Private Equity and AI: From Hype to Portfolio Execution

Speaker A: Get ready to peer behind the curtain of the private equity universe with each episode of Best But Never Final. Hi, I'm Lloyd Metz, joined by Doug McCormick and Sean Mooney. Together we'll navigate the corridors of private equity, revealing the uncommon knowledge, challenges, successes and lessons that drive the world of private equity and business forward. Let's go.

Speaker B: It's great to be back with Lloyd and Doug. Doug and Lloyd. How are you guys?

Speaker C: Great.

Speaker A: Good. Good to see you, Sean. Good to see you, Doug.

Speaker C: You too, buddy.

Speaker B: Today we thought we would talk about the topic that is on everyone's minds, NFL draft geopolitics. Geopolitics.

Speaker A: What are we talking about?

Speaker B: No, we are not going to do geopolitics. Let's talk about more about the future that at, uh, least I choose to view through the lens of abundance here. As we talked about last time, we thought it would be really interesting to dig into the ever present topic of AI. But not just the topic and buzzy buzzwords, where does it come from? But actually, how do you do the stuff in the current today? Does that sound like a good plan? Guys?

Speaker D: Love it.

Speaker A: Absolutely. Much needed conversation.

Speaker B: All right, so we're going to do our best here to make this really abstract topic something tangible and actionable for our listeners here. And because I lack the ability to make something tangible and actionable, I brought in a very good friend and acquaintance and colleague of mine, James Aylward, who is the Chief Product and Technology Officer at bluewave. James, thank you for joining here.

Speaker D: Thanks, Sean. It's exciting to talk about AI, uh, and let's get into geopolitics as well. Yeah, a little hard to talk about AI without a little geopolitics. It's shaping the world we live in dramatically right now. So let's get after it.

Speaker B: That's good. Well, maybe before we jump in, James, why don't you give us just a little bit about your background.

Speaker D: So I've been working with algorithmic and AI product experiences for at least 15 years now. Most notably was founding the artificial intelligence incubator at Fidelity Investments and trying to work out different ways to bring AI to a 76 year old at the time, financial services firm. And that was really where we got started. That was, hey, how do we go from algorithms and data, uh, to an actual meaningful outcome for people across that firm? So that was everything from classifiers to predicting engines. We started that in 2016, which was 10 years ago, which feels like a million trillion years ago in AI terms. But the underlying approach hasn't changed from there. I went to a number of different companies mainly on two uh sided marketplaces where we're trying to match supply, which could be content engines generally to demand or uh, coming in. People want to find the right content. And to do that there's a lot of probabilistic programming and AI that needs to happen in the matching up of supply and demand. And overall that's how you bring algorithms and AI to an experience for a user that actually makes a difference.

Speaker B: I think for our listeners who know about Bluewave, we have elements that are similar to this kind of matching world of marketplaces. We describe ourselves as a market network, which is a fusion of a marketplace and a business network, a little bit more complicated and sophisticated. And so we're really fortunate to have James here with us given his level of expertise. But also what we try to have at least is as forward a looking posture and perspective on AI as possible. And so he brings a lot of expertise that he's also going to kind of pull back the curtain and show to our listeners here as well. But we decided that this is such a crazy topic. James also decided to bring in a friend of his. So James, you want to share?

Speaker D: Absolutely. So I mean a lot of the AI that I'm talking to other leaders around the industry about is built by big companies, but really where it makes a difference to customers is how we use a lot of those AI models and AI products and apply them to use cases that really make a difference for uh, people in the world. And one of the inspirations for me living, I live in Boston and I love to get along to the Cambridge Innovation center and Nathan Plummer is highly crucial to that operation. Also a thing called Venture Cafe, which every Thursday has about 400, 500 different startups all looking to bring in AI and apply it to just incredibly diverse use cases. So I thought it'd be great to have a chat with Nathan as well. Nathan's a fellow Aussie, a fellow Sydneysider at that, so it's great to have another great accent in Boston. So Nathan, do you want to provide a little bit of info about yourself?

