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What Private Equity Buyers Look for in an AI-Ready Business (#91) · Exit Algorithms

• Pete Vera, Exit Algorithms: Welcome to Exit Algorithms, the podcast where we decode what it really takes to unlock growth, streamline operations, and prepare your business for a high-value exit. I'm your host, Pete Vera, and today I'm joined by Diana Weinberge. She spent nearly a decade in PWC's deals practice across the UK and Germany, rising to director. She also speaks five languages. giving her a truly international lens on cross-border deals. Today, she's a partner at DevelopEx, where she leads transaction advisory and data-driven value creation for private equity. Diana, great to have you here. Welcome to the podcast. Diana Vainberga: Thanks, Pete. Great to be here. Pete Vera, Exit Algorithms: Yeah, I'm excited for this conversation. Do you mind starting off? Can you share a bit about your your background and yeah, maybe your business journey, how you ended up at DevelopEx? Diana Vainberga: I'm a partner at DevelopX since about a year now, and before that I spent over ten years helping founders, investors, corporates navigate the MA journey and basically from from preparing the business for the exit to the actual processes and then the value creation side on the buy side of things as well. I spent majority of my business life in London, so helping there the larger private equities, buying businesses, helping them with the growth journey. and then with the exits and more recently, which is already not that recent because it's already five years, I've been in Germany, mostly focusing on the mid-market private equity. And the the last years I primarily supported business owners, founders in their sell basically exit process, sell process, and preparing them for for the exit journey. Pete Vera, Exit Algorithms: what is what is your main role there? Diana Vainberga: So we help basically investors and founders throughout the whole deal cycle. So from basically buying the business, we help them there with the MA buy and build strategy, for instance, to then the diligence phase, mostly focusing around commercial DD, technical DD, and AI DD. And then after the purchase we help them with the growth journey. So starting with the full potential plan. more often than not we are doing AI potential plan nowadays and then scoping the initiatives that would mostly contribute to to growth. And it's a kind of ABDA upside and then going into the execution. so that combination of kind of entrepreneurship, execution, and network is I think where if we combine it we add the mo most value to to the founders that are trying to position their business for a sale process. Pete Vera, Exit Algorithms: Yeah. that's awesome. Hu huge value add for for businesses. yeah, what what type of businesses do you look for or what are some of the things you like to see in a, you know, ideal target that you would invest with? Diana Vainberga: So we usually work for for investors or private equities that are investing in in those targets. So when they're looking at targets, what we see nowadays it's more and more so I do a lot of software or software related businesses. So whether it's technology, to software as as techno technology business to like a SaaS or If it's a med tech business or a defense tech business, for instance, we it's it's usually a couple of things, right? So number one, it's of course, you look at the people, right? So in the end, you're buying the business and you you what you would like to get to know the CEOs and understand what's their background, where they're coming from, and Whether you think that the story that would they will tell you will hold to it to what they're telling you, right? So I think it's in the end, in I for me, MA business is the people business, so that's what investors look at, I think, first, right? But going beyond that, assuming that's that's a check, the second thing is nowadays, of course, the degree of the business model being disrupted by AI. so I think it's a no no secret AI is everywhere. But what is more and more important is not purely well will the business able to to kind of scale but more is it able to sustain and not being Pete Vera, Exit Algorithms: Mm. Diana Vainberga: obsolete in a couple of years time right so more and more we're doing as part of commercial DD or a technical DD or kind of a combination of both really that AI angle to understand on the one hand side what is the upside of EI, where can it create value, right? So that's the kind of growth side of the story because depending on that you know you can kind of quadruple your your revenue and then there's also the downside of it so the downside is basically the disruption point that I mentioned Is this business model sustainable or is there a risk? and if there is a risk, where is the risk? and I think what buyers or or our clients that we work with mostly kind of find, I think, is beyond those kind of two buckets where I said that one is the upside, the other one is the downside. I think they're looking beyond just labels, right? so I think there's a huge point around labeling thing as things as AI and actually having substance to it. So I would say beyond this initial downside upside potential, we then go and try and help them understand okay, is it purely kind of a label or is it like what I mean by label is for instance if someone is using like a chatbot or a chat GPT Pete Vera, Exit Algorithms: Right. Diana Vainberga: or or Claude, that doesn't mean that that this business is Kind of set for the future, right? It just means that Pete Vera, Exit Algorithms: Yeah. Diana Vainberga: it's it's covering the basics. Or is it really like is there substance beyond that AI label? So that would mean are those architectures set up for AI agents, for instance, right? So is Pete Vera, Exit Algorithms: Mm. Diana Vainberga: AI T landscape set up in a way where you can have AI agents on top of it? And it will kind of give you interesting insights, or not even interesting, like helpful and correct insights. Pete