Hello and welcome to “AI Trends and Future of Intelligent Filmmaking.”. I'm Andrew Walsh, VP of sales and your host for today's discussion. The AI revolution is upon us. No industry goes untouched by the positive disruption and innovation of artificial intelligence, not least our very own M&A industry. How will AI impact us as deal makers? How will AI change the way we do deals? These are the important burning questions on all our minds. We don't have a crystal ball, but we have the next best thing, an esteemed panel of experts that will tackle these questions and more.

And I will be putting these questions to them and you, the audience have the chance to ask questions directly to via the Q&A function. So, without further ado, I'll let the panelists introduce themselves and we'll start with NAV. Thank you, Andrew and appreciate the kickstart so NAV Rashford of managing partner and active growth partners to over 20 years in M&A dealmaking and investments landscape. My career has two focuses. I've been on the portfolio principal side for two large strategic publicly listed businesses, Logic CMG and FIS in the past. And also have spent significant amount of time on the deal advisory space, working with the large investment banks, private equity portfolio companies on doing the deal making industry vice focused on TMT, enterprise software, vertical SAS businesses, workflow automation tools and more recently in the AI space and all the facets of artificial intelligence and investing in those spaces.

Brilliant. Thank you. Now wealth of experience there. We know a little bit about TMT ourselves. Clark, would you like to introduce yourself? Great. Thank you, Andrew, and thank you everyone. My name is Clark O'Neill. I'm a partner in MDP with BCGI, lead our AI topic globally for a principal investor in private equity practice. I'm also a career dork, started my career as a technologist building supercomputers and chips. So having a lot of fun these days thinking about kind of the implications of of the next generation of semiconductors and AI.

Brilliant, fantastic supercomputers, Michael. Thanks for having me. I have done deals from a bunch of different directions. I was an M and a banker and lawyer in the 90s, spent about 20 years as a corporate development officer at large B2B tech companies, and now I'm the founder and managing partner of Trajectory Capital, which is a private equity fund focused on small but well established B2B staff businesses. Fantastic. What a panel of experts we have today. So, let's get into it. So the first question is how is AI currently transforming, transforming the traditional dealmaking process?

What trends are revealing themselves? Can you share some examples where AI has provided a competitive edge? So I'm going to put that first two now. Thank you, Andrew. Once again. I guess let me start by saying pretty much all of the facets of deal making are getting impacted and benefiting from the use of artificial intelligence and all of its facets like whether it's computer vision, whether it's deep learning, it's everything that's helping out the deal makers in accelerating their understanding about the assets, understanding about the markets and how the markets are shifting.

Markets themselves are shifting very quickly because of this reason as well. So, I would just say I think pretty much the entire life cycle of deal making is getting impacted better. Now. You look at the enhancements in the deal sourcing space, people are getting their understanding about the landscapes much better. They understand the competition better. They understand how the dynamics are playing out. So, they have their tentacles in the market getting all of that light feed and those pulses to make their decisions around who should they go after, what is the investment thesis that they can bake into their investment portfolio.

And then it goes all the way through to actually doing the deal work effectively. Doing due diligence work is getting immense sort of enhancements through the use of our visual intelligence. And we'll go into the details of how people are utilizing different AI tools to enhance and speed up the due diligence work. That's one of the biggest advantages I see people have been able to gain from use of the artificial intelligence, but also then it goes into the understanding about the asset itself and then value creation work as well. So I'll stop here, let others chime in and and we will.

Then we do a deep dive on these areas as well. Yeah, absolutely. We're seeing huge productivity hacks within the division process, not least from insurance themselves. Michael, would you like to expand on that? Yeah. So I, I, you know and I'll, I'll give you the Luddite caveat, which is whenever a new technology is introduced somewhere, I always worry about the rush to implement before finding ROI. And it's especially important in M&A because M&A is very high stakes, very rapid and generally run by very small teams.

And so on the one hand, tech, any new technology can have a really big impact because the stakes are so high and the teams are so small. And on the other hand, if it becomes a distraction, it can have a very negative impact. So I, I think some of the things that not pointed out are, are exactly where I see immediate sources of ROI. Increasing the speed and efficiency of diligence is a really natural run, one where, you know, AI lends itself to absorbing large amounts of information and highlighting the pieces of information that are highly relevant so that you can have the human brains focus on the, you know, the 1% of the content that's actually important to assessing a deal, you know, gathering.

I think market intelligence is another way that they're that AI is going to accelerate processes. I think in all these cases, I don't see AI replacing humans entirely, but rather sort of leaving the humans to do the 10 or 20% most critical thinking and doing a lot of automated data processing for them and then sourcing. I think especially in the smaller end of the market. And I'd argue that the larger end of the market, if I'm trying to acquire an auto company, I probably know who all the auto companies are. But when you go to the smaller end of the market, you know, despite a lot of data sources being out there, the markets very opaque.

It's actually hard to find relevant companies, I'll say, and then I'll stop. This is a good example of where there's a flip side, I think to AI and the danger in AI, which is there are Infinity potential targets in the market. And just as you don't want to miss lots of the obvious ones, you also have to be careful that you don't inundate yourself with a large number of irrelevant ones. You know, there are a lot of good human tools for identifying relevant targets, right? Talking to people in your network, asking people who are industry experts.