Speaker E: Sure. Thank you. And thank you everyone for having me as well. I think the Australian uh, contingent in Boston is running strong now and I think that's like a big role in getting the, the invitation from James. But as James mentioned, gotten to know him through his involvement in Venture Cafe and Cambridge Innovation Center. Uh, my role is co executive director of Venture Cafe Global Institute. Despite its name, we don't actually serve coffee, but it's named after the concept of a coffee house because Venture Cafes runs physical hubs is a place where people interested in innovation, entrepreneurship and those looking to build and grow a venture can come together, can identify the right kinds of resources, education, knowledge as well as connections to further their initiatives. And so we've been doing this now for 15 years. Today we operate in 16 cities around the world. We spun out of an organization called Cambridge Innovation center, which James mentioned before. And for those who are unfamiliar with Cambridge Innovation center or cac, they were really one of the first globally doing this concept of creating physical spaces and co locating a high density of um, startups, investors, people in really deep tech research fields. So where I'm sitting today, between this building and the next building, there's around 900 companies that are based here. Most of them are uh, startups, most of them in the deep tech space as well being Boston. But obviously we run hubs all around the world. Some notable companies that came out of here included Google Android that started here and grew. And the final thing I'll say is Venture Cafe's programs are diverse. So yes, we have startups, we have people like James who are technology experts, but we have, it's the full, what we call innovation stack. So people in government, people in corporations, people in startups all coming together, interested in innovation, entrepreneurship and interested in connecting with other people who can really help them solve problems and create things together. So thank you again, uh, for having me as well.

Speaker B: Absolutely. We've clearly got experts here that are going to help us really understand kind of where we came from, where we are, but more importantly what to do. Now the couple thing I will say as a side one, I just thought both of your accents were kind of like slightly unique Boston accents. So I didn't know you were from Australia, but I've learned that now. And the other thing that I think in particular, Nathan, you'll be excited because Doug and Lloyd love sharing this every time they get a chance. They both went to school at this little school outside of Boston. Where is that school? Doug and Lloyd?

Speaker C: Come on, Lloyd, you're lead.

Speaker A: Okay, first of all, Sean, um, you're the one who brings it up. But we both went to Harvard Business School.

Speaker B: Oh, I forgot about that.

Speaker C: Okay, so Lloyd, I think the relevant statistic though is that at business school all the Brits end up scoring really well because they sound so good and smart. And uh, I don't think the Aussies do. So I don't know. It's a nice accent, but it's not quite as Nice as the Brits, but

Speaker B: it's better than the American one. All right, so our smack talking is out. We've broken the ice here. And so let's talk about the topic of the maybe a generation, probably, if not all time.

Speaker C: Right.

Speaker B: And so as we jump into the conversation, let's set the foundation here. So, James and Nathan, briefly, as we kind of get to the next core part of our conversation, what was AI like five to 10 years ago? Was it even a word?

Speaker D: It was definitely a word. It was artificial intelligence. It's been around Since, I mean, AI was at the Dartmouth Conference in 1956 was the birth of artificial intelligence. So it's been around for a very long time. Just didn't work very well for a very long time. I mean, 10 years ago we had a lot of conversations about is this really AI? And there was plenty of examples of things that I consider to be really AI in the world at the time. When things like Google Maps is an example of AI, It's a way to have a probabilistic outcome depending on a bunch of calculations to get you to do something that you didn't think was possible with computing at the time. There was examples of it around. You'd also have to find a data set, work out, uh, what the ground truth answers were to a data set, make sure you had it all feature condition. The data had to be groomed really, really well. You had to not overfit models. These are all things we don't talk about much anymore. I know they do on the really deep end of the AI's world. That'll change just overnight. When ChatGPT came out two years ago, it's a Gutenberg moment. So crazy to see an LLM, um, nail it like ChatGPT did two years ago. We did have LLMs previous to that, and to an extent that's how Alexa works and other voice activated things. But it was nowhere near as easy and good as ChatGPT. It's similar to how the iPhone changed things up in 2007 as well. But, uh, it just unlocked a whole bunch of opportunity for everybody overnight.

Speaker B: So it was there, it just was a lot harder and didn't work really well. Nathan, from your perspective, if you go back to your organization and you looked at all the startups five years ago, how many of them were doing deep AI kind of initiatives versus application software?

