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The Margin is a podcast from MGI Research that explores the evolving world of business monetization. Hosted by MGI Managing Directors Andrew Dailey and Igor Stenmark, the show features candid conversations with founders, CEOs, product leaders, and industry experts at the forefront of pricing, billing, and revenue operations. Each episode dives deep into the strategies, technologies, and trends shaping how companies generate, capture, and grow revenue—from subscription and usage-based models to AI-driven monetization. Whether you're in finance, product, or IT, The Margin offers practical insights to help you navigate complexity and drive growth in the digital economy.
Andrew Dailey: Good morning, good afternoon, and good evening. My name is Andrew Dailey with MGI Research, and I'm joined by my colleagues Igor Stenmark and Ethan Weiss, and it is our pleasure to invite you to Objection, Your Honor! An in-depth discussion around the topic that's really the pressing topic in the area of contract management, and that is what's the fit for these new, upstart, fast-growing, heavily invested legal AI tools, versus the classic CLM suppliers. Is it an either-or kind of proposition, or is it a, a yes-and type of proposition? So, during the next 45 to 50 minutes we have together, we're going to go and take a look at what each one of these categories, and even some of the suppliers individually, have to offer, what are the boundary points, and how users should be thinking about it going forward.
Andrew Dailey: So, for those of you who don't know MGI Research, we are an independent research and advisory firm. We have a unique focus in a handful of areas. One of them in particular is contract management and how it connects to the rest of the enterprise, so not just contract management from a legal point of view but how contract data can be connected to, exposed, and consumed by the entire organization, from procurement to the sell side, to what's required in billing, support, and more. We're a team of, experienced, heavily experienced, industry veterans and we bring a highly structured and quantitative research methodology to our work. And you'll see some of the new tooling that we're bringing to the market is what we call a functional capabilities assessment, and we're going to be applying it to this space, so you'll get to be one of the first set of viewers to see what that really means in practical terms. And speaking of practical terms, we work with clients on a subscription basis and really serve as a trusted advisor bringing not only research data and benchmarks but, really, insights and experience to help clients navigate some of the toughest questions that they face as it relates to business and technology.
Andrew Dailey: So, the question facing, leaders today is first, really the need to have a point of view. It's not a question of, are these new tools good? Are they bad? Where do they fit or not? The real first thing that everyone, whether you're in legal, whether you're in procurement, whether you're in IT, is to really have a defensible point of view on where these AI-first tools fit. What's the proper use case for them? And what are the boundaries? And equally, where do the traditional contract lifecycle management tools as a system of record, where do they fit and how will this market play out over time? There's a lot of pressure on organizations to have an AI-first approach to seemingly everything and our take is organizations need to look at these, questions with a dose of reality, with a practical point of view, and to really look at it and say, what's the fit today, and how is this going to play out over the next three, four, five years? It's easy to get excited about new shiny objects, but making these kind of investments is something that you don't want to get wrong.
Andrew Dailey: The companies we're going to take a look at, just to kind of position this is we think of it as kind of the core CLM, or traditional CLM supplier, so some of the names that everyone will be familiar with are companies like Agiloft, Icertis, Sirion, Ironclad, Cobblestone. There's a long list of them, and in fact right now, we're in the middle of a deep research project, rating, the top CLM providers, and those… and in that research project, we're also looking at the quote-unquote AI native and what we call CLM Lite Suppliers, which is, kind of two groups that you could, put together. And those are companies like Harvey, Legora, Spellbook, even Anthropic as an LLM provider has been kind of dabbling around, and even more than dabbling, tempting customers to say that they might get into the contract management space. And then you have even a category of products like Astra, which is a solution, an AI solution being brought to market from Agiloft. So there's a lot of confusion in this space in terms of where these products fit and how the market is going to play out between this different group of suppliers.
Igor Stenmark: I think the two operative terms here are disruption. There's clearly disruption going on and there's clearly a lot of confusion on the part of the buyers, sometimes investors as well. There's a gold rush towards some of his newer AI-based products and would have shown exceptional growth in a very short time. So it's a very interesting time to be an observer.
