#81 - The New Structure of AI Powered Law Firms

Not really anybody's job to go figure out how to transform into the AI powered firm of the future.

Are you building your firm around AI or are you trying to retrofit it by forcing AI onto it?

Hello everyone and welcome to Pearls on Gloves Off. I am your host Mario Carroll, Chief Operating Officer at Goodwin. Today we're diving into one of my favorite topics, how AI is changing the way professional services firms operate and what it means for the future of law. I'm joined today by two fantastic guests, David Duncan and Tyler Anderson, who both spent their careers helping organizations navigate disruption and lead through innovation. David Duncan is author, advisor and speaker with over 20 years of experience helping organizations drive innovation and growth. A longtime collaborator of the late Clayton Christensen, yes, the Innovator's Dilemma and Disruptive Innovation Guy, David has co authored in influential works on disruptive innovation and has guided Fortune 500 leaders through these complex strategic transformations. Tyler Anderson is CEO of Disruptive Edge and a global thought leader in enterprise innovation and strategy. As an award winning entrepreneur, Tyler has spent his career helping major organizations embrace new technologies and turn disruption into opportunity. Together they recently wrote a really insightful piece for the Harvard Business Review called AI Is Changing the Structure of Consulting Firms. And I have to say when I read it, it immediately clicked for me. So much of what they described about how AI is transforming consulting is exactly what is happening in the legal industry to where the traditional pyramid model is beginning to evolve. AI is enabling lawyers to focus more on strategic judgment, creativity and client relationships while automating that lower value, repetitive task work. It's also changing how firms organize talent, deliver value and scale expertise. In our conversation today, we get into all of that how AI is transforming the business model of consulting and what law firms can learn from it why the most successful firms of the future will be those that blend human judgment with machine intelligence and how leaders can rethink culture, structure and collaboration in this new world of work. It's a fascinating discussion about not just what AI AI can do, but how it's changing the way we work and what that means for the future of this profession. So let's dive right in.

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Tyler David, thanks so much for coming on the podcast.

Great to be here.

So excited. I reached out to you guys because I Stumbled upon this Harvard Business Review article called AI is Changing the Structure of Consulting Firms. And I've read through. I was actually on the airplane when I was reading through this and I was like, wow. If you switch out the word consulting for law firms, I think these people just basically took all the thoughts in my head and articulated it really well and put it into this nice article. So I felt just compelled to reach out to you because we are obviously like minds and would just thought this would be a great topic for us to get on the podcast and discuss. So what made you write this article? And like, where did this thought process come from?

Sure, yeah. And it was great to hear from you, Mary, and also to learn what's going on in the law space and all of the parallels between what we're seeing in consulting and law firms. Tyler and I have both been working in consulting firms for 20 plus years, and I think for most of that time it was in the context of traditional models of services firms that have been around for about the last hundred years. People refer to as the pyramid model, this kind of professional pathway that goes from, you know, either entry level associates or analysts, and you kind of progress up into sort of project manager roles and then often structured as a partnership where you have partners at the top and the whole kind of organizational structure and the professional path looks like a pyramid. It's very deep in how much it shapes the culture, the economics, the incentives, how you compete and what competitive advantage looks like, as well as the client experience. If you've grown up in, in that world, as I have, you're just really embedded in that kind of construct. And with AI in the last couple of years, as I started playing around with it, and I know Tyler too. Tyler's a very lead user of AI, the resident expert on all things AI among the two of us, I realized just quickly how powerful it was, particularly for the things that, you know, entry level roles do. I mean, I think it has applications at all levels of the. But it struck me, it just kind of. I distinctly remember the first time I played around with ChatGPT. I was experimenting with it for some work streams that, you know, we might have a team work on for a few days. And it was giving me pretty good answers, you know, in 10 or 15 minutes. And I thought, oh my gosh, this is going to change things really fundamentally. And that was really impetus for this article. We just started thinking about if you play out the implications of that in terms of how it might change the structure of firms. At a minimum, starting to make them from these wider pyramid and narrower pyramids. And, you know, there are other things we can talk about. Your looking at a pretty dramatically different thing in the future.