Speaker E: That's been the biggest shift, I would say. And we collect data on, um, everyone who comes to Venture Cafe and participates in one of our programs. The reason being Is that we want to know who's coming, what's their areas of interest, what's their stage of business, what technology enablers are they focusing on, are they utilizing? And 8 to 10 years ago or 5 years ago, even when AI was referenced as a uh, technology enabler that's being utilized, obviously there is smaller uh, amounts that were indicating that, but even those that were, it was one of just a mix of all of the other enablers. So seen as kind of just like a technology alongside, just like how there's all of these other technologies emerging, like people are interested in robotics and people are interested in quantum and there's kind of all of these range of technologies emerging. And I was just thinking as one of those areas and I would also just say that those that were kind of native to AI, it was restricted to very large organizations that had the resources and the capacity to invest in those tools at the time. And so that could be in a research capacity, it could be university or really large corporations. But it was not being seen in startups in the way that we do today where effectively majority, if not all of the startups that we're seeing emerge and we're supporting at Venture Cafe are uh, either AI native or AI enabled in really some really deep and impactful way.

Speaker B: It went from a concept to kind of everything. Now we've seen maybe a little bit before in the Internet, as a non

Speaker C: technology guy here, what would you attribute as the big innovation? Is there a single innovation or is it really that the ecosystem of uh, compute speed, data accessibility all matured in a way that all of a sudden it became really useful?

Speaker D: Yeah, I mean I think compute data, uh, and the algorithms got better and better over time to make things more efficient. So you can get back a massive answer to a prompt really quickly today as that changed the game and was part of the key innovation for ChatGPT. But what I'm experiencing right now is just a exponential improvement in the tools that keep coming out week over week. It's insane. I've never felt a time in my life where technology has evolved this quickly and this fast. So just in the last two months we've gone from using AI to help us uh, autocomplete lines of code in the software and development teams. So having huge parts of the code base written by AI and then reviewed by humans, things like Flaud code, which people might have heard of, have massively accelerated the way we work. Over the last couple of weeks it's been more about how do we change our ways of Working rather than keep on doing the same process. Because the bit that used to be really hard, which was developing all the code and making sure it all works and hangs together, is now the easy part. The hard bit is how do we get this innovation into uh, our customers hands and explain it and make sure it's the thing that people want to use. But I've never seen a more rapid acceleration of innovation and speed within a business than I've lived through in the last two and a half months here at bluewave.

Speaker B: And James, is it fair to say, like from my perspective, similar as a non technical person, but maybe a curious person, is that the ChatGPT moment harnessed the sum total of human knowledge and it could give you perspectives on how things work and what to do in ways that people would take a long time to figure out doing in a more manual way. And maybe what has happened this year, like 2026, is that now it's figuring out how things work, recommending what to do, but then actually doing it.

Speaker D: Yeah. And like anything, it's the future is here. It's just not evenly distributed so people are living different timelines on it. But my experience last year was about using ChatGPT or Claude or Gemini or other tools to help me think through my own thoughts and review emails and do things around docs. And most of the time it was really interesting and made sense. I think the big change in 2026 has been it's gone from being a thought partner to going off and doing things for you. And when people talk about agentic AI, that's what we mean. So instead of hey, help me write the requirements doc for the team on a, on a certain project, it's now help me write the doc and go and code it while you're at it. That's insane to be able to do that. That's the key change. It's gone from help me think through what to do to go off and

Speaker B: do it at lightning speed, at lightning speed.

Speaker D: And then my team's like, oh, okay, we've got to go review this. This is the hard bit. And now we've got to explain it to the customer and now we've got to market it. We talked yesterday, Sean, about all the stuff that we're going to be launching here. The hard bit is that how do we build a narrative and how do we get this all into the hands of our users this quickly?

Speaker A: This episode is brought to you by HCI Equity Partners, a lower middle market private equity firm focused on partnering with family and Founder owned manufacturing, service and distribution companies. ICV Partners, an innovative private equity firm supporting management teams of leading companies at the lower end of the middle market. And bluewave, the business builders network connecting the most proactive business builders in the world with the best of the best service providers for critical variable, on point and on time, due diligence and value creation needs. Now back to the episode.

Speaker B: Maybe before we go into some of like the applications and we'll share what we're doing here directly at bluewave with the listeners here. If you looked at the broader market, maybe even not like tomorrow, but where Were people using AI second half of 2025 to today? What were the big applications that were being used in the world? It was obviously the thought partner, thinking partner stuff. But what else?

