Leveraging Latitude | Leading AI Transformation in Legal with Bill Ryan

Transcript

Bill Ryan 00:00

The barrier to entry is, with all due respect, relatively low, and the productivity elements of it are really tangible. The tools are available to everyone. The key differentiator, at the end of the day, will still be the human piece. What is the unique characteristic that your perspective can bring?

Jay Harrington 00:14

Much of what you said wasn’t necessarily about technology challenges, but they were more change management, people challenges it sounds like. So, building that culture, overcoming inertia, risk aversion, aligning incentives, that kind of thing.

Bill Ryan 00:28

I can’t emphasize enough the importance of direct leadership engagement. To me, it’s the most fun part of AI right now, is the ability to enhance your understanding of different topics or different areas, is just unlimited right now. That is a joyful element of this. There’s a lot of hard dynamics, but that ability to consistently learn and grow, we absolutely need that and look for that.

Candice Reed 00:53

This is Leveraging Latitude: Cultivating a Full Life in the Law. Please join us on our journey as we discover how to leverage the hard work of becoming a lawyer to achieving success and leading a rich and fulfilling life in the law.

Alex Su 01:07

Hi, everyone. Welcome back to Leveraging Latitude. Today, we’re diving into a timely topic at the intersection of innovation, AI, and the future of legal work. Today, I’m joined by my co-host and colleague, Jay Harrington. Jay leads our Detroit office and is well known among the legal community for his LinkedIn takes on business development and the future of legal work. Jay isn’t a stranger to podcasting but does join us for the first time on this show, and so now I’ll pass the mic to Jay to introduce our featured guest.

Jay Harrington 01:36

Thank you, Alex. And yeah, it’s a pleasure to welcome Bill Ryan to the podcast today. Bill is Senior Vice President and Chief Compliance Officer with AT&T. He leads AT&T’s LegalEdge, which is a technology-enabled center of expertise using an AI-first approach to deliver legal services inside the company. Bill, welcome to the show.

Bill Ryan 01:59

Thank you, Jay. Thank you, Alex. Great to be here.

Jay Harrington 02:02

It’s great to have you here as well. And I know we’re going to be talking a lot about AI today, everyone’s favorite topic on LinkedIn these days. I thought we could just start off, because I know I talked to a lot of lawyers, and especially those who are maybe a little further along in their careers. We all know there’s a bit of hesitancy among many, “I don’t know about this whole AI thing,” or “I’m too late in my career to learn how to use these tools.” I thought, Bill, maybe you could tell me, or tell us, a little bit about your dad, because I know he has become someone who’s recently adopted AI as part of his legal practice, so tell us about your dad and his AI use.

Bill Ryan 02:45

Yeah. Thanks, Jay. My dad’s an attorney in Boston and has been – he’s in his early 80s. He’s been practicing for a long time. And we were together over Thanksgiving 2025, and we started talking about a project he was working on. He had to draft a letter. And I exposed him to some of the AI tools, fairly basic stuff to do some research and do some drafting. And it’s just – I would not call him technologically savvy, if you’ve seen him type or try to draft a text message, which has perfect punctuation and perfect context and perfect syntax. But it really is, to me, insightful, or it’s been educational to see that he saw the benefit and the output even then, at that point in his career. And we’ve talked about it, Jay, I mean, he’s using it now, at this point in his career, to drive efficiency and research and drafting.

Bill Ryan 03:44

It’s fairly cabined off in how he uses it. And he is absolutely disclosing the use of the AI. When he sends me an email, he’ll tell me what portions were created by AI, so he’s very ethical about its use. But I think it’s the example that the barrier to entry is, with all due respect, relatively low, and the productivity elements of it are really tangible. So, he’s using mostly Google Toolset right now, but you see it. You see efficiency, his question’s going to get more… He gets to the more complex questions more quickly. And I just use as an example that it really is just the focus and the intentionality of digging into the tools, and I think at that point, you open up a door to drive some efficiency. I should have mentioned it about my dad; the big efficiency was it was not as if he was going to use AI or use online research. He’d go to the law library and do research for some of the things he’s now using AI to drive insight, so it was a pretty massive shift.

