Episode 22: Scaling AI Adoption – Beyond Pilots and Demos

The lot's I did not pass everything but the law. Hi everyone and welcome along to another episode on the lot's I did podcast. Today I'm joined by Alex Fortisky Webb, head of Legal Engineering at LaGoura. Alex and I discuss scaling AI adoption beyond pilots and demos. We delve deeper into why Legal AI gets stuck in pilot mode. What successful AI role else actually look like? Now the scaling of Legal AI will help shape the future of Legal Operations. Plus, we touch on the exponential growth of LaGoura as a leading light in Legal AI solutions. Today who is Alex Fortisky Webb? Alex is a seasoned legal industry strategist who has seen the evolution of modern Legal delivery from multiple angles. As a lawyer, a consultant and the Legal Managed Services leader, he began his career in private practice before moving into Legal Operations and Managed Services at EY, where he co-led contract life cycle management services across the mea and later helped build innovative managed service solutions at Norton Rose for Bright as part of their firm's global innovation programme before moving on to Asher's advance to do the same. Today, Alex serves as head of UK and Ireland and head of Legal Engineering at LaGoura, the collaborative Legal AI platform where he champions the adoption of AI driven tools to enhance efficiency, quality and value in Legal work across law firms and in-house teams. The Lopsided Podcast. Four Legal Operations Professionals by Legal Operations Professionals. Amazing. So, LaGoura, amazing time to be in that space. Tell me a bit about what you're doing over there. Yeah, I mean, we're totally heads down to a very focused at the moment on, obviously, product is building very, very quickly and at the same time, we're working with more and more sort of clients and that's taking up pretty much all of the time at the moment. But it's a fantastic time to obviously be in the space. It feels like every day is another announcement of another law firm being onboarding at the moment. You guys are linked in, seeing that big bullet and that notice that comes up every time a new law firm signs up. So, yeah, you guys must be crazy busy, right? It's crazy busy. It's obviously a time where a lot of law firms are sort of interested in this space, a lot of law firms are taking their first steps in AI. But also others, there's a full spectrum, somewhere actually quite advanced and they're still interesting things. And if you think about speed that AI is moving at, what can be done now versus a year ago versus two years ago is sort of totally different and that's driving a lot of the effort. And we're saying law firms, but it's broader, isn't it? It's broader than that. It's your banks, it's your financial institutions more broadly, it's the Foxy 100 companies. Are you guys seeing that as well? Yeah, absolutely. So, in-house teams, I think, also, too, potential, obviously. In some ways, it's most urgent for law firms because it's their core business, it's law. And obviously, we're focused on legal application of AI. So, you definitely feel that, but yeah, absolutely, particularly the larger in-house teams are generally pretty sophisticated around technology and and starting to really think about how AI can be applied to their business to actually improve the service that the legal function gives. And we're going to kind of dull a little bit deeper into the kind of the application of legal AI in a moment. But tell us a bit about you first. So, who's the ask my guest these three questions? Firstly, who's your hero? Oh. Having worked very well for each other. I would have to probably think about that more carefully. I think the, this is a bit of a call-out, but probably the generic version. People in professional context at least that I think are probably unsung heroes and think it's one of the hardest things to do is actually the entrepreneurs, the startup founders who actually don't get huge traction early on, but stick with it and have the grit and determination despite one hearing from everybody else within the community that they're part of, at least implicitly, that maybe they should be doing something else, but also there's a lot of personal sacrifice in that, right? It's very uncomfortable space to be in. If you're, you know, sacrificing a potentially lucrative job, while also being told are, you know, what you're doing doesn't look like it's going to work and so on and so forth. I think that's one of the hardest places to be in. So at an aggregate level, I think guys who can do that, certainly not one, but that's a pretty amazing thing to do. One in particular, or just. Oh, I think there's so many. I think you see it at different stages as well. I mean, you can go back to the sort of Steve Jobs, you know, showing down half of Apple to focus on the right things, which things controversial at the time. So within there's so many examples, but also quite a few that you don't hear about and there's probably some that don't work out. And in many ways, that's as admirable as the ones that do, because it's not always clear really on what's going to be the outcome. Yeah. And what's your greatest accomplishment? Oh, I thought we were starting with the softball questions. Greatest accomplishment. I think, look, I think one thing, maybe it's just slightly raised the first question. Actually, one thing that I'm proud of in my career is I've actually, you know, started life as a lawyer, thought there was a ton of potential to sort of improve the way that service was delivered. And I think one thing that I've done consistently has actually maintained that sort of sense of purpose, and throughout all all the sort of different roles. So