Yeah, we're seeing a very similar thing with legal. And the whole premise of the article is that AI is coming to sort of be the disruptor. So many people, and I've said it on this podcast, I've said it many places, talk about, you know, which AI tool are you going to buy, which software is working for you, what are your use cases? I think that's a good and interesting conversation, but it ignores kind of the bigger picture. Like, that's one piece of what's happening. If that actually works, the implications on our fundamental business model are huge. And we need to be having those conversations about, like, okay, what happens on the day when this whole model is disrupted in a bigger way than kind of chipping away at the edges right now, because we know that the AI is progressing, but at a much faster rate than anything we've seen before. So this is coming at us like, do you think we should be thinking about this with a sense of urgency?

Yeah, I mean, I can take that one. The, you know, the sense of urgency, I think, is super crucial because this is happening so fast. I mean, your original question about kind of what led us to write this? For me, I'd run Disruptive edge for about two or three years before Generic came out. I think that was November 28, 2022. And the second I used it, I just knew this was going to reshape a lot of how we deliver our core services to our clients. And every 12 months, to your point of, you know, urgency, every 12 months that go by, we look back and say, how did we deliver this 12 months ago? And it would be so hard to go back to that model because we've gotten so used to how AI enables us to deliver better and faster today that we can only imagine how other organizations that are going to go through that learning curve. I think starting to learn about what that looks like for your company now and how that evolves is super important. And it's certainly not too late to start that journey of learning and beginning to experiment on both your core model and then what net new ones does AI open up from an opportunity perspective for you and starting to figure out how that structure looks like and how to kind of test and learn. I think there is a big urgency, but it's definitely not too late.

Yeah, you've mentioned Disruptive Edge. I know that is your consulting firm. You Guys describe it as an AI native consulting firm. We are seeing AI native law firms sort of emerge. What does that mean to you? How is it different for us?

Kind of look at it as are you building your, your firm around AI or are you trying to retrofit it by forcing AI onto it? And so the difference for us is are you trying to do the things you used to do in the same way just with AI enhanced research, or you know, make this content better or, you know, tacking on things? Or have you really explored how can AI change how you approach solving that problem and designing your services around that? And that's the difference we see as an AI first company. And so one of the things we did is one of our core products and services is called Domain Ignition. And essentially what that does is large organizations will say, hey, help us explore an opportunity area. And we would support them in coming up with how they should approach exploring growth in that opportunity area. And AI can actually do that extremely well. And so we launched an organization called Octus AI, which kind of runs as a sister company to us. And it's a fully AI first business that replaced our traditional way of doing it and automates a lot of the research and the validation and all that kind of stuff. And so it took a very AI approach to solving that need for customers. And we've designed our services now around enabling, supporting that and helping customers ingest it and use it. Where the non AI first approach would be, we're doing the work and we're just using AI to speed us up and kind of tacking it onto our existing process. And we think there's a ton of AI first opportunities in professional services that can be net new or built based on an existing service.

Similarly, the emergence of these AI native law firms that are obviously much smaller that have come out in the last year or so, and they're nimble and they're able to create from scratch. Right. So they're not bogged down by all this sort of legacy model that the incumbents have. And they can be AI first. They don't have the pyramid, they don't have the incentives to structure. So it's going to be a competitive advantage for sure for the new firms sort of emerging now. And it becomes very difficult. This is traditional Clayton Christensen innovators dilemma for the big incumbents to realize what's happening and to make that shift.

Yeah. Play for a bunch of years at my previous firm and as you obviously know, his famous book was the Innovator's Dilemma, which is Exactly. Describing this challenge that not just services firms, but every big successful company has and getting kind of locked almost in proportion to how large and successful they've been, get locked into a way of doing things in a certain economic model and business model and set of assumptions about what they can and can't do for very good reasons, right? I mean, it's not, it's not that they are blind or anything. It's that they're actually doing what they're supposed to be doing and maximizing this great organization that then created. But the inverse of the innovator's dilemma is called the attacker's advantage. Which means that to your point, Mary, like, you know, if you're a small company, as ours is currently other growing fast, you don't have the constraint. I mean, you're so. You don't have all the resources that a big firm has, and that's an important difference. But you also don't have kind of legacy constraints that. That cause the dilemma.