And so I think if you just try to replace that with raw AI, you'll end up with a 10,000 company pipeline that you can't possibly, you know, analyze as AI improves, the quality of that sourcing function will improve. But I think we're going to be on a journey. I don't think you can magically turn off the human aspect of a lot of these parts of the field process. And then the one that you touched on and maybe as a subsequent conversation is there's a whole other set of applications of AI on the portfolio operations side. So we're talking about AI as it affects the M and A-Team, but I think there's a whole other question of how AI effects what deals you actually do, how you think about those deals, how you think about investing in those deals and improving those deals.

For [an] analyst to associate AI, it's not going to replace that world. It's actually going to make that world more interesting because that they get to focus on the high value tasks. So really interesting points there. So we'll, we'll finish this question with Clark. No, I mean, I think Michael and I have really hit it and I think as we look at AI, what we often see is this is a productivity tool right now and it's not something that's creating alpha for most of the investment teams. Like we're at a stage now where a lot of times this is a little bit of like becoming table stakes of like everybody runs fast, everybody's looking for an edge.

But I, so I think [of a] conversation I had once with someone who says this is not an alpha generating tool, it's a tool for alpha generators. And, and that's kind of how I think about it today. And, and I think most of the clients that we are talking with are like they're at the stage of deploying ChatGPT. And so one of the things that we see with a lot of folks is the commercial off-the-shelf tools are, are quite good in a lot of instances. And the ability to go from, but the capabilities you can get with a horizontal off-the-shelf tool to something that's really differentiated from that is that takes a tremendous amount of investment.

And so for most investors, just like focusing on, hey, this is what I can get from like a ChatGPT or a Claude or a Llama out-of-the-box. Those solutions are great places to start for many. And, and we often talk about the technology is the biggest challenge here. But for most organizations, actually the adoption of the technology is a bigger challenge and the people are the bigger, bigger part of the process. And so the number of conversations that we've had with investors, like most people are saying, I can get 30 percent of my team to log in and actually use these tools.

It temp is typically negatively correlated with seniority. And so like the people that have been successful in their careers and have always like been able to do it like the old fashioned way, the, the, the burning platform to, to change the way that they've done things is always hard. And so I think what we're seeing right now is like any technology, we're finding where it's useful, We're finding where it's not useful. And there's, there's places where the, the tools can be immensely powerful. Like you can have these tools are being used by Nobel Prize-winning mathematicians to, to, to help solve new proofs, but at the same time can't play tic tac toe.

So, there's, there's a concept of what we call a jagged frontier of competence. And so understanding of where the tools are very, very good. So, things like sourcing that. Then Michael talked about things like document search and, and summarization. Those things are fantastic use cases, but you also have to be careful about like it's not ready today to write the run the deal model. Like those are high-stakes environments that require high accuracy. And so you've got to really understand what it's good for and what it's not good for.

I love that jagged frontier of confidence, so I'll keep that one. So really good insights there. Let's move on to the next question. How is AI enhancing due diligence, particularly in evaluating risk or financial health potential targets now, please? Yeah. So as I was saying before, I guess this is 1 area where and it sort of aligns with everything that's been said before by Clark and Michael as well. So, this is a productivity enhancement opportunity for a lot of due diligence teams where they have large amounts of data presented to them.

And it's really, really hard to sift through that data in the limited amount of time that usually gets given to the investment teams to analyze the the investment opportunities. As a result of that, what happens is you end up missing out or passing on a number of opportunities, which because you don't have capacity or your investment team is at capacity, they're looking at other things. And this another thing [that] cannot be really done a due diligence on. So, the productivity enhancement really enables you to create that capacity to analyze large amounts of data to effectively connecting some sort of querying system to your dead rooms and querying all of that data that's been presented to you in the form of contracts, customer contracts, customer code analysis that could be automatically done to that.

A lot of that information could be done and given to you in a way where it can be queried by some of the senior folks. So Clark mentioned, I think as you go up to seniority chain, the use of these tools or the use of the data becomes harder because of the time constraints that these people have. So for that reason, I think if you can enable them with the tools that they can query themselves and also do some scenario analysis as well. So this is where I think a lot of the productivity is going to come into play. And it's coming into play where some people who, and I think what I've seen is more strategics are going in that direction where they have some internal technology teams where they have large investment fronts and they're looking at using artificial intelligence models for their core businesses.

But they're also leveraging that to create their own models for the purpose of the due diligence on the deal space as well. So I guess the private equity space or the other investors would have to specifically invest in this area to build those tools and we're seeing that happening in this space. Yeah. Thank you. Now, Michael, would you actually expand on that? Yes, I, I think and this is the the somewhat boring answer, but I think as with as with all new technologies, the best way to get great returns is to focus on low-hanging fruit first.

This is this is not an area where I think it's great to be on the bleeding edge. There are other sectors that are going to be on the bleeding edge in terms of developing AI intelligence, but I think in M and a number one, there's a lot of low hanging fruits. So some of the stuff now I've talked about, right, you can, you can easily take with some of these tools, 20 hours of document review and turn it into 45 minutes of document review. You you can easily do initial scans of target companies and establish sort of baseline information about them that helps you sort of take them in or out of of contention, things like that.