seeing, you know, different ways of trying to add innovation to the profession. And that's always been a bit of a constant, even where roles have been quite different, you know, different types of organisations, more services focused, more tech focused, but actually kind of maintaining that, in some ways, is to want to make the analogies to the to the Steve Jobs of the world, but in some ways, it's been something that hasn't always been the easiest thing, but has ultimately been a sort of very satisfying outcome. Yeah. And I mean, thinking about having worked big law, big four, now working at LaGoura, you've really seen the kind of the ecosystem from all different, through all different lenses, that must be hugely beneficial for you right now. Yeah, I mean, it's really so right now it feels like it's possibly the first time I career, I can bring experience from the full range, you know, being a lawyer, being a non-net, doing completely non-legal stuff as a consultant, doing some tech stuff, and then working in those different organisations, all of those things sort of come together when you think about AI, because we're going through this, you know, this obviously significant transformation within the profession. All right, and the most important question of all, which song would you sing on a team karaoke? This is a good question as well. And I'm worried that this may actually be something that I'm asking you in life. Yes, that's he reads. I would probably go for a K-pop demon hunter's song now. Curdcy of having two small daughters who insist that we play that in Sesame. So I'm pretty familiar with those at the moment, probably more familiar than I am with any other songs. The Stromba Lee's Real. Yeah, I've got this, I'm I have to say. Surprisingly good movie. Yeah, yeah, and it's a great soundtrack actually. It's also great. Yeah, we're really worth in that. Final question on this section. So if you wasn't doing this, what would be the perfect job that you'd love to be doing? Ah, this, I mean, in many ways, this is like the perfect job actually. Like this brings together, like I say, it brings together a lot of my background, a lot of the sort of sort of purpose and all star of the career is, you know, this is kind of the moment to I think maximise that, and this is where I commit the most contribution. Don't keep it with something else, it would be something like, you know, I don't know. You're quite sporty. Classic pro sports person or whatever, but I think, yeah, the shocking lack of talent in that song really rules it out. So as I said earlier, given the kind of the legal AI dominance at the moment and exponential growth, I wanted to dig a little bit deeper to the kind of [BLANK_AUDIO] operational aspects to a certain extent. And the first question I had around that was, why do you think that so much legal AI and the fact that you take more broadly gets stuck in pilot mode? Yeah, it's interesting question. I think there are, I think there's a few facts. I think the biggest one is actually this fear of making an incorrect decision. And I think for whatever reason, that seems to be more acute within the legal profession than it may be elsewhere. I think elsewhere there's a little bit more willingness to sort of, you know, if it doesn't go right the first time you've got some learnings, you can move forward, you can do better in future. But I think there is an emphasis and a pressure frankly to get things right the first time. And that probably comes from the nature of legal work itself where you're not getting second chances, you negotiate an agreement or you draft a document, no one is expecting that to be a rough first draft, that's basically, you know, that should be the sort of final outcome or at least could function as final outcome. And I think that that sort of culture, and this is actually, I think we're going to talk about some of the implementation challenges as well. But I think that mindset percolates throughout a lot of this actually, combined with some of the other factors that lawyers generally very time poor, they're doing, you know, relatively high stakes work, has to be high quality, tight deadlines, typically none of those factors really set up well for experimentation, which is, you know, part of what you need to have successful sort of, or at least not get adoption in the technology. And that kind of goes to my second point, which was around, you know, which misconceptions do law firms often have when they start experimenting with legal AI? Is it that? Is it thinking that, you know, it's the silver bullet and you know, the first draft is the last draft? I think that is definitely one. So the time, the time that we see lawyers most disappointed with AI is when they are expecting perfection. And it's usually quite easy to sort of discuss one, why that isn't the case right now, but to reset the expectation slightly to say like, you know, would you expect perfection from a trainee? Is this, you know, a paralegal? Absolutely not. Everyone expects to do multiple iterations. So why are you expecting AI to sort of get it right first time? So I think there are, that is difficult, right? The expectations gap if someone's just decided like, well, I'll use this if it can do everything perfectly. And some extent there's a, you know, there's an answer that's along the lines of, if it could do everything perfectly, you know, none of us would be here. We'd all kind of be on the beach or whatever. So there's sometimes a bit of a reset there, yeah. And what are the, the kind of the most common operational blockers? So are they tech-related, culture, workflow or something else? I mean, you've obviously, as I said earlier, you've been doing a lot of implementations