Vera, Exit Algorithms: Mm. Diana Vainberga: and the second bit of that same problem around labeling versus actually having it. Even if you're if there is substance behind AI, sometimes it's around the data, right? So the the actual point we look if we look at AI is Is the data landscape actually and the quality of the data in a good form? Because no matter what AI you will use, it will produce incorrect results if the data is not sound and if it's not basically if it has not been maintained to a good enough standard. So I think that's another thing and maybe Two more points and then I stop talking. So one is the team capabilities. So the team capabilities in a way that can those teams actually execute on some of the top line or initiatives that have been, for instance, identified as potential value levers, yes or no? do they have the skills, have they been trained? Are there like some some maybe data scientists on board of that company that can help the sort of yeah that can help on that AI journey to the whole team. Pete Vera, Exit Algorithms: Yeah. Diana Vainberga: So the the the kind of team setup. And then the third thing is probably Which I kind of mentioned but maybe not not clear enough, is the technical depth, which is a little bit different from like just good data. It's more around how is the whole setup of IT infrastructure been to date, right? have there been some fast tracks which basically need to be completely replaced before you start with adding agents and AI on top of it. I guess a lot of points to that EI point, right? So I think Yeah, so the first point, the the people, the EI, and the third point is of course the the market potential, right? And how well the kind of equity story of the founders, for instance, fits the market potential, right? are they capturing all the risks, are they capturing all the opportunities in a way that is kind of sound if you look at their if you look at their business plan, right? kind of are are there any risks beyond pure AI, right? We have a lot of geopolitical things going on that may limit some cross border topics. of course and on a local level same right some of the external factors need to be considered. So I think this is also a a consideration. Of course it has been always been a consideration but I think now as the the world Became more and more kind of interconnected. There have been some various political topics that I don't need to elaborate further. But I think all of that is quite recent, and therefore I think a full understanding of how could this impact the equity story, I think is very important because yeah, if not, then what we also see sometimes there is a crazy focus on the AI topic and then. And sometimes people forget the other topics while they still exist and they may still also be as important as the the kind of new technol new technology that is arising. Pete Vera, Exit Algorithms: Yeah. Yeah. What do you see most often kills a deal, you know, during d due diligence and the transactions you work on? Are are there some commonalities, common things too that you see the deal go go south? Diana Vainberga: the exit timelines have been postponed I would say that, right? like because of the macro environment that wasn't quite certain. a lot of kind of businesses kind of weren't sure if it's the right time to enter the market. So and then the buyers again weren't sure how to price those things, right? So what we've seen most is like processes being very, very either extended or yeah just not not yet Enter it, which ca kind of of course results in a certain scenario where the investors have funds but can't deploy them quick enough, which again is not great for the cycle. and in terms of killing, you know, like EI has been definitely something where there have been a number of I would say kill scenarios in the in the past in the past year or so where the technology made some businesses obsolete basically. and yeah we we kind of someone wouldn't have thought about that twelve months before, but then you know, there was hesitation, then process extended, and then eventually the EI that killed the the USP kind of entered. And I think that's probably the most yeah frequent scenario Pete Vera, Exit Algorithms: do you do you have any advice for companies who want to maximize their value when they decide to to partner with a a P E group? You know, how how does a business best prepare for for a sale? Diana Vainberga: how does a business best prepare for a sale? I think I would say start early. so that I think that's one of the key topics that we keep seeing just going wrong. that the founders start preparing like a few months before you know it's it should go into the process. the the the reason for that is that in order to get the best kind of equity story, or not the best, the more most succinct and transparent equity story, you need to get your data straight. And where we see a lot of topics are happening is that the data is not that goes into the data room is not good. So it doesn't either it lacks granularity, so it doesn't give the full view of the business, like some of the customer product economics are not there just because the data on that lower level is not good enough to to put into the data room, right? And it of course discounts your value of the company if you can't communicate what are the drivers in a kind of data driven fashion. So I think a lot of the things we do Around that data-driven value creation, or the reverse the reverse scenario where you prepare companies for exit is actually try, and if you can, start very, very early with data preparation, meaning understand what story you want to tell first, do a storyline for that, and then see where how you can underpin the story by data, and then we go and And try and understand what are the different sources of the data, what are the KPIs that are needed to tell the story, and then in most cases we kind of create the data cube out of multiple sources that would form the basis for then basically creating analysis and to tell the narrative. So that's yeah basically early start