tell you like they’re just buried in sludge. They’re buried in tedious work. They’d be very glad for AI to take on 70 % of it.
So they can then focus on the things where they can think and make decisions and provide leadership. ⁓ So it’s a variety of agents that are going to be knitted together taking on these kind of multi-step workflows.
Marlene Gebauer (27:46)
So it, oops.
I was going to ask you, mean, is that something that clients can sort of do on their own on the fly or is that something that they partner with you or both?
Daniel Lewis (28:01)
Now our goal is to create agents where there’s knowledge and process built in so that folks don’t have to reinvent the wheel every time, but that is personalized to the context of their business and the risk preferences that they have and the policies that they have. So with matter management, for example, that’s a combination of, we’re going to build the technology that will
ask questions and gather information. And all that you need to do as the business is provide the kinds of information questions that you want asked and answered. Right. And when it comes to playbooks, we’re going to build the technology that can take your word document or your spreadsheet or your template or 10 agreements that you’ve readlined in the past, convert those into a playbook that’s designed for AI and designed to work within legal on.
present you with questions and clarifications, and then optimize it for you and say, the way that you’ve written this is great for humans, but it’s not so great if you’re trying to train AI to spot these issues. So we’re going to automatically do that for you, because we know how to do that, because we’ve done it countless times for a bunch of other areas. And then we’re going to let you click a button and activate it in the product, and then you’re off and running.
So it’s that combination of like built-in know-how and technology and then like making it easy for customers to provide the context that then deeply personalizes it to their business.
Greg Lambert (29:29)
So, Daniel, now that you’ve solved all the mundane tasks that the in-house team has to do, you’re automating a significant part of the review process. I know that the demands on in-house teams are different than outside counsel and law firms. So, what do you see the evolution of the in-house team?
doing, what’s kind of the strategy going forward that these teams are going to provide for the entire business.
Daniel Lewis (30:04)
Yeah, when I think about like the individual lawyer or the individual person on the legal team is doing this kind of work that we can support, I think about how do we elevate them so that they can think and decide and lead. And when operational efficiency improves, people win back time for that work. They win back time for thinking. And with that time, they can use AI again and they accelerate the next idea. And so in something like review, it’s
not about replacing the corporate legal department, it’s about reframing their role. And for decades, in-house teams have been bogged down in some of this work that feels reactive, line-by-line contract review, triaging bottlenecks. And that work is essential, but it’s not exactly what they want to be doing when they think about the most strategic parts of their job. And so when we can provide technology and AI that serves as a partner to them,
not as a replacement, but as a partner, they gain back this thing that’s been scarce, which is time and bandwidth. And with that, they can then shift to supporting the business in different ways that can be helping better understand risk framing and prioritization. It can be process improvements, technology leadership. And I think some of that raises the bar or changes the expectations for the kinds of skills that
lawyers need and some of the new muscles that they need to cultivate. So maybe we want to talk about that too, but I do think it’s a reframing around more tech and process and prioritization and strategy.
Marlene Gebauer (31:38)
Yeah. I’d like to know more about what are these muscles that the attorneys are going to need to develop? mean, what are your thoughts on that?
Daniel Lewis (31:44)
So I’d give you the example of a senior attorney I met recently at an event. Her name is Mackenzie, and her next desired role is as a GC. That’s kind where she’s at in her career. She’s experienced. She’s got good communication skills. She’s got good judgment. She’s great, right? And yet, to get to that GC role, she’s like,
I think I need to have demonstrated experience implementing a legal AI product and proving how it adds value to the business. And I agree with her. If you were thinking about hiring a GC today at a business, you would certainly have on that list somebody who has the competency for implementing technology that makes that team more efficient, faster for the business, more responsive. So that feels like a core competency today.
And you might not have said that 10 years ago.
Marlene Gebauer (32:37)
Yeah, agreed.
Greg Lambert (32:38)
Well, Daniel, before we get to our crystal ball question, we wanted to ask you, and we’ve been asking our guests lately, there’s so much going on in the market with the technology, with the changes. You mentioned just having a Google search set up for a hallucinated case where lawyers have been caught citing hallucinated cases.
Where is it that you go to kind of stay up on what’s happening in the industry? Do you have certain resources that you hit up?
Daniel Lewis (33:12)
So within, I’ll segment it into like within legal and then outside of legal. So within legal, you know, I’m on LinkedIn regularly and you can get updates from all the different companies that are out there. And I feel like that’s a helpful way to kind of stay up to speed. There’s various resources and newsletters, whether it’s from, you know, Law 360 or Legal Tech Hub that I think do a nice job of aggregating some of that news.
For me, I probably spend more of my attention though on the sources that are not directly within legal. So two that I, well, the one that I really like is an analyst called Ben Thompson who writes a of a regular newsletter called Stratechery. And it’s not about the legal industry, but it is about technology and it’s about chips and it’s about AI. And to me, that is like a really helpful perspective in
unpacking some of the deeper, um, the deeper forces that are shaping everything that happens downstream of that. like understanding what’s going on with chip production in Taiwan informs like how you understand what Nvidia is doing and what Amazon is doing. And it informs like how open AI is approaching building data centers. And it informs
thinking about the speed and the cost of these models over the next two years or five years. And it informs thinking about what’s actually possible that we want to be building our product to be able to accomplish three years from now. So it takes me from like a very foundational part of like how to chip factories in Taiwan work to like, how do we want to build our product so that it may like feel kind of slow today, but 12 months from now, the technology will have like caught up to the place where it works really gracefully.