And I think that's going to be the first wave is just real easy low hanging fruit exercises that free up your very small team to do the high quality work. This is sort of your point is it's not that you get rid of your analyst, but your analyst is 8020 rule doing the 20% of the work that's the most valuable and uses their brain. I think eventually people will invest in the intelligence of AI, put enough data through it that it can actually start to automate away some of that human judgement. But frankly M&A is a relatively in the grand scheme of the market, a relatively low volume endeavor.

And so I suspect it will take, I think there are other sectors that will automate AI and invest in automating AI. Maybe, you know, consumer customer service would be a good example where, you know, you have literally millions of employees and you can create massive efficiencies. I think that the M and a process will come later in in that journey. And because the stakes are so high, while there's the the point that I think Clark was making about, you know, the the older and more established somebody is, the more resistant they are to new technologies. The the sort of counter to that is it's such a high risk endeavor.

I'm going to remove human judgement only where I'm absolutely sure I'm not put putting something at risk. Because the stakes are if I'm if I'm doing a $500 million deal, saving 20 hours or even 100 hours of Labor at the risk of making a mistake on that transaction isn't worth it. And so I think the bar is going to be very high in a lot of ways, the same way it is with automating legal professional services and some of the other professional services where you want to create efficiencies. But at the end of the day, you're paying for 30 years of deep expertise and experience, and you're going to be very hesitant to take that out of the equation entirely.

Michael, can I have one thing that I'd love to just build on, which is one of my favorite use cases for judgy PT is to play like red team on what am I not thinking about? Like just giving it the situation of the deal and asking like, what are the things that I'm thinking about it this way? Here's the things where I think the opportunity and playing that red team on my logic is actually a great way to like like really pressure test the thinking because it's unconstrained by like kind of the biases that we have as humans.

And so for folks that aren't using catch EPT and the other tools like that, it's one of the things that I think it can really help elevate the conversation because to your point, it's not changing, It's not getting rid of the role of the deal maker. It's helping the deal maker think about something from a different perspective. And it, it gives it a fresh set of eyes that we always worry about. Am I missing something? And like, what am I missing? And it's a great tool for that. I think that's a fantastic example where you're enhancing the human brain rather than taking it out of the equation.

I do think one of the other subtle challenges that underlies all of this is AI is only as good as data. And so the interesting question will be, how do you get all the data into one place so that the AI can do whatever it is you want it to do? If it's diligence, it's probably the easiest use case where you go, well, here are 100, almost identical customer contract. They're all in one folder. Please scan them. But I think that the stuff you're talking about where you go, well, I'd like you to challenge my assumptions around growth in this oil and gas business become highly dependent on what data the AI has access to.

Yeah. And another big factor that we haven't discussed is security. We're talking about ChatGPT and these other open AI platforms. Security and M&A is paramount. And say what we're hearing from customers is that customers are looking to companies like Interlinks who are building these AI platforms in-house to keep that security wrapped around it and not in the public domain. I'd like to switch gears a little bit and I would like to talk about acquisition targets. So, my next question, will AI play a role in identifying acquisition targets that might not have been on the radar using traditional methods?

And if so, how? I'll put that question to clock if I may. I mean, Andrew, that's a use case we're doing all the time now. Like it's, it's really a combination of both traditional generative AI as well as all data that's existed in these industries for a while because I think Michael talked about it and it's really good for looking at the long tail of things. I think one of the things that we struggle with right now and the M&A is often a very reactive process of let's see what the bankers have on sale, which is useful and necessary and and perfect for the large organizations like the auto company that Michael talked about.

But oftentimes for tuck ends or for, for growth or venture opportunities, that's insufficient. And so you have to kind of figure out how do you, how do you get an edge in those scenarios. And so I think one of the areas we're seeing a lot of folks invest is like, particularly in the venture space, in the growth space, people using a lot of alt data and using generative AI to help pre-process and kind of manage the filter in the top of funnel. So it's both identifying more things to go into the top of funnel, but also figuring out where do you see signals within that funnel to figure out and, and where to focus?

Because I think like time is really the, the, the thing that everyone's really optimizing for and in these processes. And, and so I think this is a, it's a perfect use case for it because we like we've talked about it. You don't have to be perfect. It's not like the deal model where like, like you've got to get the credit terms, right. Like that is like that is not that's a non-negotiable. Missing one opportunity or missing another opportunity is, is bad, but it's like those are recoverable errors. And so, I think finding something, the needle in the haystack is a, is a great use case that, that we're, we're using a lot with our clients an- it's an opportunity that I think a lot of people like everyone.

You should talk about 80/20, but now, now AI can read everything. And that that's a, that that's a becoming table stakes as people go into these processes. Yeah, absolutely. And Michael, I'd like to put the same question to you. Yes, I agree. I think that it's a great use case for AI with a couple of caveats. The first one is AI is never going to get the targeting exactly right because there are a lot of subjective judgement kinds of things, you know, even.

Just. How? Confident. Am I in the management team or what? What's driving different dynamics in the market sector or in this product that are going to require human judgement, I think for the foreseeable future. But so I think that that AI can help with it. It's almost like Clark's earlier point, which is I'm going to generally know enough about the sector to identify some targets. AI will help me find similar companies and similar businesses.