Yeah, that's right. Okay, so let's talk about the things that we're seeing. AI is chipping away at the junior level consultant work, the junior level associate work, paralegal work in our space. And that starts to affect what is known as the pyramid structure, right? A few partners on top. As you get to the base where you have the more junior folks, it gets broader. We call that leverage. And that's how firms become profitable in just the way that they staff their matters. What does the future look like from that pyramid if it is crumbling, as you say?

The kind of core metaphor in our article is this idea that the wide pyramid. Here's my visual aid demonstration. Will kind of narrow to something that's much more vertical, right? Where you have more parity between one level and another. And we call that the obelisk model, right? Because an obelisk is sort of, you know, like the Washington Monument, which looks like a very narrow pyramid. So that's one change, right? You'll have a skinnier kind of career pathway among the professionals. The other couple of changes that we asserted in our article is that you'd have probably fewer levels, right? But the minimum viable number of levels is three. Those levels are just derived from thinking about like what are the fundamental functions you need. And a services firm to provide services, right? You need some people that are kind of doing the work, right? That are doing the analysis or the research or developing communications and so on. You need someone who's kind of responsible at the level of project management, who's going to Kind of drive and, you know, keep the trains on track, which is a very different skill set than the, than the doing the work people. And then you need some kind of senior client people who are going out and cultivating relationships, building relationships, kind of bringing the breadth of their deep experience of problem solving and so on. And that's the minimum viable configuration. So that's kind of the second characteristic that we were describing. And the third is that even though you still have those roles that might look a little bit like what they looked in the past, they're going to have different responsibilities and skill sets that are needed, particularly at the kind of junior analyst kind of associate level. I think this is inevitably going to be true. It also creates a problem around talent development and apprenticeship. We can talk about that. But they're going to be using AI tools. They're going to be doing maybe a lot of the same work or the same type of output, but the way the work will get done, it might be more about managing AI workflow. Very sophisticated and in the way that you use these tools. And it's kind of like a bionic human, right, that's still doing knowledge work, problem solving, analysis, but doing it powered with these AI tools.

And I would add one of the key concerns that we've seen people be having with the pyramid crumbling is on business model and pricing and how they're going to still get that leverage. That has done quite well for five decades now and potentially longer. And I think that pricing doesn't necessarily need to decrease as part of this unless people do not mean less billings and less business. I actually think it can mean quite the opposite. I think higher client value can be delivered from a quality perspective. I think it can be delivered faster and potentially in new ways than we've seen in the past. And so redesigning your organization to deliver in that model does not necessarily mean that it's definitely not a race to zero, in my opinion.

Yeah, let's talk about that because that comes up a lot, right? So as a large law firm now, we have a lot of clients who are calling up and saying, how are you using AI? And the immediate follow up question is how is that going to start to show as a discount in my bill or how are my bills going to get smaller because of it? And I agree with you. I think certainly AI can help make us more efficient and we're not seeing a ton of, you know, impact yet. We're still, I would say like 5% around the edges, if we're being honest. And I think in house, teams are finding the same thing. So when you have that discussion, they're actually not surprised by that. But you know, it's changing fast. We're getting there, as we talked about. But AI can also create more client value. It can actually add, you know, do things that we couldn't do before. We provide insights in a way we can't before because we are investing in different skill sets, in different talent, in different technology. Where do you see the pricing structure of professional services firms has that change? Our consulting firms similarly, on a very billable hour sort of time and materials type model. And are you guys seeing pressure to change that?

For us, we've always been an outcome based billing organization from the start. So and you know, we do the odd hourly engagement. Just when organizations expect a different engagement.

Model and there's a time and place for that, right? Sometimes it has to be for sure.

There will always 100%. And I think that service provider should have flexibility to meet client needs while having a preferred model.

Right.