Andrew Dailey: It's a great time to be an observer. For investors, it's a really tough time. If you look at it, Harvey and Legora are two companies with very strong growth. They've come literally out of nowhere since 2022, 2023, and now suddenly you've got businesses with 150, 200-plus million in revenue, and valuations that are, now, both of them are in discussions for funding rounds where the valuation talk is 10 to 15 billion, respectively, so if you're an investor in one of the core traditional CLM providers, you look at those new shiny objects with, with a fair bit of, jealousy.
Igor Stenmark: Right. So we'll look at what the gaps are, like, what really separates these types of products and specific companies as well. So, Andrew mentioned earlier that there's really… we see the market kind of fragmenting into three different categories, or segments, or flavors, if you will. And while this is a prevalent, motion in the market right now, our long-term view is that eventually, these products are going to completely converge So, I would say, if you look at, like classic native CLM, those are the full lifecycle platforms that support all stages of contract management, from signature to document management to intelligence and execution, whereas with AI-based CLM Lite products and legal point solutions like Harvey and Legora, they tend to focus much more on one or two layers, specifically on the intelligence layer. And they found, we think, a very interesting and fertile ground for application, specifically in legal departments, where lawyers find that adopting these tools is quick, it's productive, and even though the costs are reasonably high, most of the customers we spoke to who actually are using these tools give them pretty high marks. So, the challenge to the incumbents is real. Yes, there are gaps, and we'll illustrate the gaps, but the challenge from the incumbents is not trivial, and that's something that if you're a buyer looking in the market to buy it, it's certainly, certainly something to take into account.
Igor Stenmark: So… if you look at the free use cases here, free segments, the classic native CLM, that's generally a large buyer complexity in terms of product itself and implementation. If you look at AI-based CLM Lite products, those are generally departments, work teams, work groups, mid-market, within any size of enterprise, really, from small to very large. Typically, it's the legal buyer who pays for this and owns the relationship. And with AIPoint legal solutions, a lot of them are sold not to internal legal teams but more to law firms. And this was originally, I think, the dream that many CLM incumbents had of penetrating not only corporations but also penetrating law firms and that hasn't happened for them, but it is happening now for the likes of Harvey and Legora, where we've been able to rapidly penetrate the law firms and law practices, and the numbers are certainly speaking for themselves. And as we mentioned earlier, all these products have slightly different DNA.
Igor Stenmark: In some cases, more than slightly. So the native CLM products may go back 30, 35-plus years back, where we still are out there on prem-based legal automation solutions that are still in active use, believe it or not. And then the latest batch of those came out of cloud-based products that really focused on workflow, on automation, on kind of early stages of intelligence, and substantially on integration of contracts with operational systems. So that's that last layer in the CLM maturity model, where you basically say, alright, I have a contract, signature's been down, now what? What happens to that? Why do I have a contract? Well, I want to align my legal obligations and entitlements to my operating performance. So if something is broken on my production line, rather than calling the vendor to sell me a new one, I should really look in the contract and see if it's covered by warranty, and then execute a workbook that really supports that. Similarly, if I'm selling to a large financial services provider, and I'm a new salesperson, what is the current pricing for this large financial services company? Where do I find that? In my ERP system? No. In my CRM system? No. In my CLM systems? Yes. Exactly. That's exactly where it is, because that dictates the business rules, and in many cases, the pricing rules.
Igor Stenmark: So, all these kind of streams of activity, they are somewhat different, have different buyers, different pricing profiles. The growth profile for native CLM versus AI-based products. A lot of CLM products at the early stage had very similar growth. We were growing, 50-100% a year, in many cases even higher. And now the growth has kind of petered out a bit, and because of all the AI confusion, customers are often not sure how to really seriously commit or not going forward, and implementations have been tough. It's a classic implementation, cycle with a service provider, a vendor involved, could take 6, 8, 12 months. Sometimes it has to be redone. So it's complicated. And against that backdrop, you look at something like the AI legal point solutions, you can sign up and start using it right away. Great. But the capabilities are equal, not equivalent. The question becomes; will all this stuff ever converge? We certainly think so. There's going to be a lot of convergence. Will all the solutions converge to one model? No. It's not going to happen, but we think that there's going to be a lot of overlap, or it is a lot of overlap, and there's going to be more convergence between these different models. So, evaluating enterprise software in general is complex, and evaluating contract lifecycle and management is… is quite, quite complicated. So how we approached it, we basically built a system that assesses every product against a standard grade, we look for evidence, we grade the evidence, grade its veracity, quality, and so on, and so on, so on, and add up all the scores, and that generates a massive amount of information about every company. And in this case, we looked at about 12 vendors through its lens of 77 major capability categories assembled. It's not 5,000, it's quite a bit more, in terms of actual evidence records. And, and so we're here to share with you some of the early results of that.