And the more I feed it in terms of what the characteristics are, the better it will be. That said, I think #1 you're still going to need that human filter, because otherwise some really dumb stuff is just going to make it through. And secondly, I think you have to manage the volume that you take in from AI. Because as Clark said, the goal is not 100%. You don't have to find 100% of the potential targets. You have to find enough that you know you're getting to the good ones and then choose the ones that you want. The flip side to that is if you're inundated by targets, then the exercise is useless, right?

I'm not suggesting AI is, is like this, but if you just pulled up a company listing off of one of the, you know, public data sets, you know, I can create a target list of 100,000 companies. That's it. That, that's actually worse than nothing, right? That, that, that's just going to be a giant drain. And so I think there'll be a, a management of flow issue with AI where you adjust your criteria and a bunch of other things so that AI brings in some additional targets, but doesn't overwhelm you to the point where you're nonfunctional. I, I and one of the one of the quick point I make is where I think AI is incredibly useful for me is a quick hit summary of a business that allows me to do that human evaluation.

So if, if I, if I identify a target, AI allows me to find the basic information in one place that tells me if I should dismiss it as a target or, or keep it as a target. And, and that's in, in my mind, that's a, an efficiency play on, on humans that can be very powerful, you know, saying things like, I only want companies that are above a certain headcount, above a certain level of revenue, you know, operate, operate primarily in a certain market. There are a bunch of ways that AI can be a fast screen. Yeah, I'm saying thank you. Thank you, Michael.

So let's talk about valuations and the deal negotiation process. So question, will AI-driven data analytics impact valuations and the deal negotiation process in the coming years? And if so, how I'm going to put that to now. Yeah. So I think this is a complex topic for a lot of the deal makers, a lot of the investment bankers and you know, investment teams as well. So how can they leverage the new tools that that they have available to themselves? I think one of the basic things that I would like to highlight here is obviously there's a fundamental difference on how the traditional finance models have been implemented.

So coming up with an approximation, a linear approximation of the relationship that could a lot of the times be very complex and not linear by definition. And that's how the finance has worked, right? So, but it's a fundamental departure from that. If we were to say we want to use a, you know, a multi-layered neural network to do the valuation work for us now, it might be very efficient. And it precisely is used for capturing those kinds of hidden patterns within those nonlinear relationships that you would find oftentimes and the variables that can determine the valuation of the businesses. However, if you try to go in that direction, explainability becomes a problem, right?

Because, you know, the benefit of traditional finance models is that you can, you can explain them very easily like this has this relationship, this variable of this relationship. And, and for that reason, this is what the outcome of that change in the in a variable would be. Whereas in the in a neural network or in a deep learning model that you would implement, it becomes a bit of a back black box. And I think that's where the tuition has comes from as far as implementing, you know, more advanced artificial intelligence-based valuation techniques are concerned. However, the some of these models are being used to feed into and augment your traditional finance valuation model, right.

So if you would need to have better understanding of how the cash flows are going to be operating over the last five, over the next 10, five years, I think the artificial intelligence will give you a lot more data to analyze the historical data that's available for the industries. And that's where it would help. And I think that's where the dealmakers are at the moment. The investment teams are at the moment before they can get to a point, you know, they're getting comfortable and explain, explain ability and interpretability of the model can be sold for before they go there.

The other thing that I've seen as far as evaluations are concerned is comparable. So, I think that's a huge task for investment teams to come up with the comparable targets, come up with comparable transactions, and oftentimes it takes a lot of the new ones to look through the transactions because every single transaction is different. Every single target would have different nuances. For that reason, the valuation might differ from what you're trying to compare it with. For that reason, I think the artificial intelligence models can actually sift through the data about these transactions and data about these targets and help you understand better comparables that you can extract from from from large amounts of data for your relevant transaction.

Clark, what are your thoughts? The only thing I would add to that is what we're seeing a lot with many of the processes they're running right now is if AI is a risk to the business or an opportunity that has a pretty big impact on valuation. So we're almost seeing it more than the tool to be used to identify the risk. It is the, what is the impact of AI on a business has real implications for whether it can trade. Because I mean, at the end of the day, when we think about kind of like managing IRR, those things and coming up with discount rates, discount rates are are tied to risk.

And the fundamental reality is AI is creating a new type of risk in many, in many markets. And I think people are trying to figure out how to implicitly value that risk. And so the beta has gone up for many categories objectively. And so how do you manage that, particularly as you think about a private equity deal that has to think about a 10 year time horizon? And if you think about like 10 years ago, neural Nets were we're trying to come up with on like 70% of the time I can identify whether this picture is a cat.

And now we're having debates about like, OK, is this, we just blew past the Turing test and like people are debating whether we're not going to get to AGI next year. So it's like on, if we're looking at the scope of super intelligence on the on the deal time horizon, the, the risk is something that I think people are really right now need to think about. And I'm not sure that it's fully priced in everywhere. And so I think that's an area where we're seeing like particularly on sell sides where we're working with clients, like there's some businesses where the risk is more acute and in near term and, and people are having to deal with that.