And so for us, if you think about outcome based pricing and if, you know, you're a lawyer that needs to do some sort of settlement, you estimate your fees at maybe $35,000 and it's going to be six months from now and hopefully meet the, you know, the goals we hit. But if you can say to a client, hey, this is going to be the engagement, these are our KPIs and we're going to charge a flat fee of $50,000 and you know, maybe there's an accelerated pricing scheme if that can be done even faster or you know, a stretch goal or anything like that, it starts to create a more mutual benefit incentive scheme. And it also incentivizes the legal practice to be as efficient as possible, which will then turn into client time savings where, you know, today there's a lot of pros in the model. But one of the things that I personally as a legal consumer think is maybe not a pro, is there's an incentive for things to take longer or require more emails or more back and forth, which of course they, you know, maybe not the standard, but I think there's a lot of potential upside for both the legal organization and the, the end consumer by adopting the AI, tooling the models, the efficiencies that come with this.

I'll just add, I think there are ways that you could reimagine, you know, the value proposition to be even more compelling beyond just outcomes or speed or whatever in terms of the product itself. And I won't speculate on Lock. Because I'm definitely not an expert on that either. But I'll just give you one example. So we had a client, you know, we do strategy work. We had a client, wanted help with strategy, a particular strategy question. And it was something that involved thinking through like big kind of seismic changes in the industry, right, that are, that aren't like just business as usual, but you know, they could really reset kind of the way competition works. And so it's the kind of thing that if you were to do strategic planning and you need a way to explore bigger cascades of moves between all the big players, and it's the kind of thing you would do a strategic war gaming exercise around, if you've ever done that type of thing. The way that's worked traditionally is, let's say you're. You get 20 people from your company in a room for a day long or a couple day workshop. You divide into teams. Each team represents a different competitor or stakeholder in your industry and thinks about the world from that perspective. And then, you know, have a consultant come in and facilitate it. You set up scenarios and then you do these rounds of moves. Each team acts as that player would act and you see kind of what everybody does and you react to each other and it's a way to get insight into these bigger shifts. So that's what they wanted. And but what we proposed was that we would do that, but we'd build a new AI tool to facilitate that. And the tool was instead of having your, your own humans be competitors or stakeholders in the market, we create AI agents that represented them. And then we train those agents to act like that company's executive team. And you can even do it down to the level of, well, what would the CEO do? What would the CFO do? And you could train it to almost be a digital twin of a competitor. And then you have them acting through this war gaming exercise. They'll make much more accurate decisions than a human team would. They will also. You can also explore a lot of things. And so we actually kind of rethought the whole idea of doing a strategic war game empowered by AI. Like what could you do? And they loved it. Right? And I think that's the kind of sort of blank piece of paper, you know, blue sky thinking you could do. If you step back and say, our client has this problem, we used to help them this way and it worked great. What's possible now that we have these AI tools, What could we build that could really wow them and, you know, redefine that Experience.

Yeah. And that's a win, win. You know, I keep saying like the future state that I see is win for all the parties involved, the client, the firm and the individuals working in that firm, whether it's consultants or lawyers. Why is it so hard for us to get, get there? The big consulting firms or the big professional services firms, what's holding us back?

I'll be honest with you, like even for us it wasn't an easy journey. And we're you know, between 40 and 50 people, quite agile and able to adapt. And whether it was, whether it's new AI tooling, new products and services, with AI we're launching, there's change management and frankly even corporate structure changes we've had to make as a small business, including spinning some of them out in order to truly allow them to thrive. When you take that and look at it from a large organization perspective and you know, instead of 40 people, it's 40,000 people, the changes that would need to be made to truly kind of allow these things to thrive are significant. There's so, so much success that these organizations see that AI is definitely alarming and a cause for change and everyone is building an AI strategy and thinking about to do. But a lot of the organizations we've met with, they're seeing changes, but it's not a dire situation or anything. That's a cause for immediate knee jerk reaction. And so what organizations we're seeing are trying to prepare for is, you know, how do we start to learn about this and what's going to be the most, make the most sense and where we go with that is, you know, don't suggest to your partner, team or your managing director, whatever, however you're structured, that everyone's comp model should be completely changed next year. But how do you start to look at how you can deliver in new ways with clients, Test a few of those out. What works, what will provide a lot of value and build a place for those to kind of spread, whether that's one business unit or a specific client engagement or anything like that improve why this could be better rather than trying to force change over the whole org kind of on day one you mentioned.