into law firms. Are there any kind of developing, emerging trends that you're seeing over and over as a common issue in that implementation piece with law firms? Yeah, I think the, so the culture piece is definitely, and we see the false spectrum. There's definitely very high levels of variability between firms, which is just sort of surprising if you think about it within a segment because they're all by definition, particularly on transactional sides, doing a lot of the same, yeah, work, right? If you're on one side of the transaction, I'm on the other, we're negotiating the same agreement. So we're doing very consistent, very consistent stuff. But there is quite a high levels of variability in terms of sort of culture and openness to change and to sort of new technology. That often comes from the leadership level. So if you see a leadership team, this goes right down in a law firm to practice group level in house teams to sort of, you know, some team leads or AGC sort of level, where you have real buy-in and focus from people in that group or at that level makes a huge difference in terms of sort of adoption throughout the organisation. I think that you describe that as sort of a cultural lens. I think the other factor is just acknowledging that this is a really big transformation, multi-year transformation in how lawyers will work. That hasn't been the case for, you know, at least 30 years, it's something like the point when email came in or work processing or maybe digital knowledge, but nothing, nothing recent. And sometimes I think it's easier on the rest to make that, which, you know, which don't mean you don't necessarily put enough resource against it or enough sort of mental focus and that could challenge as well. And how is it working in terms of the kind of this, this, this kind of technological war of attrition that we see from law firms at the moment where it's like, you know, there's been early adopters, there's been some that are related to the table. Is that impacting how quickly they look to scale? So in your kind of more conventional transformation, you'd have your pilot group, maybe pilot practice sector market. On law firms going kind of full all chips in with Sarah LaGoura and just say, we want to roll this out across the entirety of the firm from day one, or are they still going down that more conventional pilot route? Yeah, so I think it's probably a couple of the two most most firms, most in-house teams will want to do pilot some sort. And I think that's natural. They want to actually get the technology in their own hands. They want to apply it to some of their own use cases, their own day-to-day work. So that's always that's always part of it. What's interesting is then the variety of approaches to actually rolling out the technology within the firm. And we see, you know, we see basically two versions. So one is called Big Bang, where actually everyone gets a license on day one. There's very little bit sort of upscaling on at least the basics of the platform and maybe some practice group specific elements. And then you, you know, you basically get into more of a BAU process quickly. The other approach is to sequence your roll out a little bit more. And the advantage of doing that is you were able to give each area sort of more white love sort of support. Because obviously you're not doing the whole firm, you're doing a specific part of that. And you can sort of drive a bit more a bit more attention to each one. I know there is a better or worse. And my mind, I think that's just because there's tradeoff, you're just doing different, you know, doing it different ways. Yeah. And are there any particular, I don't want to say problems challenges, but opportunities that you find that law firms are coming to you to try and solve, by a way, of, you know, particular kind of low level legal services that they want to focus, lagour on, or is it just more of a kind of whole scale operational? Let's, you know, see how this can help us without drafting of emails through to, I don't know, reviewing NDAs through to everything in between. How does that looking at the moment? Yeah. So there's, I mean, the complexity of the work that can be materially sort of supported by AI is going up all the time. So, you know, NDAs can absolutely be supported pretty holistically by AI now. But it goes, it goes much, much further. You can start to have reconciliation of, you know, turn sheets against sweets of, you know, sweets of amenade docs, for example. So it can be really, really significant in the types of work that can be, can be kind of done. I think one, in terms of, sort of the client facing element for law firms, we, we sort of released back in, early November, it's a concept of kind of an AI, enables client portal. The idea there is that it gives law firms a way to actually really enhance what they deliver to their customers by integrating it out of it. So you can imagine a world where instead of getting a flat DD report in PDF format, but you then, you know, 200 pages, you've got to read over the weekends. You actually get a version that is sort of, has all the richness of AI. You can view it within the platform, and you can click through to all of the underlying docs, even the individual clause in a contract. Maybe if it's been highlighted, you can look at that context. You can ask queries, you can get a synthesized result from that. So that's one aspect and the other element of that is actually being able to provide law firms with a way of giving their clients AI-enabled services. So potentially you can self-serve stuff like, you know, drop your document in here and get a view based on the law firms IP of how far this document is off market, or where it's