And then the other analyst that I like is Benedict Evans, who also kind of covers AI and tech. And I think these guys both like help me try to stay grounded in external perspectives, as well as like help pop some of the boosterism and bubble-ism that can come from, you know, people sort of pitching their own supply.
Marlene Gebauer (35:24)
Those are good. We’ll have to, we’ll have to check those out. Those are two new ones that we’ve heard. So that’s good. Thank you. so Daniel, now it is time for the crystal ball question. And so we would like to know from you, what a change or challenges do you see on the horizon for, the legal industry, particularly for, for in-house teams, ⁓ that you believe we need to start prepping for now.
Greg Lambert (35:25)
Yeah, thanks.
Daniel Lewis (35:46)
So to me, think one of the biggest changes that everybody’s going to go through over the next few years is the change to using AI every single day. Businesses are already asking for greater efficiency from their in-house teams, asking them to do more with less, do more faster, maintain the same or better quality levels. And that pressure is only going one direction, and we know what direction that is. It’s not like those things are ever going to work out.
Marlene Gebauer (36:08)
I’ve literally, I literally was on a conference
hearing that, that, ⁓ they were saying this was an in-house person. were saying, you know, one of their KPIs was like, we want to have this X percentage of people using it every day by a certain date. So that is real.
Daniel Lewis (36:21)
Yeah.
Yeah. And I think it’s very obvious, but if you thought about it in terms of, like, you know, in-house teams are asking more of their outside counsel. Well, what’s the next thing that happens? Businesses ask more of their in-house teams, right? Like that’s just a process that is going on right now. And I think up until now, like the legal teams have had to choose between relatively bad options to take on the…
the things that they’re asked to do by the business. They have to hire more staff. They have to maybe take more time and things go slower or they have to cut scope. And AI is starting to rewrite that equation. And we’re entering a moment where AI can really partner with lawyers and folks on the legal team to take on significant portions of work and help them move fast and focus on the details that matter. And contract reviews a great example of that, but it’s certainly not the only example of where there’s value to be had in using AI every single day.
And I think the challenge is not just adopting a new tool, right? Sort of goes back to like, are these new muscles that folks need to have? And it’s, to me, I think it’s about preparing for that redefined role and how lawyers are going to be asked to evolve from being primarily reviewers and risk spotters to being more strategic advisors and risk framers and technology leaders. So Mackenzie.
You know, to me is this example of somebody who’s thinking about it in those ways. It’s like, if you want to be a leader, she and people like her need to be thinking about, where is the lowest hanging fruit within my organization to get wins? What are the processes that are costing us a lot of money today or slowing the business down most significantly? How do I think strategically about tackling those one by one? How do I think in a system?
to understand how solving this problem here frees up resources over there. And so it’s cultivating these new skills around tech fluency, an ability to sort of think about the technology that’s available, understand its limitations, understand a company’s risk appetite, knowing when human oversight’s essential. I think it’s around like business orientation and knowing how to speak the language of the business.
because I’ve heard even lawyers say this, lawyers will be like, in some cases, I’d rather just interface with software than another lawyer. Don’t you think a lot of business people also think that? I’d rather just interface with software than a lawyer. so lawyers need to be in this position where they’re adding value on top of that, because more and more people are going to be able to just turn to software to ask questions and get answers.
And then it’s around change leadership, right? Like technology is going to move a lot faster than the human teams are. And I think we’ve seen this over the last three years, there’s such a big overhang of capabilities that technology can provide, but people are people in every industry. That’s not unique to legal. And like the pace at which people are changing is slower than the pace at which technology is changing. So I think those are some of the areas that are really interesting to me of like where leadership within legal teams is.
is going to be needed over the next few years.
Greg Lambert (39:22)
It’s definitely going to be interesting and we’re all going to be working out some new muscles, think. Well, Daniel Lewis, Global CEO of Legalon Technologies, we want to thank you very much for taking the time to talk with us here on the Geek in Review. It’s been a pleasure.
Marlene Gebauer (39:27)
Flex.
Daniel Lewis (39:37)
It’s my pleasure. Thanks for having me.
Marlene Gebauer (39:40)
Yes, thank you, Daniel. And of course, thanks to all of you, our listeners, for taking the time to listen to the Geek in Review podcast. If you enjoy the show, share it with a colleague. We’d love to hear from you, so reach out to us on LinkedIn and even TikTok.
Greg Lambert (39:53)
and TikTok now.
So, Daniel, for the listeners who want to learn more about what you’re doing there at LegalOn, where’s the best place for them to go?
Daniel Lewis (40:03)
come check us out at legalontech.com and follow us on LinkedIn for updates.
Marlene Gebauer (40:07)
And as always, the music you hear is from Jerry David DeCicca who has a new album out. Thank you, Jerry.
Greg Lambert (40:12)
Yeah.
Jerry. All right. Talk to everybody later.
Marlene Gebauer (40:16)
Bye.