early and get your data right, right? And sometimes if you haven't been tracking some KPIs, you may as well ha still have time to do some of that work, right? Because if some KPIs are important, for instance, we see that sometimes in the online or the digital business landscape, right? Where you can easily track some of your e-commerce KPIs. and th those KPIs that are kind of that where you need some processes basically and you can still start tracking them right a couple of years before the exit. You can't do that right before you want to do the transaction right and so yeah, I think early then do the data right and then also think about how you want to position yourselves for the future. What I also saw a lot of times now, a lot of or many many founders have interesting ideas to some of the use cases they want to push, where the growth comes from. but sometimes it's too many, right? It's it's also about kind of making the equity story concrete and trying to bring it to kind of a focus in in a way because a at least true for some of the software cases right where There's so much out there currently, so much out of like so much can be created with purely some some tools that you know you don't need actually engineering capabilities for for some of the topics. So there it helps to be very specific about what are the use skills you want to put as your key kind of equity story anchors in a way. Because then and then making sure that those anchors are not gonna be disrupted by AI, etc. It's Pete Vera, Exit Algorithms: Mm. Diana Vainberga: sometimes makes the story easier to digest, even though you might want to do it in other use cases, but maybe you know select those that are kind of critical to your business plan, critical to your equity story, and then it's it basically becomes a more succinct succinct story in in a way. So yes, so I would say focus is the third kind of point Pete Vera, Exit Algorithms: Mm. Diana Vainberga: that I would say would would be needed. Pete Vera, Exit Algorithms: Yeah. really useful. Thank you. yeah, and we touched on it quite a bit already with AI. you know, you mentioned AI due diligence, you know, AI labeling versus actually using it. I'm I'm curious, you know, how are you how are you you know leveraging AI today most and how do you see it affecting your industry? Yeah, well said. I I love that. Lot a lot of good takeaways there. yeah, n you mentioned it this a few times, but I'd love to if you could elaborate a little more on data driven value creation. What does that look like practically speaking in a in a business that you work with? Diana Vainberga: sure. I mean data-driven value creation. So we we have two sides where we help businesses, right? So the one is exit readiness, where we already discussed briefly, right? And the value creation is kind of the reverse side of it. So the the value creation is when an investor buys the business. what are the key steps he or she needs to do in order to grow the business, right? So where we start usually we try to come in as as soon as possible as the business is being bought by a private equity investor, for instance. And the first step where we help is actually set set the transparency for the performance, right? so what we do is usually kind of a reporting work stream where we work out with the management and the the new investor what are the key kpis that actually tra need tracking because sometimes they have kpis. It's not a lack of reporting, it's maybe it's a lack of the right reporting that we usually find. So we help them shape the actual tracking mechanism in the form of trying to understand what success looks like and by what can you measure it. and so when we have that, and usually this is where it goes wrong, in well not wrong, but where we see a lot of topics that y th there where there is some reporting, but the reporting is actually not fit for purpose, right? So the KPIs that are have been selected are not fit for purpose. So we redefine them and then the next step is actually then trying to see, okay, now we redefined those KPIs. Do we actually have the data for it? And that's the second show sober where we usually see, okay, the KPIs have been defined, but then We are missing data to to actually execute on those KPIs, right? So we either need to bring certain data sources together, because in order to calculate that KPI, the data sits in ten different systems. so what we would then do is kind of a data exercise to create a one source of truth, where we then bring those those sources in a kind of in a cloud, in an Azure layer or something like that together to be able to calculate those KPIs. If the data so then there the next two problems are the data is not not good enough or the data doesn't exist, right? so then for the data quality exercise we then do the exercise where we try and establish some governance around the data going forward, but then also do the cleaning exercise for the data that's historic in order to have some traction, That is more or less correct, right? Sometimes it works out, sometimes not. so the data cleaning access one thing, and then if the data haven't been captured, like for instance, at all, then we implement processes of capturing that data, and of course then it takes some months to actually get some reporting. but that is I would say the the second step. so yeah, th that reporting topic is I think the absolutely first step and I I just mentioned a couple of pain points around it and why it's important because that forms the whole basis for then future value creation right and that's why we call it data driven is that we do not produce a strategy without actually understanding the performance and where the drivers of the business sit currently and where they're coming from right because if you create Strategy for the next few years without having understood well, which clients, which products, which channels have been causing your performance or underperformance of the business, then whatever strategy you put together might be like a kind of a theoretical exercise. So that's why, and I think