off market, for example. And that's quite potential, I think, because this is a way that law firms can potentially monetize all of this IP that they have internally, but that generally at the moment only only earns money when it's charged for bioloil through hourly work. And what does, I mean, or what was the law firm say that success looks like to you when looking to roll out [BLANK_AUDIO] a Ligora into their company. What are those three KPIs that they're usually looking for? Yeah, it depends firm to firm a lot. I think the sort of probably the universal one would be levels of adoption. So I'm using a very high percentage of users on the platform on a weekly or daily basis. And then the more detailed work is around drilling into how they actually using it, what's that for, how much value is it adding? In general, there's an assumption that people are using Ligora to create value. They're not using it to look at their whole day or something like that. Okay. Is there anything around the kind of commercialisation of it as well in the sense of directly building products or services out of Ligora? We've Ligora sitting behind it for an offer to then sell onto clients. Yeah, that would be the, that's a sort of portal example. Right. Okay. I'd be thinking about is there a workflow that the firm could build for a specific client? So it could be something like evaluating a certain situation in the context of the relevant legislation and internal policies and maybe some other guidance that the law firms provide. And that could be something that is set up by the law firm and then can be used by, by the client. And in some cases, it might be, it might be okay that that's completely self-serve and there's no zero touch from the law firm. But it might also be that in certain situations it triggers a request to a specific part. And it's actually, you need to have a look at this because there's some winkle in the situation here. And to kind of change that a little bit from the law firm lens into the in-house council or, you know, client lens, what are the key success drivers that those guys are looking for? Is it around policies, procedures, you know, processes? What are they seeing as the real opportunity in procuring a Ligora? Yeah, within house teams, they, you know, I think assuming out in house teams always looking essentially at how they can improve the service to the business, like thinking that there's like a mini law firm that's embedded in a wider business. And so they're looking at things like obviously efficiency, it's always a critical thing for in-house teams, speed as well, something that's massively facilitated by AI. And then service level, so actually, am I able to provide something that is instantly more useful to, you know, my internal clients than I was previously? And that's actually interesting, and it's interesting sort of set of, sort of use cases or opportunities for in-house teams along the lines of what you were just talking about. So actually, this sort of productisation of certain elements of the work, you know, that's something that they can do internally, potentially between the in-house legal team and business users, potentially is actually give those guys work flow to they can sell server. In terms of scaling AI, how do you think that will shape the future of legal operations more broadly? It's an escape, yeah. Yeah, like, because, you know, there's this consensus, I think sometimes that people are buying legal AI because other law firms are buying legal AI. And to go to my kind of, you know, example of the kind of technological war of attrition, if you will, and just ensuring that, you know, they can go in and say, hey, they have Harvey, we have LaGora, and they have LaGora, and we have Harvey, and so for co-counsel or whatever it might be. What do you think of the material benefits that, from an operational perspective, that law firms and clients can be achieving from procuring these legal AI technologies? Yeah, I think the benefits, the benefits are relatively consistent. It's these things. Spees, it's also quality. Even the most basic use case for AI in the legal context, if you're the most skeptical lawyer in the world, you don't believe that AI can do any of your work. If you do the work, and then you ask an AI tool to just evaluate it, you know, say it's a defense, say, you know, evaluate this for any weaknesses in my arguments, that is a sort of almost zero cost way that AI can potentially uplift your, even if it comes up with 10 things, eight of them you disagree with, but two, you think, oh, actually, okay, that's sort of an okay point. I might just tweak my language. You can get high quality speed is obvious, and you've seen how quickly these systems can take, you know, huge amounts of language in, and then gives a synthesized, synthesized amount out, and that obviously leads to efficiency as well. So it's always some, I think some combination of those things, but the breadth in which it can be applied is just growing all the time. And I think that's the thing that's hard to kind of keep up with for everyone, because even if you, you know, if you buy the board a platform today in six months in guarantee, you'll probably have 30 to 50% more functionality, and the underlying models will be able to do, you know, maybe 30 to 50% more than sales, but maybe 100% more, and that combination means you're into these space at a rate that's very unfamiliar. So you've, I think, you know, lots of people feel like they've, they've just got a handle on the implementation and what they're doing and what the priority use cases are, and suddenly there's a huge amount of potential for them. Amazing. Alex, thanks for joining me. Thanks for having me. Thank you.