Andrew Dailey: Igor, do you want to walk through the… the six-stage CLM maturity model? Sure.
Igor Stenmark: Sure, yeah.
Andrew Dailey: That's an important baseline, because you'll see these S1, S2, S3 boxes throughout this presentation.
Igor Stenmark: Right, right. So we have a standardized reference model, which we published a number of years ago, called with Six Stages of CLM Maturity and the intent of it is really to serve as a point of reference for both CLM buyers and CLM users to figure out what needs to be done in terms of assessing your own maturity. core capabilities that I needed to achieve maturity, and what do you have? So it starts with very basic things. You're converting from paper to electronic. You start with document management. Document management is not just PDF. It's things like data classification, data management, integration of security, compartmentalization in some cases, so it's more sophisticated than just take my paper document, scan it for an OCR reader, and it comes out. And so, when it goes to signature, signature, again, can be very simple, or can be very sophisticated for advanced authentication. And then it goes to workflow automation, workflow management, how contracts are approved, reviewed, who is involved, what are the different levels of approvals required, what are the different business rules, how it operates. It can be very simple.
Igor Stenmark: Or let's say if you're in a life sciences company, and you're building a new plant, and you need approval from FDA, and approval from various other regulators, the actual approval cycle is a book. It's a book about that thick. So, and then offering, legal teams and procurement teams, building contracts or reviewing contracts would come from third parties, redlining them, managing them, suggesting alternative clauses, doing mechanized redlining, risk scoring, and so on. Lots and lots of software players have capabilities in this area. And with intelligence, where you get into mass management of contract data that's in place. It's not unusual, we find in the research that we're doing right now, what Andrew mentioned, we find that there are companies that easily have 100,000, 200,000, in some cases, upwards to a million different contracts, in storage, in various CLM systems. And growth, it can be 10 to 20% a year. So it's very, very significant. So some… whoever sells storage to them must be very happy, because we can see lots and lots of it. And so intelligence is really the ability to go in and ask intelligent questions about what's going on. Like, which contracts have forced majeure? Which contracts have automated renewals, what we may have… we could miss in the next month or so, and maybe we want to cancel or renegotiate. And if you're a seller, if you have renewals coming in, what should kick off a sales effort. And then the last stage, stage 6, is one of the more advanced stages, is one that really deals with post-signature execution of, really, if you deter… in Stage 5, you determine what information is, what my intelligence is. Stage 6 is actually what takes care of, okay, here's what I'm going to do about it. Here's how I'm going to mechanize it on a sustained basis, as opposed to just, like, take some data, load it into a batch file, and do something with one, so that's not really a solution. So the definition of this six-stage model is available as a research note. I think it's downloadable by anyone with registration, even if you're not a subscriber.
Igor Stenmark: Okay, let's keep rolling here. So… so we ran this process, we collected a bunch of scores, some of it surprised us, some of it did not. So what, what emerged? So we really are three bands of scores, and we've taken pretty solid companies in each category. It's with significant, significant companies and we basically wanted to see if you put everybody through the same lens, what happens? Okay? And what we found is that with CLM, vendors are somewhat separated in terms of scoring, where scores are significantly higher, because we do a lot more capabilities. You see later, where capability set is much richer than either the AI lite or the AI Point Solutions, and at the same time, with AI lite, legal points, AI lite, CLM lite solutions, and with point solutions, we're kind of close. We think that eventually, this middle layer will get converged into one over, or both. So that layer will dis… it's going to get really squeezed. It's a man in the middle, so to speak. But even with the best companies that we looked at, there's clearly a separation in scoring, and we're going to show you where the overlaps are, where the gaps are, and where, for example, challengers are not only catching up to the incumbents, but doing much, much better. So if we look again at the space as a team sport here, so you have a CLM core team, you have native CLM, you have CLM lite, and you have legal point solutions. Core CLM has a lot more capability in almost every category of capability. Compliance and security, and even things where AI tools are strong, and negotiation. But obviously the AI-based solutions, we're way better at marketing their stuff, so that's something to think about, right? So there's clearly a gap between those three groups. There's less of a gap between the two AI flavors, we think they're going to converge, and there's still a big gap between incumbents and the challengers overall.