Compensation and I think that's all about incentive alignment of the firms with kind of the disruption that's happening. I see that as a big challenge for law firms. Does that exist in the consulting world? Like what were the status quo sort of compensation models and how does that need to change?

I think it works fairly similarly around, you know, partners get, you know, base salary And a bonus and bonus is based on performance that isn't tied necessarily as directly as I understand it is in law to kind of billable hours and things like that. But it certainly relates to how much value you create for the organization in terms of sales. And what's interesting, we didn't write about this in the article, but we're writing a follow up thing on it in terms of like what was competitive advantage based on, you know, in the pyramid model and how might that shift? And among other things, there's four or five different things, but one of them is scale. There's sort of this assumption that doing bigger things is better even if you're thinking about it from a client perspective. It's better in your services firm economic model because you're employing more people and keeping them busy. But there's also this assumption, and I think it's sincere, that if you're doing big things for your client that are expensive, you're having a big impact. And so there's an advantage to scale and that goes into the incentives that you're asking about. And but to Tyler's point, I think that scale advantage is going to go away eventually and it's going to be about what are going to be the new sources of competitive advantage. I think it's going to be outcome like what Tyler mentioned. It's going to be speed potentially. And then I think AI workflow, you know, app related tools like the one I was just describing, where you've got a new service actually that's kind of integrating AI from the beginning.

How do we think about incentivizing adoption then? You know, when AI tools are sort of at odds with this billable hour and incentive compensation model that we currently have. Is that a challenge for consulting firms as well? Are they sort of struggling with this paradox?

Yeah, I mean, I personally wouldn't incentivize doing something if it's at odds with the compensation model. I would suggest switching the compensation model and the structure if we realize that it is, you know, something that is going to be meaningful to us over time. And then if it's a test, incentivize, you know, that accordingly. When I think about that, that is a big piece from an efficiency perspective of how do you actually get our organization to adopt these tools. And I don't think just saying, hey, you know, use AI for the sake of AI, especially if it goes against the business model, is an effective way of doing it. But really drawing how AI will impact their value to clients and make them more successful in Their job is absolutely fundamental and at disruptive edge. It wasn't until we were able to uncover that across our engineering division, design division and consulting division that things really started to click. And you know, we started with the hey, you know, weekly, let's all meet up and share new AI tools and hopefully people will use them. And you know, that doesn't really work until you can directly correlate them to, you know, client value or outcomes for that specific employee is what I've found. And you know, I'm trying to think about what would be most interesting takeaway for legal firms when they think about that. And for me, you know, if I was a managing partner of a large legal firm, what I would want to understand is where does AI pose a risk just to understand that. And that could be across fraud or, you know, I think there was recently a big report that went up from a consulting firm that had a lot of some hallucination from AI within it. And so where are the risks that I need to be aware of? And then after that I'd want to understand, okay, where can this make the business I deliver today better? So that's from an efficiency perspective, a research perspective, maybe a client management perspective. And you know, what are the tools, solutions that exist and what's my ROI on each of those going to be? How do I test and pick which ones? The most interesting, interesting thing that I think present day allows us to explore is, you know, where can I win through AI and where can I get net new clients and blow away and further ingrain my existing client relationships? And I think there's huge, tremendous opportunity. I personally don't see AI as a risk. I see it as a massive opportunity. It's a risk to organizations that are maybe not going to invest in learning about it and trying to understand it and that will eventually lead to a medium term risk for them. But for the organizations that are testing and learning about it, it's a huge opportunity to provide new ways to interact with your customers, deliver value to them, create more enriched data locked relationships with those customers. You don't have to spend millions and millions of dollars building something. You can partner your way there, you can invest your way there. And then if you do want to build software is so democratized now with all these AI tools that we used to pitch proof of concept of software applications and it was about a six month delivery and you know, you'd spend a million dollars building it. We now bring that same software to the initial pitch because that's how Commoditized and democratized it is today. That means that organizations, because everyone, you know, everyone's been through the last 20 years where, you know, we tried to build software, maybe it worked, maybe it didn't, maybe it was just flat.