Jay Harrington 04:47

Well, I think we can piggyback off that and talk a bit more about a much more kind of sophisticated enterprise-wide effort to drive efficiency and productivity with AI, which is LegalEdge. So, I first became aware of what you’re leading there through your LinkedIn post, where it was kind of just rolling LegalEdge out, looking to hire some people to join your effort. So, maybe we could just set the foundation, talk a little bit about what LegalEdge is, what led to the development of this department and initiative within the company, and start there, which I think will set us up for where we’re going next.

Bill Ryan 05:28

Yeah. AT&T LegalEdge is the manifestation of a multi-year journey of recognizing, and our general counsel really putting a lot of effort and intentionality behind recognizing the disruptive nature of artificial intelligence. And I know that’s a very common statement and we all see it, but I think the depth and intensity of the transformation was something we started feeling probably about 18 months ago, maybe even 2 years ago. And we took, initially, a holistic approach, in terms of expanding or making AI and some of the tooling available to a broad set of our department. We talked to our partner law firms and asked them to think about ways to utilize it. And I think the shift, or the speed of adoption, was okay, but I don’t think we saw the gains. What we believed were possible really these exponential gains that if appropriately harvest and focused, we should be delivering.

Bill Ryan 06:33

And so it was late last summer, less than a year ago, August 2025, when we had a discussion, and so how do we accelerate the adoption and impact of AI? How do we ensure that we’re driving the right levels of efficiency and output, and really upskilling our work? And we just thought that we needed to have a contained, focused entity to move the needle in a way that would be most impactful. AI adoption is like a lot of other transformation initiatives, you need to have good process, you have to have good culture, and you have to have the right incentives. So, when I think about good transformative initiatives, they tend to have good existing process, good culture, and the right incentives. And the broader you go, the more disparate incentives are, the more different types of cultures, it’s harder to drive foundational transformation.

Bill Ryan 07:36

And you see some of these really sophisticated companies, it’s really insightful to see how AT&T manages transformation, and has been managing for so long, is smaller teams can sometimes be a great catalyst, especially when they’re taking on a really new technology or a new initiative or focus on a product set. So, we thought containing the right process, the right culture, the right incentives, in this entity, effectively what we call internally our in-house law firm, was the right way to approach it. And we would use that AT&T LegalEdge, our in-house law firm, as a catalyst for broader adoption and disruption within the department, and hopefully, with our partner law firms, just showing a different model to see, “Let’s go test these things. Let’s have the right incentives.” Completely AI-native, right?

Bill Ryan 08:25

I mean, we don’t have some of the pressures that other folks have in terms of maintaining existing approaches. We could be 100% disruptive, and we could test and iterate and learn. So that was really the genesis, is we needed to speed up. We needed to make sure that the impact was as profound as we thought it could be, and we felt that a self-contained unit would be best to do it.

Jay Harrington 08:48

Yeah. And so you touched on a few things there that I wanted to follow up on, including just some of the challenges and what’s necessary to make this transformation to be a more AI-first approach to a legal department or a law firm. And much of what you said wasn’t necessarily about technology challenges, but they were more change management, people challenges, it sounds like, so building that culture, overcoming inertia, risk aversion, aligning incentives, that kind of thing. I guess, what have you learned, or what has maybe surprised you, or what obstacles maybe did you not foresee going into this that have arisen that might be applicable or interesting for other people who might be earlier in the stage of transformation but looking forward seeing what might be coming ahead?

Bill Ryan 09:40

Jay, I think it’s a super important insight topic question to pull the string on. And one I’ll acknowledge, I mean, we are really fortunate to be operating the AI space at a company like ours, where we have an incredible AI infrastructure and an incredible team that really has been disruptive and driving lots of transformation across network, across our internal systems, across call centers. So, you’re constantly inspired by people that are solving really complex problems. And I think that proximity to that disruptive mindset, everything I’ve learned, I’ve effectively learned from watching people around me disrupt their ecosystem, so that’s a massive advantage when you sit in proximity to those sorts of leaders.