Andrew Dailey: I want to hit Igor on one thing quickly, which is…
Igor Stenmark: Go ahead.
Andrew Dailey: If you're using an LLM to assess the different tools, they're just scraping the public web, and there's a lot of disinformation that's out there and, as a consequence, there's an enormous risk of making the wrong decision based off of the data that comes out of that.
Igor Stenmark: So, where are the gaps? So as I mentioned earlier, so on one end, it's with tail ends of with CLM maturity model, it starts with signature and ends with execution. Those are areas that really, where CLM native incumbents, they… they tend to do better. And when you get into the intelligence, negotiation, and Agentic, the gaps are very, very tiny, very small, and they are closing, and incumbents are on track to really challenge the incumbents at this point, where we could see, like, real disruption in this space. And if we sort of zero down from looking at this as a team sport, looking at this more an individual sport, and look at the sample companies we put into a study, you can see where some of these companies are strong, and where we are light, where we have real capability, and where we have a white space that needs to be closed in. If you start with a kind of a classic native CLM solution, and you start waterfalling it down to where does a legal point solution like a Harvey or Legora land, the gaps are really between, it starts with offering and, like, with e-document, signature, and then it slowly kind of goes down to an average score that's quite a bit less. So, we mentioned we wanted to look at where… what are the areas where we're challengers are much stronger than, say, with incumbents and, and who, in that case, of, of that pack, who is the best?
Igor Stenmark: So, "Eevo" or Ivo, I'm not sure how we… if we correctly pronounce the name, but, so this company, we've seen it in the field, we just spoke to a couple of users that have both CLM products, and we have Ivo for a legal team and they have, pretty strong capabilities with things like redlining and automated contract review and playbook enforcement. These are topics that we heard about from CLM incumbents for years. And yet, every year, year and a half, you hear yet a new discussion about, okay, so now we're doing it, but didn't you do it before? It sounds very familiar. Oh, this is better. But essentially, it's the same thing. And I guess using… when new tools are in place and approaching a problem fresh gives the challengers a significant leg up. And when you look at some of its other areas where we see dead heat, so you have more Harvey, more Legora, more Luminance as well. These are also areas that CLM incumbents have been working on for years, and so this really has to force the incumbents to think pretty, in an honest way with themselves, like, what do we do for non-core here? Like, how do we sort of deal with that? Because if we do nothing, the challengers are going to eat their lunch and doing something means either accelerating development, which means changing the culture of a company, buying a company, which means spending money and diluting the shareholder base. So there's non-trivial questions that probably penetrate many boardrooms and investor discussions at this point.
Igor Stenmark: So we kind of positioned throughout this conversation, kind of reflecting what's… the tonality of a conversation in the market, of its incumbents versus the challengers. Reality is, as we see it today, that challengers cannot do the same things that incumbents can, and vice versa, and we do it for different groups of users and in many cases, different use cases. So, challengers really have a tremendous leg up in intelligence, negotiation, maybe in redlining, but again, more in law firms, and in some corporate, legal, organizations, Office of General Counsel. There's native CLM. They kind of run the contracts, they arm the system of record. You can't not do that. Everybody wants to have some sort of a robust repository capability of search, organization, with workflow, with security, with compliance, and customers kind of don't see that the point solutions really have it right now. Maybe we will, down the line. Maybe we'll buy somebody, maybe we'll develop it themselves, but we don't really have it right now. So… so it's really not either or, and this makes it very difficult, we think, for the buyers to figure this out, do I… by CLM, and wait for them to step up and close the gaps illustrated here. Or do I ignore it and just go with, maybe a CLM lite until things stabilize? Or do I not even worry about it now? I just accept it with some degree of chaos in record keeping, but I'll, focus on improving productivity of my legal department, or my law… if you're a law firm, that's clearly a prerogative to do that but, so the choices are not… not trivial. Andy, did you want to jump in on that?