Wow.

There's a whole new version of that today where software is super easy to build and so are AI workflows. My point is there's tons of value extraction and new ways to engage customers.

Yeah. And that's awesome. And I think that is the future. Like when you look at the staff mix, it's just a very different makeup now, or at least in the future. Do these skill sets, do these roles currently exist at these firms? My guess is probably not. And how do we upskill, up level or even start to introduce new roles and figure out how that's going to all work together?

Well, so one of the other, one of the questions you asked earlier, you know, what gets in the way of kind of pursuing this bigger vision around AI and everybody has a job to do, they have a day job and it's not really anybody's job to go figure out how to transform into the AI powered firm of the future. So I think to do some of the things that Tyler's talking about, you need your leadership team to kind of align around a few things that will enable it. Otherwise it's going to be experiments kind of percolating around here and there, and they're not necessarily going to be the things you want to really invest in. But you need your leaders to align around a few things. Like what do we want to focus? What are the big, most high value use cases? We want to explore first, to Tyler's point, around risk. You need to put in place some things around governance and ethics and risk. But there are good solutions that are known about how you do that and a kind of minimal operating model for what your AI, you know, how are you going to actually explore those use cases, how are you going to roll them out and so on and then you're off to the races. I think in what Tyler's describing, my answer to your question is I don't know the answer. We got a ton of feedback on this article, which was really exciting. And I'd say, you know, of the thousand or so either emails or comments we got, the most prevalent theme was this one around, okay, what does this mean for the talent model? Because, and I don't know how it works in law, but in consulting.

It'S counterintuitive. It was to me initially, but people who have more experience are Often able to get more value out of AI than maybe younger people who are more familiar with the technologies. Because you have judgment that you've derived from long experience. And so you can kind of go back and forth with AI and assess whether it's giving you something good or bad and you can redirect it. But the judgment you have was based on doing a lot of stuff the old fashioned way, as I did when I grew up. I had to make slides and write communications that weren't very good and get feedback on them and learn to be a better problem solver. But now if you're asking a brand new person to simply use an AI tool to do that, they're not getting that apprenticeship. So it's a lost apprenticeship. And I think it's a big open question, well, how do you build judgment in people that are newer to roles when AI is basically replacing the need to do things the old fashioned way? I think that's a huge issue. I don't think that's the new talent profile necessarily though. In the entry level positions it could be a similar talent profile. But you somehow have to train them to use. You certainly have to train them to be able to use these AI tools to understand how to use them effectively. But that's different than being an engineer. Right. It's just like I'm not an engineer, but I use AI all day long. But I don't know how you give them the craft, the profession that you learned the old fashioned way. I think that's a big unanswered question.

That is the big question that comes up over and over. You know, if we don't go through all the reps the old fashioned way, how will they know? And I agree with you, the people who find the tools most effective and, or can even assess that. Right. Are the ones who kind of know the answer that they want the tool to come back with at this point. And I've said that a few times on this podcast, just if you know the answer that you want it to respond with, it's helped you do it faster. And you can glance at it and go, yeah, this is it, this is. I just gotta tweak.

It's good news for us older people, at least for now.