And I think there's also going to be, I think the peak, the place where people are going to really have to start thinking about is what happens as you get 5 years out from here because like the, the potential possibilities get a much, much broader. I think there's a lot of cases to be optimistic, but there's going to there's going to be some industries that get run up. Yeah. So very good segue to my next question. So I want to flip the coin a little bit. We've talked a lot about the positives of AI process efficiencies, streamlining targeted lists, valuations, etcetera.

Let's talk about the dark side of AI within M&A. What are the risks, What are the dangers of using an AI in the M&A process? Michael, what are your thoughts? So I, and maybe I'm just a pessimist, but I see a whole bunch of them. I think #1 there's just inaccuracy, right? AI is not perfect. And and there's actually a dangerous temptation to read it as being perfect, right? Because it's technology, You know, I don't question my calculator when it gives me an answer. But AI and ChatGPT, you know, chat functions are different.

So I think there's number one the danger that people take the information for granted, don't apply human judgment and make catastrophic mistakes in terms of everything from, you know, market dynamics, you know, the strength of a product, the valuation, all those kinds of things. I think secondly, there's a, there's a potential for tremendous amount of noise. You know, if I just asked AI for B2B SAS targets, I can spend the rest of my life doing an initial valuation of what'll, what'll spit out. And I think the last one is, and I see this a lot in corporates and I think deal makers can fall into the same trap, which is someone says, well, we must be using AI.

And so everyone rushes to use AI to say they used AI, right. And in the case of investors, maybe that means telling all, they're all PS oh, well, we're an AI enabled investor. Or if it's corporate development team, well, of course we're using AI because the company has an AI enablement strategy in places where AI is not useful and it actually slows down the team. You know, now the team is desperately trying to figure out how to enable AI to do something that especially because we're talking about, you know, relatively small and lean teams, the juice may not be worth the squeeze in terms of, you know, spending six months trying to AI enable a function that was being done by an analyst perfectly fine beforehand, right.

So I think those are all the kinds of places where there's danger. I do think going back to the first one, inaccurate data and inaccurate data being ingested into diligence and being ingested into, you know, evaluation of of deals is incredibly dangerous. You know, if you take a tool that digests a whole bunch of legal contracts, on the one hand, that's fantastic. I just saved 20 hours of analyst time or of lawyer time, which is much more expensive. The flip side is if the AI makes a mistake and we don't gut check that with humans, especially on high-risk items, it can be catastrophic right there.

There are no changes in control provisions in any of the customer contract. Turns out to be, oh, there are change in control provisions. They just didn't use the word change in control. They used another word. So I think there are like any technology that there's a, there's a danger to improperly using it or too flippantly using it. And so I, I always start with, with any technology I'm using, what is the concrete ROI I'm getting from this use case? And what are the risks that I have to manage against as opposed to let's just assume we use AI everywhere and hope that it works out?

Yeah, absolutely. So let's talk about AI on the valuation of businesses themselves. I mean, seeing these AI companies popping up everywhere and they're the big, particularly in the TMT space, the big technology companies are trying to acquire these or figure out what's our AI story, what's our AI play. And I've seen first hand as a role as my CEO, as a CRO multipliers when there's an AI portion in there. So what impact does AI have on portfolio value creation now? Yeah.

Again, as I think I'm just going to start by saying that a lot of the times when the deals are coming to the market and sellers are prospecting the buyers, potential buyers, they're looking at how ready their potential buyer is from an AI transformation perspective because that's the next journey. That's the next phase of the growth for most of the businesses transacting at that at this point in time. They want to get on to that band, Megan. They want to get on to that wave of productivity enhancement in their businesses and fundamentally compete with some of the other players who have come into their industries and might just completely change, fundamentally change their business models and they have to work on that.

So how ready the the buyer is to sort of equip them with that tool set to make that transformation happen is very important. And that is also then going to determine the valuation as well. Because I think the valuation is a function of what a value of the business in somebody's portfolio is over the next five years or whatever the holy. Would be. And some of that value would be given to the seller, right, Just by definition. So people would want to have a negotiation with the people who are ready.

So the stable stakes people, investors whether they are sponsors or strategic have to get ready for that AI transformation of their portfolio businesses. And when once it's in their portfolio, they have obviously they have to sort of gain those productivity enhancements. At the moment obviously they want to go for those low hanging fruits, make sure I see that is implemented correctly without necessarily heavy capital investment into the businesses. But eventually we're going to get to a point where heavy capital investment would be seen as part of the transactions and deal making structures where that investment is going to take place within 12 to 18 months of transaction happening.

Talk. As we look at like kind of the future of the next 10 years of private equity, I firmly believe that the funds that are going to fundamentally differentiate and create the most value are the ones that are going to figure out how to deploy AI and drive ROI. I mean, we saw it with kind of the financial engineering in the 80s. We saw it with kind of the rises portfolio group kind of over through the 90s and the 2000s. I think the next wave of this stuff is really going to be about driving impact with AI And in like, I think what a lot of people think about it is like, oh, it's just a tool.