Bill Ryan 10:27

I think the change management piece was harder than I thought it was going to be. And I still haven’t fully figured this out. There’s inherent resistance even from people you would have expected to be those who would embrace that shift. It’s a harder process, and honestly, I can’t put my finger completely on the why of it. I will just acknowledge that, not unique to a legal department, but just even talking to peer companies and others in the field, the depth of adoption is really varied, and people who believe they’re fully adopted, are fully transitioned, they’re probably really earlier in the journey. So, I’ve been focusing and looking, and trying to learn a lot more on what gets people on that. Someone described it to me at once, as there’s various stages of AI adoption, that there’s general awareness, and then there’s personal productivity enhancement, so I’m just more efficient. And then there’s process disruption, and then the last stage is really white blank paper recreating your entire model.

Bill Ryan 11:42

And I think a lot of us are well in the awareness stage, probably somewhere deep in the personal productivity stage, but the question then becomes how do you get to the true process disruption stage? Is there something in your day-to-day output that you are never doing the same way because of the introduction of AI? Now, some of us are probably using AI opportunistically, like, “Ooh, I should use AI to do this task.” And then some of us will run Monday morning prompts, or daily prompts to help us get ready for our calendars. But in terms of just generating legal work output, how much of us are truly fundamentally disrupting the processes that exist previously? That is a harder equation. That is generally harder. Let alone reaching to the fourth stage, which is throw away everything I’ve ever known, knowing the tool set that now is available to me, how should work be created and done? And that’s aspirational, and I think we’re definitely trying to spend some time in that fourth kind of bucket. But helping people along that journey, and somewhere along that personal productivity, the process disruption, there’s stalling, or there’s a pause.

Bill Ryan 12:56

And I think what I’ve seen the most effective organizations, who are really able to do that, I can’t emphasize enough the importance of direct leadership engagement. We’ve been talking to some leaders, some thought leaders in the AI space, and they’re throwing around the quote of “You can’t lead what you don’t touch.” And something as simple as that is really, really true in this space. And you can see disconnect immediately from leaders who have spent time in the tools, from people who think they probably should have spent time in the tools, and leaders are recognizing that. And I think conversely, when I look around, our general counsel’s a great example of it. I mean, he’s spending a fantastic amount of time in the tools himself, generating output and generating work product. That’s standard setting. That makes it absolutely unavoidable that this is a direction that we’re going to go.

Bill Ryan 13:51

So I think from a leadership perspective, one of the things that I’ve really captured from the most effective leaders that I’ve talked to is direct personal engagement in the tool set. And maybe that sounds overly simplistic, but the credibility issue and the trust that teams have, we all know there’s some skepticism about the toolings. It’s got shortcomings. It does have hallucinations, it does have issues, so there’s already a trust gap. And when people see leaders nevertheless digging in, being fully committed to it, I think that’s a little bit of a difference-maker. I do see people’s mindset shift a little bit. So that was one, that if you’re being asked to lead a change, you personally have to have the deep familiarity with tools, and that’s on a personal level, a professional level. I mean, I’ve signed up for every single tool that exists on a personal side, try to use it more and more, because I know I need to speak with it with credibility and with understanding of what is it good at, what’s it not good at, to set realistic goals and really help the organization hopefully move along. So, I think that’s one.

Bill Ryan 15:02

I think the second is patience. Someone gave me a really great analogy, you take some of these tool sets, it’s almost like hiring a new associate. The new associate comes in, and it’s only okay. I was not a very good first-year associate. I got better over time because people invested in me. They trained me. They took time with me. They took me aside, and said, “Don’t do it that way. Do it this way.” It’s not a perfect parallel and I’m not equating it on a human level, but to make the tooling better, it takes a lot of intentionality. Give it the access to the right data set, modify the prompt, put on the conditions of output. There is a coaching. It’s a dynamic. It’s a relationship. AI is a relationship. It’s not a static tool set that’s going to sit there, you need to invest in it to get the right output.