Yeah. So I think the question for, you know, the next generation and look like so many of us have moved to hybrid work models or remote, we're not getting that apprenticeship as much as, you know, former generations have. That is a challenge. And like going back to, you know, if we find a use case and We've got a team and they want to use it, or if they figured out how to use it, it. We're butting up against our incentive models where they're just fundamentally at odds. Right. We have for our associates, at least in the legal world, there's billable hour targets. I don't know if you have that. In consulting. One criteria is the number of billable hours you achieve. And so you have an incentive to crank out more hours. Now if something's going to save you time, you start to, you know, be at odds with that. And that's very natural. So you start to think about, okay, well, then we have to change that incentive model. We have to change the pricing model, and we have to change the way we recruit and the way that we train and the way that we look in terms of, you know, a pyramid and the number of headcount we need at different roles and what new roles we have to create. And then the. I would call my team, which is the operational team or the business professionals that you need. You know, you start to need engineers, data specialists, and different types of roles. Oh, my gosh, it's very overwhelming, you know, all the things that we have to do. And so again, I look at the AI native boutiques versus the incumbents, and sure, the incumbents have scale, they have breadth, they have international reach, they have, you know, reputation. Yeah, all these great things going for them. You know, how long is it going to last before this starts to really.

Be a threat, you know, for me, and I can only speak out of my personal experience with legal firms, we use AI like crazy. So it's fully contextualized on the issues we're trying to solve or the contracts or anything like that. And so our workflow dealing with our legal team, which is external, we will actually have AI draft contracts or do reviews. We actually have an internal agent in the company called Legal Bot, and they're familiar with all of our risk tolerances and standard procedures and all that kind of stuff. And you can ask some questions.

Just to be clear, it's an app that you created for disruptive ads, right?

Right.

Yeah, yeah. And it's trained on all of our data, what we've signed in the past, what we haven't, what we've reluctantly signed, you know, things like that. And so we use that to draft our contracts, and then we send it to our legal counsel for advice. And personally speaking, I'm paying for the expertise of, hey, help us make sure we're doing the right contract here. We're not going to get in trouble and it's the best thing we can possibly do. And I still have a trust agreement with that external council where, you know, I would pay the same amount, potentially more to have that expertise transferred over and reviewed and that contract we send and then, you know, here are the changes that need to be made. Send it back and we can do that in a day instead of send it over to them. There's a week or two drafting period, they send it back. I would pay more for that immediate kind of workflow where they're reviewing content we send and making changes. And I still have that confidence and trust from, you know, the person with the expertise to do it because AI is really good and it changes a lot, but it still needs. We've spent a fortune on the validation and sourcing of evidence and how to find hallucination. You know, we've designed that workflow and we work with our legal counsel in that way. And I would suggest other legal counsels design similar workflows and then, and then. David, I'll pass it to you in a second. The other aside is that there are still customers who totally don't work that way. And large corporates, which major law firms serve definitely aren't going to be working that way. I would say in the very short term trying to learn how those models work, how you engage with your customers in those new ways and then scaling it to your slower, larger customers. 80% of revenue. I think there's a really natural kind of progression there. But David, what would you say?

Well, the first thing I would say is, you know, I think it's easy for big successful companies to beat themselves up about, oh, you know, there's all these new changes happening, how do we adopt them, we're too slow and so on. But it's the goal of every startup to become a big family. Like you've already won the game in a sense. Right. So that's I guess just the framing I like to repeat because otherwise it can become very self critical. I think the first thing is you start with strategy. You need to get alignment around the leadership table on what kind of firm do we want to become in the AI era, say three, five years from now, what do we kind of transition into? So you know the destination you're going to and then you can set up a process where you start to explore like you're not going to commit all at once. If you were to set out what that destination looks like, you're probably wrong about some percentage of it. So you need to set up a process where you can explore what that new model looks like with use cases that you choose carefully. And then I would think about setting up your incumbent model, call it model A. Like that has to pay all the bills, right? Like it's a precious thing. And you can set up a kind of another unit that, where you could build essentially an AI native model. It's kind of separate, leverages some of the same capabilities and you could let that kind of iterate and explore new concepts. And then the hard part will be, you know, how do you evolve the mix between the incumbent and the new over time? But I would start with strategy and then I'd set up some sort of mechanism to have an unencumbered sort of AI native unit that can explore and build towards this new future vision that you set up with your strategy.

Yeah. All right, so if you're looking at the consulting world, which you're much more familiar with, where do you think it goes in let's say five years? What does it look like? Is there consolidation of these startup native boutiques? Do they start to chip away? Is it just at the bottom? Do they move up, up, you know, to the, to, to higher value work? What happens to the incumbent firms? Do they start to look different? Do they go away?