Like this is actually change management. It's understanding how do I retool a business process? And that is not like that's block and tackle hard work, roll up your sleeves. Like, this is not a simple thing. And I think what we're seeing is like the, the market's getting more competitive for deals on the deal side. And I think the people that are going to be able to like differentiate in this are going to understand in the diligence process. I see this organization and these functions are ones that are right for AI transformation. I'm going to bid it up more than the implicit valuation of the current business with an understanding that I know that I can take cost out of the back end, Dr.

efficiencies out of the back end of this business and create value on the valuation side. Like I think that is the people that are going to like have the top quartile funds they're going to have, they're going to understand how to build a suite of solutions to be able to go after that stuff and get confidence in the dealmaking process that there's value to be created there. And so as I look at this type of stuff, when we talk to most of our clients, Michael, Michael talked about a little bit earlier, these are lean teams, like driving productivity in the deal making process does not move the needle on just run any company.

Like it's not, I don't want, I don't care about the dollars that I'm spending on the deal team. The value that gets created is like multiplied because of the multiples that we pay here and the multiples of value creation that get that, that that happened during anime process. And so that's the real lever. And so the people that understand where the real leverage in this business are, are the ones that are going to be able to understand what use cases are really value added, have the playbooks to generate those things and figure out how to lean into the deal processes where they see those opportunities and can deploy them more quickly.

Very interesting insights and Michael, if you want to take us home with that question. Yeah. So I know I absolutely agree. And I think that it's going to have to be real and not fake. In other words, you know, and if you look at the evolution of portfolio operations, I think this is part of the evolution is people sort of talked about portfolio operations even before they really had what I think of as effective portfolio operations. Same thing is going to be specifically true with AI, which is, you know, stage 1 is maybe I find one senior executive, it's got AI all over the resume and I show them to people.

But what Clark is talking about is a much more complex capability to understand AI, understand the implementation of AI, understand the, the change management, the product road map development that has to now embed with AI data requirements, all that kind of stuff. So that kind of expertise in in private equity investors is #1 going to allow them to deliver more performance. As Clark pointed out, I also think it'll make a difference in winning deals because unless you're buying 100 percent of a company, the shareholders are going to look at you and say how are you adding value? And then I think the last thing is LPs are going to start to pay attention just like they did to portfolio operations in general.

And they're going to want to put their money behind teams that private equity teams that not only have general portfolio operations, but have a real track record of being able to implement the AI. And then I think the last piece that I'd throw in there is AI has this other effect because while it creates a risk, as Clark pointed out, for some businesses, it's also going to create opportunity. You know, a simplistic example is there are a lot of data businesses that are going to be worth a lot more because they fuel AI. So, there's now a big use case for their data. And so, but that goes back to portfolio operations.

How do we take advantage of that and having people on the investing team who can, who can help. So I think it's going to become a must have the same way other capabilities have been a must have for investors all along. And, and frankly, I like the evolution of investors from being financial engineers to having a real operational impact in one way or another on their portfolio companies. That may be a bias because it's what I enjoy doing. Michael, can I just build on that? Just I want one point I want to make is in the many conversations that are happening with GPs, many of them are starting to say I need a strategy that I can go communicate to my LPs about why I can deploy AI better because it is going to be essential for me and my fundraising.

The next fund that I raise, I need to have that story and I need to have proof points in My Portfolio of where we've done this well. And so as like as, as people and investors are thinking about this, it is going to be imperative that you can actually put some points on the board and say, Hey, I've actually done this and showing a track record because you're going to compete with other investors that are saying I've got a. Like I've got a process here, give me more money because I'm actually better at this and I'm going to get a return, a better ROI for you. Yeah, good expansion there.

Thank you. So let's move on in the discussion, what are the common misconceptions about the role of AI and deal making? Let's talk about some of the misconceptions. There's a lot of noise in there in the market. We talked to them. I will put that to clock for me. The one I always start with is like everyone thinks that AI is a technology problem. I actually think it's a people problem. And so like we, we see this over and over again. We have an adage of BCG that we talked about, we call it 102070.

Whenever you're doing an AI project, 10 percent of the work is going to be the algorithm, 20 percent is going to be the technology, 70 percent of the work is people who change management. And, and I think we're seeing this over and over again, organizations, whether it's a deal team or it's a portfolio company. The hard part is actually getting it adopted and changing the business and like taking cost out. Because I think what happens is if you create productivity, but you don't have a use case for that, it shows up in someone's golf game or or some something along those lines.

It doesn't necessarily flow to the bottom line. So, like as you, as you think about like technology for technology sake, we've seen that movie many times and it has never been a happy ending. And so this is, this is something that like the, the, the best operators out there that are the ones that are going to really drive value and be like, like meticulous about like figuring out how to, how to change an organization. That's what it takes with AI. And, and, and it's not something that it's just like a technologist problem. And you can't start from the technology as the, as the objective.

It has to be something that's wrapped in a business process. And you need to understand if it's wrapped in a business process, technology is a small component of this. And so you need to look for large opportunities that are going to be transformative to a business. And so whether that's touching a lot of revenue or that's touching a large cost base or a lot of people that are doing repetitive things, you need to look for the right patterns to be able to deploy this to get the ROI out of it. I've seen that movie many times indeed. Hopefully, it's not James Cameron's Terminator.

