What Problem Are You Really Solving? Tyson Ballard on AI and Legal Ops

Yeah. It's a really good question. First of thank you very much for having me. I really appreciate it, Daniel and Eric.

Yeah. I think it probably goes even back from an early mindset early childhood mindset where I've just always been curious with patterns and continuous improvement and essentially kind of questioning the why. I was always a very curious kid. And I think the moment I entered legal, which was a really long time ago, you know, Clifford Chance, I was really lucky and fortunate enough to get an opportunity there where you know, because I wasn't wanting to become a partner in a law firm.

But what I really did see is patterns and the way of being able to kind of standardize and improve those patterns, which has always been a curious thing for me. And I think I've taken that forward into the why, you know, why we created the company that we have today and then also the career that I've had. And I think I've been fortunate enough also to kind of, you know, gather some colleagues and merry band of followers along the way that kind of helped me on that mission. So yeah.

And I think it still excites me today, and then, obviously, we'll probably get into it. But, you know, it's an incredibly exciting time to be in the business that we do.

Yeah. And I think it's an interesting question. I think, you know, what we're seeing today is a plethora of data available to us that probably wasn't available to us when we started this game fifteen years ago. And I think if you can measure, you can improve. And and I think that's kind of the key point to take away here. We're being able to measure nearly, you know, so many things now that you couldn't measure before because you can use the AI tools to take that unstructured data and structured data and then ask them any question that you've ever wanted to ask that you couldn't ask before. Whereas before, you know, you'd be sitting side by side measuring the process or kind of, you know, gathering that data on a much more manual, you know, way of thinking.

I actually think that that's all great, but you still need to be questioning the why at the end of all of this. You know, what is the problem that this process is trying to solve, and what is the human need that is at the end of that process? And I feel that is a little bit of the disconnect right now because we've opened up the candy box to measure everything, but we're just still not kind of bringing it back to that simple question of the why in terms of what is that problem that we're solving.

The work that we're doing is, I guess, trying to kind of, you know, not steer away from the excitement that we have with all those tools that you can harness, but it's still to kinda keep it simple and, you know, focus on that north star of the of the why.

Well, I mean, that's part of our job is to try and change that behavior. You know, I think, full honesty, I think what we're seeing is a mixture of, in big law, we're seeing a fear of a threat of what AI means for their business model. For the in house counsel, there's, you know, there's still that problem of the law firms telling them that they've got all these great tools, but why do they're still getting a rate increase every year, year on year? So I don't think it's quite gone yet to kind of the elephant in the room question, which is probably maybe we'll get to.

And truthfully, I don't think we have enough people asking that question why. You know, we respond to a lot of RFPs, a lot of proposals, like, I'm sure, like, you guys do too as well. And a lot of it is very tactical, map my process, tell me what I should do in terms of how do I drive adoption in my tech, etcetera. But fundamentally, I really don't think we're kind of asking that North Star question on the what is the problem that we're trying to solve for in these particular areas?

Yeah. That's a that's a deep question.

Yeah. I appreciate that. This is like real talk. No. I mean, maybe I should start with the premise that probably I think the average age of someone at our company, Stellar, is probably around twenty six.

So we do have a lot of bright young things, essentially. And and, obviously, they come with just with a different level set of knowledge in terms of they know all the tools, but maybe they don't have a lot of the subject matter expertise because they haven't been there and done that. So I think that, you know, part of building the company that that we're hoping to build is exactly that. Like, I feel like we have a responsibility to develop that younger talent because the job roles are changing in what we do.

I think the second part to your question, which is, you know, it's all a race. It's so exciting. You know, let's take as much commercial advantage as we can in a short period of time and ****** the human job loss potentially along the way is an interesting one. Now I listen to a lot of podcasts, and I was listening to one recently about the founder of NVIDIA, and he was talking about one of the very first use cases of AI when he was talking about radiology.

You know, in the beginning, there were radiology. We can use AI to kind of, you know, provide analysis on results from, radiology exams, and there'll be so many less radiologists as a result. Now what happened was more tests can be done, so there's way more radiologists than are ever in, there was before.

And so I think I'm quite an optimist in that respect because I think fundamentally, people come to a lawyer to solve complex problems, and that purpose doesn't change. I think the repeatable administrative tasks can be done more efficiently. But fundamentally, you still need someone to apply that human judgment at the end of it. Yes, I think there may be more or less tasks on the you know, more lower complex and medium complex administrative repeatable tasks. But fundamentally, you still need someone to glue that together.

I think I was just trying to work out if it was my ninth or my tenth. So this is like I did miss last year.

Correct. I flew from Lisbon yesterday, and I'll leave in a couple of hours. So, unfortunately, I'm not gonna be here to see a lot of the great talks. But, yeah, I'm very thankful that it's this side of the states right now because, you know, we can get a direct flight here.

I'm always curious to learn new things. So, like, what what has amazed me even just walking around the halls yesterday was, I mean, in terms of the kickoff, was how many new vendors are out there that I haven't heard of, which surprises me considering, you know, we're all in the same business, and I feel like we should be at the forefront of knowing who's new and great. You know, obviously, everyone's jumping on the buzzwords in terms of AI, so there's nothing that isn't AI. I think what I'm really looking to get out of it is, you know, I feel like, you know, we need to get stronger as a community in terms of collaboration. And that's why I love coming to these things because there's a whole bunch of us in one place at one time, and we're we're not on the end of a computer laptop screen, you know, we're we're in three d.

So I think what I get out of these is typically kind of like the, wait, oh, you know, you're working on this or, you know, you're doing this or you're using this tool to solve this problem. So I'm looking for probably those more corridor conversations by the water cooler or, I'm not gonna get a chance to see most of the talks because I have to leave later today. But I've gained a lot in the past from those talks originally in terms of, you know, frameworks to adopt and, you know, lessons that they've learned in terms of how they've delivered. I'm probably less interested in my tool does this and that the sales pitch around those tools because I feel, you know, I'm pretty abreast on those.

Copyright, Tyson. No. Actually, I think the more of us talk about this, the better in terms of how we share. Like, I don't pretend to think that what I'm doing is the or what we're doing is, you know, the panacea, but I think it's a really good question.

I think there's kind of three components to that, which, you know, at the law firm, I see a real resistance to change. So but what I'm seeing driving the change in terms of the why is the clients that are, you know, requesting the work. And I really feel like maybe the clients are underestimating the role that they need to play to kinda drive that change. And, you know, I think they're not insisting on it enough.

We're really fortunate to be a very young company that get tapped on the shoulder when people really wanna ask that question, why? However, sometimes it's not always the right question. And so how do you tell them in the nicest way that they've got an ugly baby and whilst obviously, you know, not jeopardizing the relationship? I think that's a really interesting dilemma that we have.

It's the art. It's a dichotomy. But, you know, I think in twenty years in this business, you know, I'm not a lawyer, but I feel like one skill I have quite got good at is letting lawyers feel like they've come up with the greatest idea. And that for me is kind of the me telling them that, you know, this is the problem and how to solve it is not the right way to go about it.

I need to guide them to understand that that they need to ask that question themselves and then get them to the answer. So quite often, you know, and I learned this really on way before I was in legal that that is a real kind of secret gift to kind of driving change, I think. And when it happens, and I don't think it happens all the time, unfortunately, but when it does happen, it's incredibly thrilling and incredibly rewarding and, you know, I've walked out of rooms going, yes. You know, like, I've I've really helped them here and I think that's why I get up out of bed in the morning.

Yeah. Excellent. Thanks guys. Have a great day.