Bill Ryan 15:51

So, I think those are probably the two biggest things I’ve seen, Jay, is just direct leadership modeling and engagement, and then that patient side of treating the AI as a relationship that needs to grow, and there needs to be some mutual growth. How do I maximize its utility, and how do I help it maximize its function with my coaching and feedback? And I’m starting to see that help a little bit move the needle.

Alex Su 16:15

Bill, I’ve got a question about how you decided to sequence the rollout of the transformation. So, which buckets of work did you all decide to attack? What was the thinking behind it? You made the analogy to a junior associate or a first-year associate. You know that AI is not going to be perfect. Did you sense that some practice areas, or some buckets of work were more ripe for disruption versus others? Share with us how you though about that.

Bill Ryan 16:41

We completely did. And I think again, AT&T’s been really thoughtful about ensuring that if we’re investing in AI, there needs to be a return and we need to make sure that we’re getting… That not everything has a discreet direct ROI, but we’re mindful of if we’re going to put time and energy and resources into this, we need to see some benefit. So, we did want to look at areas where we thought that we could find an ROI. And we did think about just looking about how work had been produced over the last decade, that some of that, it’s a little bit of a generalization, Alex, but I think generally, that junior associate-level work, initial drafts and memos, doc review, initial drafts and memos, research projects, depo binders, initial investigation drafts, there are a lot of hours spent, both internally and externally with our partners, that as we were exposed to the department and ourselves to tooling over the last year and a half, two years, we knew that data aggregation, data analytics, document scanning, document summaries, research were going to be the ones that likely were most primed for disruption.

Bill Ryan 17:58

And we also knew that there was a cost opportunity there coupled with our internal… We have a lot of experience. We have a lot of fantastic lawyers at AT&T that put the tools in the hands of great lawyers to go do work that we think is going to be done more efficiently with AI. Then you kind of intersect, okay, this is a new experience for all of us, we want to be mindful of the risk dynamic. We don’t want to put the final draft of the most important legal memo we draft this year through a tool, and say, “Here’s the final product.” This was the iteration stage, and so we really felt like this was the right risk-adjusted, iterative approach where we also had some efficiency in ROI returns, and so that was it. I mean, we did a lot of data and made sure there was enough work for us to warrant the creation of LegalEdge, and there was and there has been. And I’ll tell you, it’s not perfectly finalized math, I would say, but the data set would tell you that we are driving incredible efficiency against work that was done a different way for the last several decades. So, saw that data set, we think putting the Edge on top of it is driving the right efficiencies, and we’re excited to expand the aperture for Edge as we go forward.

Jay Harrington 19:25

Bill, I was speaking on a ACC Michigan panel a few weeks back, and I’d say, I don’t know, maybe 40% of the conversation was focused on the issue of ROI on AI use for legal departments. Any other thoughts on just where… Maybe today, it’s driving efficiency with just more junior associate work, but in the future, as you see the tools improve, the models improve, and as you’re thinking ahead to what does ROI look like maybe a little bit out in the future, have you given some thought to that? What legal department leaders might be thinking about in terms of, all right, how should we be measuring this moving forward, or what should we be looking forward to maybe a year or two out?

Bill Ryan 20:12

Yeah, yeah. In our next podcast, I’ll tell you how to prove ROI for a legal department. It’s obviously a really complex question, and so it is not a simple answer. I think on pure cost, I think there’s some data that would say that work product that we generate is more efficient than it’s been generated historically. Just on an absolute basis, I feel pretty confident of that. But I think there’s some other issues here, that I think that ROI is a longer-term question. Two things I would say is the tooling set, and we’ve seen this, right? We’ve seen this not just in the legal industry, but across all industries. The tool set that we’re using right now, and this is not a controversial statement, will not be the tool set we’re using 6 months or 12 months from now. A lot of the shortcomings, deficiencies will be cured. I’m a full believer in that. And so the ROI now, is I like to think about it as we’re building the right systems, processes, culture, incentives for the future tool set that’s going to be a lot even more impactful than the current tool set.