Michael, would you like to expand the map feel? Better like Sam Altman's a gun nut and has a bunker so. Yeah, in the machine. But I actually think it's an important point, not that I think the machines are going to rise up, but I think most people think of technology as an on switch and an off switch when it's really much more of a long evolution. And so it didn't it what happened is not, we did not have AI and then we had AI, but rather, you know, there are inflection points along the technology curve.

And we've, we've hit probably over the last couple years and inflection point, but AII think there's a simplifying assumption that AI is right, which is very dangerous when in fact AI is going to be, you know, writer over time. I think there's also another assumption that AI makes any process more efficient. And as an example, when I think about sourcing, I think AI will have a relatively minimal impact on sourcing of mega deals because I think humans have already captured that amount of what AI is really good for is in my mind. Two things: number one: massive amounts of data that a human can't absorb easily, but the AI can simplify for the human and number two: what Clark alluded to earlier, which is to sort of bounce things off the AI and, and make sure you're not missing something.

So if I think about sourcing at the large scale at the at the mega deal level, I don't think AI is going to have much of an impact on the micro deal level. I think it will because there you have a data set so large that humans can't sort of absorb it. And I think the point Clark made, which is applicable to both the deal process and the portfolio is critically important. This is not a technology problem. There are people who are who are on the cutting edge of AI for whom it's a technology problem. But we don't play at that cutting edge, right, with, you know, even even the most sophisticated deal makers are using AI that is well-established technology.

The challenge is going to be change management implementation, the integration with human functions, all that kind of stuff. It's it's never going to be and the differentiator is not going to be AI. You know, there's never going to be 1 private equity fund that is using a slightly more advanced AI technology. And that's why they win. They're going to win because they more effectively use even a stale AI technology. Absolutely. You touched on the human element there, and I want to expand on that. Let's move to the audience's Q&A section.

We've got a lot of really, really good and interesting questions here. I'm going to pick one at random. This is from Wesley Polios. Do you face resistance in the business from either junior or C-Suite colleagues? And if so, how are you convincing them to change? This is an interesting human element. So we talked all about technology. Let's talk about the resistance within. Who would like to take that? I can get us going. I mean, look, I think this is one where the resistance to change is real.

And you see this across every organization. And most of the time when you see people pushing back, it's a very personal reason. And we see this many times. We've done a lot of work with R&D organizations and like an example that we often see in an R&D organization is a very senior, well-respected engineer will put their hand up and say, I think R&D coding tools are garbage. Despite the fact that if you look at all the empirical evidence, like there's like the average productivity is about 25% gains, but they will go and put their hand up and say that.

And so if you work in that organization and, and you care about the, the, the perspective of that individual, like it's really hard for you to adopt that tool. And so we see those types of engagements around AI. And so I think like being able to like address kind of the human elements of adoption are really important. And I think we, we often as we do the research around what organizations are being most successful, there's also a correlation with a very senior like executive that's really into it. And there's a techie. And it's because they've created the, the ability to to try and experiment and it's OK.

And it's socially acceptable that it created that, that space to to to experiment. If you're a leader in an organization, recognize that your people are looking at you and the usage of AI is going to be correlated with your usage of AI. And so give your team the right to experiment on this type of stuff in a Safeway. I mean, Michael highlighted real considerations. And so it is not a panacea, but it is also there's a, there's a, there's a risk of if you don't participate now, they're like, you may fall behind. There's a red team element and red queen element of this that I think is really important to think about.

And so I think we see the resistance to change is like an age-old human adage, which is going to be make it go slower than I think everyone wants. But it is also for valid reasons of like how humans interact. And I think we're going to end up in a world that it feels more like we are managers and executives of a is versus where the doer like, which is where we are right now, we're the doers in the deal process. We're going to be managing AI that are that are executing the deal process.

But we're still like providing them feedback saying, hey, that analysis is wrong. That doesn't look right. You're missing something like we do that with our deal teams today. Like, I think we're just going to do it with more and more AI under the covers in the future. Yeah. Would anyone else like to expand on that? The one thing I'll I agree with everything Clark said, the one thing I'll throw in, which I think is I personally find fascinating, is how much human psychology effects business operations.

And everything Clark just said about the resistance to change, I could have just replaced it with integration planning or other changes in an organization, right? Whenever you're, whenever you're, you're doing a big acquisition and you're talking to your existing team, but what's going to have to change once we buy this business, you get that same kind of resistance. We don't want to adopt different processes. We don't want to adopt different technologies or methodologies or whatever it is. And I think that the lesson is exactly what Clark said, which is you have to, it has to come from leadership.

Anytime you're trying to change and push out of inertia, it has to come strongly from leadership. And the other thing I'd say is helping people understand how they personally are positively impacted. You, you know, you shouldn't have to. It's an efficient company. It's all just about maximizing profit. The reality is if you want to avoid resistance, making it clear where you can without lying that this is and this is a point Clark made really early on about analysts. This is going to make your job more interesting. You're going to spend more time architecting the code and less time banging away on a keyboard.

You're going to spend less time doing boring, you know, database research and more time evaluating different target companies. Whatever it is is going to limit resistance a lot culturally. And we'd like to think we're efficient, but the reality is giving people that comfort and as well as the push of leadership is requiring this. And it's, it's part of the new world. Is is important to avoid resistance. And by the way, one of the point of resistance that we haven't talked about is in target companies. So, you know, Clark talked about how effective investments are going to be driven by how the fund demonstrates that they can implement AI effectively in portfolio companies.