Andrew Dailey: It's absolutely not an either-or. In fact, what we're finding in the field research and even today, there's an article in the Financial Times about Legora going out for funding, and it mentions one of their major customers: Deloitte. As it turns out, we were just doing a briefing with a company, Pramata, in which Deloitte is not only a customer using Pramata, but also a reseller of their solution. And that's just one anecdotal data point, but there's a lot more. What we're seeing is that, when you really dig in, you find that organizations will have a core CLM, which spans multiple functions of the organization, and is really that robust, not only repository, but with the capabilities that everything Igor's been walking through. And then there are these point solutions that exist, say, within legal, as really a productivity tool, or an enhancement to users within a single function in the organization. And this could persist for some time in the market.
Igor Stenmark: Right.
Ethan Weiss: And it's important to note that the use of CLM tools as a way of accessing contract data and pulling insights. Core CLM solutions face a lot of user adoption kind of slowdowns in just the weight of those implementations, and how companies choose to set things up, whether it's done correctly the first time, or if it's over-configured. And we're finding that the companies that use both a native CLM tool and one of these AI legal or AI-based CLM lite solutions, it's kind of a way to balance the user adoption question, because they can still pull insights from either solution, but getting their employees to adopt these technologies and bring it into their day-to-day workflows. That's the question that core CLM tools are still trying to navigate.
Igor Stenmark: It's definitely a question of adoption. How quickly can you adopt it? If you have a problem today, and your first use is a year away, maybe that's not workable anymore because in a year, the whole world can change. And so you need something now, even if it's not going to be very durable as a solution. So, we hate to give the answer, try both, we'll use both. But in some cases, you will find that that's actually workable, because you're going to have different user groups, and as long as there's budget to support each group separately, independently, that may not be the stupidest thing in the world to do. But generally, I think understanding where you have the most pain and most disruption in your business, and how to kind of optimize for that is the order of the day.
Andrew Dailey: And what the… what the biggest payback is going to be. Now, there's one thing, Igor, we haven't, and Ethan, we haven't talked about, which is also one of the most pressing issues in this market today, and it's something that we see come up in every CLM or CLM Lite, or point-solution conversations, which is why can't I just vibe code it? Why are we looking at these three things?
Igor Stenmark: Yeah.
Andrew Dailey: Oh, here we are.
Igor Stenmark: We could spend three days here talking about vibe coding, and where it really applies, and where it probably should not be applied, just like AI itself, as well. Situations where you have a backlog of requests that's been sitting around for three years, and you never got around to it, and every CIO, every head of sales ops, every head of legal ops, every head of procurement operations has that list, and it's an ugly list, and vibe coding could take off some percentage of that, certainly. Questions how you do it, so you can still pass audit. If you can still kind of keep your sanity. Is it a fit? I mean, we've seen now actual conversations, companies saying, oh, we think we're going to wipe code the whole thing ourselves. We don't need Legora, we don't need Harvey, we don't need… like, what is Legora and Harvey? They just took a corpus of legal knowledge and loaded it up into a vector database and set up a system. We have developers who could totally do that. We don't need you guys. The question is not can you do it? The question is can you maintain it and sustain it over a long period of time? And what happens when your core developers leave? Because it is a development project. It is not something that, an average lawyer is going to be comfortable doing. They have already a full-time job and don't have any time anyway, especially for House Counsel. So, the bottom line is, can you sustain maintaining it over a long period of time, even if a long period is two years?
Andrew Dailey: The hard irony of this is where vibe coding is going to have the most lasting impact in this space is within product managers, within CLM or AI, vendors. So, product managers who were prototyping future releases of their software, it's not necessarily going to be on the user side, it's going to be on vendors for prototyping.