Bill Ryan 21:29

So, even if we can only do certain levels of work now, because we want to have that human in the loop and the human oversight, and take some of the more risk-adjusted work, I do think the tool set’s going to improve over time. That’s, I don’t think, a controversial take. So, I think there’s ROI, Jay, in the sense of we need to figure out how to use these tool sets, and that is not an on/off switch. That goes back to the things we talked about, in terms of culture and data and processes and incentives. So that’s one. So that’s not a number I can put right on a piece of paper, but I think it’s the right approach because we need to be prepared as those tools improve.

Bill Ryan 22:03

I think the other side of this is even if they want immediate cost savings, the reality of this is the complexity and amount of work is increasing as quickly as our efficiency. I don’t know if it’s one for one. I’ve heard anecdotal stories of courts being flooded by increased amounts of paper, because it’s a lot easier to generate 18 additional arguments in your complaint than it ever has been historically. You see that in terms of complaints we receive, the way customers communicate, the way, in the nonprofit space, I see an increased sophistication in terms of memo preparation, because everyone’s leveraging the tool. So, even if everything else being equal, the amount of work that’s being generated, it’s not just us that has the AI tools, it’s the counterparties. It’s people that want to do bad things to your company. They all have access to AI, so the efficiency is almost a non-negotiable, because I think the counter-pressures are growing at the same rate that the efficiencies are grading. Maybe they’re not the same rate, it’s obviously probably a little bit of a stair step.

Bill Ryan 23:15

But in that sense, Jay, as well, I think the ROI, I can’t prove to you that every single thing I do is going to be mathematically proven. But I think in terms of just the increased workload, the increased volume, the increased complexity of issues, the increased sophistication of our own clients, our own counterparties, require a certain level of proficiency with AI just to meet that kind of swelling tide. So, those are two ways I view ROI. Those are a little bit more qualitative than quantitative, but we hold ourselves to a very hard line quantitative standard as well. The math is always a little bit imperfect, but I’m really confident that we’re delivering legal work product more efficiently than we ever have when we use LegalEdge in the way we’re using it.

Alex Su 24:03

I got to imagine as you’re seeing how all this transformation and AI and technology plays out, evaluating AI, you’re probably noticing that there are some types of work that are best held onto by humans, by people. I’m curious if you’ve developed any sort of view on where humans fit into all this work in the future. You’ve mentioned increase in maybe demand for the work, maybe more efficiencies, but where do the humans fit in?

Bill Ryan 24:30

Yeah. I mean, decision making and relationships, right? I mean, at the end of the day, the decision, we can’t delegate the decisions. Decision making has to come down, ultimately, to the human. And then relationships still really matter. This happened this morning, Alex. There was a back and forth on a nonprofit issue with somebody who might help on the nonprofit side, and I could tell the nonprofit side was generating super smart, sophisticated questions and points of view using AI. I think most of us can pick up some of the markings of it. And then to be honest, I was using AI to help formulate my response, and then adding in my own color. And there was some point where I was like, “This is driving, enhancing efficiency,” because we’re both using it as opposed to just having a human call to call, and just saying, “What’s the zero sum that we need to reach?” We were both using the tooling to replicate a more complex approach than we probably missed.

Bill Ryan 25:36

So, I do still think that there needs to be the right level of relationship and decision making of what are we trying to achieve and what’s the tool going to help us achieve? But if you use the tooling just to generate more information, more data, more optionality, more thoughts, it’s incredible at that. But that really wasn’t driving efficiency in that case. This was just driving us in the sense of like, “Look how much information I have on this side and look how much I have on this side,” and we weren’t getting any closer to a decision. So, I still think you have to come into strategic alignment, and then obviously, the relationship side, whether it’s transformation, whether it’s just the professional law generally, to me, is still an absolute key element, to have the trust of people that you’re advising. Regardless of your output, they’re still going to want your opinion and your perspective on things.