that's the data-driven part of it, is that we anchor it in kind of really capturing the as this at the granular level right so granular I mean usually I mean if it's a retail business you can go to SQU kind of right as an in individual Pete Vera, Exit Algorithms: Okay. Diana Vainberga: product or barcode if it's a software business it's usually a customer product view right so then you can look at trends which is subscription versus n versus not etc and get those Pete Vera, Exit Algorithms: Okay. Diana Vainberga: ERR trends right So and I think that's that's the data-driven starting point. And after that, we see where it's coming from, right? Where the performance, like what are the drivers of the performance. And based on that, we can then define kind of a plan for the key things that will matter for an eventual EBDA uplift or revenue uplift or growth, however you want to define it, right? and the next step is then forming on the basis of the status quo specific initiatives that would lead to a kind of a top line growth, for instance. So we we Pete Vera, Exit Algorithms: Mm. Diana Vainberga: focus quite a bit on on top line topics. So it's more around sales, mark marketing, customer journey, pricing, etc. right? So and if you see for instance in the taking kind of the status quo that your pricing was all over, you know, some customers you were pricing X, some Y, etcetera. and there's no specific reason for it, then you might do something about the pricing strategy, right? If you actually haven't captured the full cycle of the of the customer if it it's a customer journey topic, right? Sometimes we see okay, we actually only capture data for one part of the customer journey and then the rest of the customer journey is kind of not captured. So it's it's a missed revenue opportunity. And in order to be able to to capture that revenue opportunity you need to capture the the full customer life cycle, right? So Pete Vera, Exit Algorithms: Mm. Diana Vainberga: so those things it's then a little Bit more of more specific to the business, right? But but things around top line is something we do a lot in terms of that data-driven topic because then you can kind of leverage that data to then form initiatives for the top line. What we now do more and more now as well is kind of AI in initiatives on the efficiency side, so that's more kind of operational side. So things like voice EI topics or customer service or EI agents for for those kind of I'd call it more operational efficiency topics. this is also something we do and yeah, same story. the the reason why we do it it comes from the data, right? It's where we see some things either a miss The full journey, right? We we don't fully understand the full kind of cycle of a customer. and in order to capture that, you need to create some initiatives to be able to capture it. Or where we actually have the transparency that certain you know, that the performance of certain channels performs better, so we then can take some learnings from that and apply it to to to for instance other channels. In a way that w where it's transferable. So it's it's kind of capturing all the growth initiatives that we do in that initial kind of full transparency exercise, which is the data-driven reporting, we call it. And yeah, and I think there's no magic to it to it. I think it's more our secret sour sources, I think, is That we can we have the capabilities to do the data analytics topics at the start of it, and we have the engineering capabilities to do the implementation of the topics that we find are then helpful for the growth. And I think doing that full cycle from a kind of data-driven assessment of what's needed to then executing on those initiatives is what really makes us I think unique because not everyone has a software team that can actually implement those things in addition Pete Vera, Exit Algorithms: Yeah. Diana Vainberga: to the strategy bit and I think that's why and this is what I found one of the topics also I I I came to develop X where I was like, Okay, we actually can help our our clients with you know with strategy but also with the execution as a true sparing partner because we have the capabilities and we we've done it before ourselves. So we build businesses so we we we will not just advise we have the expertise of having done so. And yeah, so that that makes that makes it interesting. I'm coming back to my my interesting point and Yeah, to to kind of help help funders build their growth stories. And this is what we kind of wanna stand for it. So we wanna help people build growth stories from the start to to the end. so also supporting them throughout the holding periods and then helping them position their their assets for exit because that's the fun thing to do. And I think we are yeah, we have the capabilities Capabilities network and the mindset of doing that. Pete Vera, Exit Algorithms: Yeah. love it. Awesome. I have one final question and you ta you answered it pretty thoroughly earlier on exit readiness. But I'd like if you could summarize and give one practical tip for business owners wanting to position their company for a strong sale outcome right now. What would it be? Diana Vainberga: That's a good question. I asked I already ans give you many points. but I think Stay curious and do it early. The curiosity point is around the AI topics, right? Because you can't position something for an exit where you're not asking the questions to yourselves first before the investors ask them, right? So I would say since I mentioned many points already, stay curious and get onto it early would be the two points. Pete Vera, Exit Algorithms: Awesome. Wow, thank you so much, Dan. A lot of good insights today. if the listeners enjoyed this conversation as much as I have, where can they find you? where's the best place to learn more? Diana Vainberga: good question. I mean I'm on LinkedIn, so happy happy to connect with everyone on LinkedIn. Pete Vera, Exit Algorithms: Great, thanks for coming on today, Dan. It was a great conversation. Diana Vainberga: Thanks Pete. Have a great day.