Igor Stenmark: So our recommendation has, over the years, we started MGI in 2008, and we've always told with few exceptions, we always told our clients, don't do this at home until AI arrives. And with AI, the answer is more… a bit more nuanced, because there certainly are classes of problems and types of use cases where Wipe coding is completely the right solution. But for core systems of record, like CLM, maybe not. Maybe not. Even if you're very large and very experienced, you probably have another business. So, okay, let's keep rolling here. How does this pan out? So, convergence, we think that's very high probability. We already are seeing it, so it's kind of a… almost a foregone conclusion. So you have… you see in this list, you have Agiloft releasing Astra, which originally was called Screens, was a company they acquired, and now they've kind of elevated some of the capability and repackaged it for a PLG motion and repackaged it as a tool, but kind of fits squarely into the CLM lite category, something that could be used by a contract administrator. I mean, even in a small company could… they could use it, because we all have contracts, and what's in the contract determines what you do.
Igor Stenmark: So, that's clearly happening, and we're going to see more of it. The bigger question is, are kind of the guys who are leaders right now perceived to be leaders in AI point solutions? Are we going to also eyeball the I would call it on-prem market, but the in-house legal market, and which we're already penetrating easily, are we going to also try to sort of build out more capability for, like, a CLM system of record? And I think it's only a matter of time. It's going to happen. How it's going to get done, I think in different vendor cases, it's going to be different. In some cases, we will buy a company at low cost, and get to people, and try to integrate. Probably not with oldest companies, but more newer companies. Maybe we'll buy some of the CLM lite companies, and kind of give them some budget to step it up while we're all private, in some cases, we'll merge with another large company. So, there's multiple ways of how it's going to be accomplished and that's really the case where we say core AI model providers double down on legal are open AIs of the world, and Anthropic… well, Anthropic is already in it. Anthropic stepping into a legal automation market caused a big brouhaha in the stock markets back in January, February, but we're still, like removing remnants from the floor of that explosion was, in many ways, completely misunderstood, and people didn't really pay attention to anything but the headlines, and it was kind of stupid, but it is what it is. So, those are lightweight solutions for someone, let's say, like me or Andrew, or Ethan saying, okay, we got to review a contract, we don't have time to call our legal counsel, he's on vacation, it's August, let's do that. There's definitely a use case for that. Are we going to ever be the provider of enterprise choice? No. It's like asking Google to be your ERP provider. Good luck with that. It's not going to happen. There's no culture, there's no support.
Andrew Dailey: They will definitely put pressure on the entry-level products in the market, and…
Igor Stenmark: Yep.
Andrew Dailey: Now, keep in mind, there's a lot of solutions, legal-like solutions in the market. Take something like LegalZoom where small businesses look to them to go for different contract types. Certainly Claude and others, like Claude, the LLM providers, are going to put a lot of price pressure on those types of solutions.
Igor Stenmark: It will take, for them, investment to provide more tools to organize things, even where normal tools, what all of us use, we could use a lot of improvement. And the minute they become public, which is soon, forget it, we're not going to do that. They're going to abandon it, put it in a ditch, and declare it all to be partnership, you know. We don't have a staying power to do this. I think that's not going to happen, honestly. But pressure on low end of a market? Absolutely. 100%.
Andrew Dailey: It's going to put pressure on a lot of lawyers. I mean, I was talking with a partner of a large firm on the West Coast last week, and she was explaining that the first thing she does is go to the public LLMs. And I said, "well, why do you do that? You don't trust anything that's in there." And she said, "no, I don't trust what's in there, but I absolutely need to know what my clients are going to be asking me because that's where they're going first."
Igor Stenmark: Right. Good. There's a whole discussion about using consultants, advisors.
Andrew Dailey: Yeah, conversation.
Igor Stenmark: We're going to have a conversation in September, in October, I think, right? Yeah.
Ethan Weiss: September. September.