Bill Ryan 26:23

So to me, it’s still omnipresent. I’m not worried about the human element being marginalized at all. I think the tools are available to everyone. The key differentiator at the end of the day, will still be the human piece of what is the unique characteristic that your perspective can bring? Because at some point, there’s going to be a zero-sum game with everyone having access to the same tooling, right? So, I think we’re going to see… I’m in the camp that the human piece will always, still is, and will continue to be absolutely essential. So, we’ll see, but that’s where my perspective is right now.

Jay Harrington 27:07

Bill, I think that’s a good segue into talking a little bit about something that you’ve obviously had some exposure to now, in building your AI-first department, which is kind of maybe qualities and capabilities and characteristics of people that you might be looking to hire in your department. How has that changed when AI is a tool that can enhance people’s work? Has it changed the nature of who you’re looking for? Have you noticed there’s certain qualities in certain individuals who are thriving in these roles, where AI is maybe a greater aspect of that role than maybe what you would have looked for in a lawyer in a pre-AI era? I’m just curious as to how you’re evaluating talent and what you’re seeing from your team, and maybe your outside firms you’re working with.

Bill Ryan 27:55

Yeah. I think it’s a question we talk a lot about, Jay, in hiring. To bottom line it, I think we’re still learning. I tend to believe it’s a massive advantage if you just have a voracious appetite to learn. We all talk about the speed at which things are shifting and changing right now, and can you keep up with that shift and that pacing? And as a former teacher, the commitment to learn, to me, is an incredibly distinguishing characteristic. If I want to continue to learn, your ability to be successful seems to be just come out at much greater odds. So, there has to be a willingness of I’m willing to try and be uncomfortable and learn and ask for help, and get better, and talk to people around me, and say, “How did you solve this?” And talk to other companies, other firms, and just continue the dialogue of how do I get better? How do I get better?

Bill Ryan 28:56

And I say this all the time, to me, it’s the most fun part of AI right now, is the ability to enhance your understanding of different topics, or different areas, is just unlimited right now. I mean, maybe it’s a bad analogy, but I used to watch… When I fly for work, I used to watch a movie, and now I’ll chat. I will chat with one of my AI tools about a topic. My son is a electronic dance music DJ. There are a lot of things I know a lot about, electronic dance music is not one of the things I know about, but we want to relate. And you can get into a conversation with the, this is on the personal side, but with Claude or with Chat, and it just fills in the gaps of your learning. And that’s fun. That is a joyful element of this. There’s a lot of hard dynamics, but that ability to consistently learn and grow, we absolutely need that and look for that. So, that is one.

Bill Ryan 29:56

And then I think the other side is it’s a certain amount of, I guess I’ll call it judgment, and it’s obviously a phrase or a characteristic we talk about. But the importance of judgment, and the ability to stay calm and analyze, as the data sets and the opportunities and the ideas are rising, nearly unlimited. If I say, “How should I approach, strategically, this contract negotiation?” I used to probably look at a precedent, or think about my own 25 years of experience. Now, you get 150 ideas. How do I pick that? How do I choose? What is my decision making and how do I make that decision? I can go back and forth, and come to a machine-created suggestion, but ultimately, I need to look at that data set, have the thought process and the ability to exercise appropriate judgment, like the willingness to exercise that judgment, and the ability to look at that data set, prioritize which of it I’m going to analyze to make the decision.