Where I think there's serious opportunity to play right now is I think the future of expertise and a lot of, you know, these IP driven consultancies is in really specialized AI workflows and having them become the, the kind of pinnacle of the market. So like for example, McKinsey sat down, I'm assuming, and created H1H2H3 one day and that has been extremely successful and a fantastic way for organizations to think about scenario like investment over different time horizons. I think we're in a really unique opportunity right now as AI is new and matures to create a lot of net new IP where people are going to build the best version of the corporate strategy workflow. We've built competitive warp gaming simulators. You know, Okta has built the best innovation product launch workflow. And I think there's an opportunity for that expertise to be built and then consulting delivered services around that is how I think these things are going to progress. And I think the traditional incumbents are going to snatch up boutiques and organizations that build those and deliver their a lot of their services through those which they'll love because it's largely a much more scalable model for them. It can be higher margin and it can be a recurring revenue model. Which a lot of the consulting firms are continually trying to move to. And so that's kind of my view on how these things are going to advance. But, David, what would you add?

I think there is going to be an explosion of these AI native boutiques. Certainly the big firms that are going to thrive in the future, that we're going to be the ones that are able to reinvent themselves and adopt these tools at scale. I think clients are going to have more power, like, they're going to have more choices, and they're going to have the ability to use a lot of these tools directly themselves. Right. So they'll be like, well, I don't necessarily need an advisor for that. Those are a few of the traits of the future world.

Yeah, that's interesting. The last point, I was going to make the same, which is, I think in consulting, if. If you consider one of the competitors, the clients who are able to do a lot of the work themselves. Now, we certainly are seeing that from a legal perspective, where in house teams have access to tools, are getting better about leveraging their own resources to do some of the work. We have sort of the benefit of they still, you know, want to sort of mitigate their risk and to be able to have that partner with 20 years of experience and judgment look at something and sort of sign off on it, giving them peace of mind. But maybe not as much in consulting, so maybe more of a threat to you all.

Every company in the world is trying to figure out what to do about AI, not just services firms. Right. And a lot of them have the same challenges that we've been talking about. If you're an advisor, you know, if you're McKinsey or a smaller firm like ours, people need help with exactly this problem. Right. So there's a whole new category of service line which is help us figure out what to do with AI, how to implement it, help us through that journey. So the question for you is, I've got to believe there's an equivalent to that in the legal world, right? Like, are there. Is there a whole new set of problems related to legal issues that companies have because they're trying to go through this AI transformation? Is that a whole new line of business for you, all the type of problem you have to solve?

Oh, oh, absolutely. I mean, I think the demand for legal services has exploded because of what's happening in the AI space, and you have all sorts of implications for how to set up things within companies. So, yes, it's definitely a boom for, I think, legal services in the short or maybe even long term. Regardless, I do think, you know, to the point of your article, a lot of things are still getting disrupted and will change as a result. So it's a. A fascinating time to be doing, I think, all the things that we do.

It's really fascinating to learn about what's happening in the legal industry and to think about the contrasts. And you also asked me several questions I need to go think about more. I don't think I have good answers to you, so it's helpful to know what's on people's minds. So thank you.

Yeah, it's an interesting time. I kind of see consulting as slightly ahead of where we are in legal and always in terms of our business model transformations and the way you're working with clients. So we have a lot to learn from pricing, from staffing, and appreciate your time again. I look forward to the next article because like I said, the first one was, was so, so great.

Thank you for hosting us. And my parting words would be, you know, think about AI less as a risk and something you need to mitigate against and more of an opportunity because there's so much change, everyone's wondering what they should be doing. There's going to be a lot of winners that come out of this and, you know, organizations that invest now have a huge opportunity to win in the future.

So one of the reasons I love Working Retailer is I love that spirit. So. I agree.

Absolutely. Mindset is like 90% of all this. So onwards and upwards. Again, thanks so much for your time and to everyone who's listening, we'll catch you next time.