Igor Stenmark: September, yeah. Okay. So, Point Solution buys CLM plumbing, possibly. There's plenty of good properties on the market, and it might happen. Private equity continuing with a roll-up of various, incumbent products in legal. It's happening, and it's going to accelerate. Money is available, exits are murky, so there's a lot of uncertainty, clearly, but it's not… it's… we shouldn't say that it's not going to continue. More fragmentation. Some more companies are beginning to focus on specific use cases, specific verticals, or even becoming industry vertical specific. It's happening already. We have already seen…
Andrew Dailey: Already happening.
Igor Stenmark: We've seen, you guys know, we've seen companies that do, for example, legal and contract management just for… not even any financial services company, but let's say just for hedge funds, or just for private equity, which have specialized legal structures, and you have dual structure, onshore, offshore structures that have to be managed in multiple jurisdictions, complex contract organizational structures, so it's happening. Life sciences, it's happening.
Andrew Dailey: Yeah, there's no reason why the contract research organizations, as part of their offering for, say, FDA approval process management, won't include a legal play there to manage all the contracts related to everyone involved in FDA trials.
Igor Stenmark: So, one thing we feel pretty certain about, that things staying as they are, less than 10% probability, very low. This is, again, a market characterized by confusion, disruption, and a lot of structural motion. So, stay tuned. If you are in the middle of actually doing an eval right now, trying to figure out your strategy, maybe you have had no solution before, or maybe you have a solution, but you have outgrown its capabilities. Maybe it was a good choice in the beginning, but no longer the case. You started doing this when you were a $100 million organization, now you're crossing $700 million, and you need a different kind of animal to help you get this done. What should you be aware of? What should you be asking? When you are building a short list--
Andrew Dailey: To jump in, I would say the first thing is everyone needs to have a point of view and an answer for when the board or the executive team says, what about X? Right?
Igor Stenmark: Yep.
Andrew Dailey: Why aren't we vibe coding this? Why are you telling me we need to spend a million dollars solving this problem, right? Whether you believe in vibe coding or not, you believe our point of view or not, doesn't matter. It's… there needs to be a credible answer to key questions, that's first and foremost.
Igor Stenmark: Yeah, absolutely. I think looking out beyond what's immediate on the table is important. Even though you could argue that, look, the world is going to change in 6 months, it's going to change definitely in a year, we may be in a different, completely different set of circumstances for a variety of reasons, but thinking… business is business, and you have to kind of have an understanding of where your business and your industry is going to be 24, 36 months, with a proviso that rate of change has just picked up a lot. But the core is going to be the core, so you need to kind of calibrate your decision-making and strategy to that, not just what you have in place today, because that's going to… it's going to become irrelevant very, very quickly.
Andrew Dailey: Yeah, and just extending that, it's also, what's the penalty for doing nothing?
Igor Stenmark: Yep. So, what about white coding? What about AI? What about doing nothing? If I just ignore this problem, will it go away? What's my use case? And how does it really match? Where vendors we looked at, and where do we find vendors that really are experienced in our specific use case. I'm a billion-dollar hospital chain, and I have free hospitals, and I have a research institution, and I need to manage lots and lots of supply chain contracts, and deal with drug trials, and all kinds of other stuff. So, what… who is experienced in doing that? So we don't have to explain from Adam, how do you do that to a vendor? I mean, is it Legora or Harvey? Maybe, maybe not because a lot of your requirements are going to be operational. Like, what happens after a contract is signed? Like, just getting to a contract more efficiently is great. It certainly removes friction in relationships and makes your lawyers more productive. Terrific. But what happens afterwards? That's where a big payback is. That's where a big ROI really, is… is… is… can be realized. It's not just making contracts, contracts be signed from down from three months down to two months or a month, right? You need to have a vision of what happens afterwards. And so these are some of the questions that you should be asking. And what's your roadmap going forward? Okay. So, bottom line, there are, right now, three different product flavors, or market segments. Ultimately, we think that these things converge, even though there are gaps between them right now, those will, really narrow down significantly from both sides. Everybody's racing against the clock to get that done. Where gaps which do exist today are often not necessarily as much in AI capability because LLMs are available to everyone, it's not a secret, and it's open source, essentially open technology.