Bill Ryan 31:02

So I think judgment, voracious appetite for learning, and then the last is something I mentioned about. I don’t think there’s been a more important time to be effective at building relationships on a personal level. A lot of this tool sets, we see the skepticism around this. We know every organization that’s ever tried to go through a transformation, it’s hard. You need trust, you need credibility. The ability to build relationships, and being strong on the emotional intelligence side, that you know how to build trust with those around you, have constructive mutual beneficial relationships, is a key element. So, I think appetite for learning, judgment, ability to build good relationships. You can see legal acumen is not like busting through the top. I don’t want to be dismissive of that. I think we’ve been fortunate. We’ve gotten lots and lots of applications for LegalEdge and we have incredible candidates. But I think those are the characteristics that I believe are going to be most significant to help distinguish people with great legal credentials as we continue to navigate what is an ever-evolving landscape.

Alex Su 32:17

Bill, I think we could go on and on here, but we’re running out of time. And as we wrap up, I though I’d ask you, our listeners are probably curious about this as well. Over the next 6 to 12 months, what are you going to be most interested in paying attention to and watching out for with regard to AI and the legal profession?

Bill Ryan 32:35

So I think, and I don’t know if this is 6 months, and I don’t know if this is 12 months, but I think two questions that I’m watching really closely, they’re pretty different answers, Alex. But I think one is, again, people who really know this stuff are very, very focused on the context layer creation and how to leverage data strategically to build the right tool, to build the right output, to build the right agents, to have the right agentic workflow, is really consistently, especially in the legal field, comes back to focused on, and pulling on, the right data set. We know we have the tools to analyze the data. We know we have the tools to create the right output. The question then becomes, do we have the right context layers to drive highly sophisticated, highly reliable legal output? And I think it’s a challenge a lot of different industries are facing right now, which is how do you create that right context layer? And I think how in-house legal departments in particular approach that question is going to be fundamental, I think, to the long term functionality of some of this tooling.

Bill Ryan 33:51

If we can appropriately harness the data sets that we have internally, I think the long-term tooling becomes really, really powerful. I think it’s a challenging dynamic of what’s included in a data set, what’s included in a context layer and what’s not, is a challenging question. So, I think that’s one, is how are these tools supplemented or complemented by the right context and data sets? And then I think there’s been a lot of public discussion about this, but I think we’re all watching a little bit to see how the law firms continue to think about AI and continue to shift in AI. There’s more disruption in the legal industry than I’ve seen in my career, and in any time in recent memory, for sure. I think there are some entities, and some law departments and some firms, who are absolutely full speed, completely disruptive. I think there are others who are in a little bit of wait and see mode.

Bill Ryan 34:52

I tend to be in the camp of we’re not stopping this transformation. This is fundamentally shifting how we’re doing work. At some point, we’re going to get in those 4 different categories of process disruption to redesign the blueprint of how legal work is created. At some point, we’re going to be in that space, and I don’t think it’s that far away. And I think the law firms have a real question, in terms of we will always partner with great law firms, and we are so appreciative of their relationship and their dynamic with us, but the reality is the deployment of AI is largely, at best, inconsistent. There are some that are really focused, and trying really, really hard, but I think they will be leaders in this space, and so how they approach full AI adoption, how it changes their business model, if it changes their business model, how does it change their training models? So many of us benefited from a model of learning the practice of law as part of a law firm. So I think how the law firms, who are obviously so significant to the legal industry, take on a more focused approach to AI will be the other key issue I’m looking at.

Alex Su 36:10

Very interesting. Well, Bill, thank you for taking the time to speak with us today. This was a really insightful and interesting conversation. So, where can people reach you if they ever want to learn more about you and what you’re doing at LegalEdge, and your thoughts about AI?

Bill Ryan 36:27

Yeah. I’m on LinkedIn. I try to be pretty responsive. I’ve benefited from a lot of great connections, especially talking around AI. And that’s the fun part about it. It’s because I think there’s a common challenge for us, and there’s lots of… Everyone, we approach it one way, but I know there’s a lot of other great models that are working just as effectively right now. So yeah, always feel free to reach out to me via LinkedIn, and I always try to respond as much as I can.

Alex Su 36:54

All right. Well, thank you again for your time. Great to chat with you today.

Bill Ryan 36:59

Okay. Thanks, Alex. Thanks, Jay.