You've also got to get the portfolio company on board with that vision. You know, you can have all the great portfolio operations teams in the world. If the portfolio company doesn't want to make that change, they either won't do the deal with you or they will and then they'll drag their feet. Very interesting points there. So as we wind our way to the end of this discussion, I want to touch on another question from the audience. And we're going to have to put our futurists hat on a little bit and get our crystal balls out.

But the question is, so is there any human value to deal analysis, diligence and sourcing in five years, 10 years? Does the industry or perhaps us become a commodity as AI tools become better and better? Interesting question there. Who would like to tackle that one? I can go open up for the rest of the panel as well. I guess the point of this whole access to the advanced technology is that you are in the future going to become this augmented workforce. Everybody else, everybody's going to get impacted with this and people who are not adopting are going to be left behind.

So I think it's stable stake. As we've said before, everybody would have to get equipped and trained up on these technologies so that this becomes a new normal for everybody to use these technologies to augment their intelligence and effectively then make the judgment call where the human judgment is absolutely necessary and is required. So, I guess we're not going to get made redundant as far as our human intellect and and judgment is concerned, is the point I'm trying to raise. And there's a huge relationship element to M&A or any dealmaking, right? I don't think AI is ever going to take that major factor away.

Would anyone else like to expand on that? I think what would always is, as you reflect on the history, what you often see is there's a lot of unknown unknowns. And so people can get to a perspective of, Hey, this is going to create a like it, it's going to change what I've done, Therefore it's going to be bad. Is is kind of an implicit like perspective that a lot of people have. But if you look at most of the technologies is there are winners and losers. And so I don't want to like talk about this things like the, the horse trainers, like got beaten out by the, the mechanics, but like the, in general, whenever you go through technology changes tends to be expansive.

And, and like in it, I think it's going to create more opportunities that is going to destroy. And so I think humans are very resilient. And, and I think the folks that are doing M&A are the types of folks that are, that are, they're really creative individuals. And so I'm, I'm very like, there's always a lawyer involved. And so like I somehow I don't think I'm ever going to get around, but there's always going to be a lawyer involved in any situation. So like, I'm very optimistic that humans are going to make themselves useful in a process.

And so like in general, as we, as we look at this type of stuff, like I do think 5 and 10 years, it will change a lot. But I think the people that are going to, to, to come out of that and be the most successful in those things are the people that are going to think about the process differently in leveraging technology and in different way. But I think there's like there's a lot of things that, that require judgment that are implicit knowledge and context that are not written down that you can train into a model. And and so like just the, the pattern recognition that the deal professionals have of having seen this stuff for 20 years, like you can get 80 or 90% of it, right, but it's that last 5% of where the edge is often.

And, and that's why we like, we always go to the people that have expertise and we ask the questions of what's your basis of perspective? Those are important for reason. And I don't think that changes in the context of AI because just like we asked the basis of perspective of our experts today, we're going to have to ask the same thing of our AI is of like, OK, like, is this in your training set? Do you have the right perspective to make a judgment here? Can I trust that judgment? And so kind of some of the old like Ray Dalio, like, are you believable on this topic?

It's very believable in certain topics because it has read every academic paper and medical literature. Like it can be very, very believable in that it hasn't read, it hasn't necessarily had access to all the information and internal data of a company that we're diligent. And so like being able to, to use the kind of the judgment things that we've seen in the past, I think is going to be something that is going to take a long time to build into a diligence process. And I'm, I always look at like the most successful investors of all time, like it like Renaissance and some of the hedge funds, they only looked at price.

That was the only thing that they looked at. They didn't look at the macro of a business. And then they made just decisions based off of that. And so like AI is not able to capture the complexity of what we do. And in the same way that humans can today. And, and maybe we'll get to the point that it can in five or ten years. I still think there's a lot of complexity that humans will implicitly have for the foreseeable future. Yeah, I, I think, I think the evidence we have is what's happened in the hedge fund industry, right? That this technology sort of hit technology and data hit that industry sooner for a variety of reasons, including the fact that they're publicly traded assets.

There's a lot more data out there, public data out there about them. And you know, I don't, I think hedge fund headcount has been rocketing upwards, not downwards. So they, they still need humans. The humans are just doing different things. And to the point Clark made way early in this conversation, I think generally more interesting things. So, I think you, you, you probably see a period of fear and adjustment followed by a higher level of job satisfaction and intellectual challenge. Yeah, this is, this is very encouraging to hear. We're we're not over yet.

So, this is a fascinating topic. We could, we could talk for hours on this. And there's a host of audience questions and I apologize I can't get to them all, but we are up on time. So that just leads me to thank first panelists, Clark, Michael and NAV fascinating insights there from you and of course the audience. So thank you very much for joining us in this really interesting and deep discussion. Join us next time at Interlinks for next webinar, which will be discussing AI and the impact on the M&A industry.

And with that, I bid you good day. Thank you very much for joining us. Thanks again to the panelists and have a great day everybody. See you soon. Thank you.