Igor Stenmark: The gaps are in integration, architecture, compliance, being able to implement something in a complex enterprise with challengers and the incumbents have slightly different focus, the challengers tend to read contracts simplistically, and platforms with CLM incumbents, they tend to run the contracts. They can do the reading as well. But we also can run them, so we kind of can support both pre- and post-signature pretty effectively. And so… whatever you end up doing, have you a list of as Andrew was describing, kind of critical questions, you know. What do we do about this? How do we deal with doing nothing? Who are the likely suppliers? What about AI models? Should we have our integrator be involved in this and really lead the process? Should we hire a big consulting firm to get this done, or an analyst firm to help us decipher it? Do we have the right expertise? And what kind of ROI and outcomes can we expect down the line? If it's just about a little bit of productivity improvement, probably not going to be a big selling proposition. It has to be something that has that, plus some outcomes that will have generally nonlinear results for your organization. So, that's our story, mostly, on this, and we're sticking to it.
Andrew Dailey: We've kind of referred to this in passing, but our notion of, and our solution that we're calling the Functional Capability Assessment. So what we've done, and this is not just limited to CLM, we have this in the other areas that we cover as well, but what we've really done is taken all of the data that we've collected from doing so many different user evaluations and our own evaluations of products. And so, taken those, put it into, 77 distinct capabilities that fit within our six-stage CLM maturity model, and then started to assess the suppliers at a detailed level across those capabilities, across the six stages, and then across three architectural layers, and begin to score them, and say, where are they… what can they demonstrate? What can they prove that they have in each one of those areas?
Igor Stenmark: So, and in case of CLM, it goes beyond that. It also looks at the various use cases for CLM and tries to score the products based on specific use cases as well. So your vendor score, say, in risk management use case may be very different from supporting, say, a buy-side exercise, or a sell-side sales operations, or M&A. So, and when we're also, as Andrew mentioned, we are rolling this out in other markets, in billing, in mediation, metering, rating. Those are complex areas to evaluate, always require specialized expertise and what this tool gives you is really the ability to jumpstart the whole process and kind of instantly become an expert by having access to a tool like that.
Andrew Dailey: And there's one more thing, which is another product that we're rolling out into the market is what we call MGI Signal Customer Intelligence. So, we've been doing deep customer interviews, and when I say deep, they're 45-minute, hour-long, almost like depositions. They're confidential, they're anonymous, and they go into what is the real customer? What is the real user experience in using a given solution? What are the products… what was the problem they were trying to address? What are the solutions that they evaluated? What's the solution that they're now using and what has that customer experience been like? What works? What doesn't? What would they like to prioritize on the roadmap? Do they feel like they're getting good value? If not, why not? So, signal, customer intelligence and CLM is really the ability, whether you're a buyer, whether you're a vendor or an investor, to be able to go in and interrogate that data using a chat interface. So it's really the ability to pull customer intelligence forward and use it immediately and in a very tangible way, whether you're evaluating suppliers, looking to improve your product roadmap, gain more competitiveness in the market, or just trying to understand the differences of different suppliers in the market.
Igor Stenmark: It's basically a specialized AI model for, CLM customer intelligence.
Andrew Dailey: It's off of proprietary data that's unavailable anywhere else.
Igor Stenmark: Right, so it's not something you can do on… on a general purpose LLM, because we don't have the data. So, that's the difference. Okay.
Andrew Dailey: Igor hinted at it; the next session we'll be doing is September 24th. "Will AI Make Analysts Irrelevant?" This is going to be a lively and engaging conversation. The answers, not entirely clear.
Igor Stenmark: Tune in and find out.
Andrew Dailey: And then October 15th, we'll actually be announcing and publishing the CLM Buyer's Guide and Market Overview. So if you're in the process of making a CLM decision, we are deep in the throes of that research and analysis. Reach out to us. We've got the freshest take on what's going on in the market, and we'll expose a lot of the results and data, the rankings, on October 15th.
Igor Stenmark: And if you want more information about either, FCA, Functional Capability Assessment, for CLM or other areas, or about signal, whether you're a subscriber or not, give us a ping, and we will get you more information. We have quite a bit more data available on both of those things.
Andrew Dailey: Thanks for your time.
Igor Stenmark: Thanks, everyone.
Andrew Dailey: Thanks, everyone. Bye for now.