Speaker D: He had some specialist breakthroughs on the graphics and video front. Google came out with Nanobanana 2 which was amazing. So up until then a lot of AI images were wrong, although they couldn't handle text or they put just weird stuff all over the place. That's changed dramatically. And Gemini 2.0 came out last night I haven't had a chance to play with yet. But that's multimodal so you can talk to it, you can interact with it pretty much on any product surface you can think of. So that didn't exist end of last year. Like it's all been text based or code based and completing the sentence on things, putting in the next best word after it. That's great if we want to talk about talking to a computer as a, as a thought partner. But it's all changed now with Agentic. It'll go off and do things for you.

Speaker E: I would just say I'm based here in Boston and if you think about that comparison to say San Francisco and what's happening on the AI front there, where obviously that's the home of where a lot of the large models are being developed. But in places like Boston it's mostly about applied AI. So what they mean by that is like they have really deep expertise in certain industries in Boston that's things like robotics, it's clean energy, it's life sciences. Boston is considered the biggest, if not one or two in the world as a uh, biotech hub and Kendall square area in particular. And so it's applying AI, it's not a game of as much, oh uh, we want to get efficiencies and cost reductions of course. Every business will try and do that. The applied AI is about innovation. What is it that they can create now that was just not possible before. And so if you think about, for example, drug discovery, Pandora's box that that opens up, there's a company I met here last week that's doing AI imaging that's accessible to everyone for us to use for skin, uh, cancer checks. There's been imaging tools around for a while, but the quality has not necessarily been to a standard of kind of medical care. It is so high now that it is more effective than actually trained doctors just because of how effective these tools are beyond what say a human eye can do. That's where we're really seeing it in places like this is where there's existing capability and then applying and layering that onto those areas of strengths.

Speaker C: Two observations I'd make. Those innovations that you guys described, capital is flowing in a way that's commensurate with those changes. And so in the private equity world, speaking more about buyouts, there are certain sectors that are heavily favored because people see the opportunity to use AI as a significant cost reducer or, uh, competitive advantage. And then on the flip side, there are certain sectors like software, where investors are reevaluating the business model and the implications that AI will have there. Given some of the things that James was saying about the pace of innovation and how AI is coding on the venture side, it feels to me like as I described, where most of the startups are, it's a pretty compelling value prop in terms of limited capital to innovate and deploy these new technologies. And so traditional startups, a lot of times you have a lot of cash burn, a lot of these applied AI tools are relatively high ROI for the customer, low cost to develop. It seems like a really sweet spot for like the venture community on a, on a risk adjusted basis. And I'm not talking about like the investing in Claude, I'm talking about like these smaller businesses that are really interesting tools.

Speaker D: I think the limiting factor now is not capital as much as it's really deep understanding of a niche use case and having potential access to data that you have. No one else has to be able to provide a bit of a moat around that niche. So even in a world where it's an exceptionally obscure problem or challenge. And I think last time I was at Venture Capital, Nathan, there was a Italian guy from MIT who was tackling sickle cell anemia and had a really innovative way to do it, there actually might be like five people doing the same thing in Cambridge. So you can easily go off and build a solution. Now what sets you apart? Uh, what's the moat? It's going to be your access to data or compute or having a totally innovative way to tackle a problem that's going to set you apart. So having access to obscure data sets is still a very interesting and great place to be.

Speaker C: I feel like uh, 10 years ago we were saying data is the new oil. It's the lubricant that minimizing friction in the economy. And today it's like data is the new asset. The value of data has just gone up dramatically.

Speaker B: It went from being oil to gold, right?

Speaker D: Yeah. Or maybe rare earths. Uh, that's another way to think about it. Not all rocks are the same. So if you can get access to data that really sets you apart in a place which is viable and desirable, it won't be hiring a computer programmer that's stopping you from turning that into some real revenue.

Speaker A: Doug and I being in the private equity world, not as much technology driven or technology focused. But your point about unique data, I wonder do some of the companies that we invest in or that we want to invest in that are in some obscure niches, small niches, do they have just inside of their own four walls interesting data that AI can be helpful for them to extract insights price better find adjacent customer segments? Uh, I don't know. Those are the questions that are coming up in my head when you talk about do you have unique data? And some of the companies that we deal with, they do have unique data in terms of their own business. I don't know how we explore that, but I pose that question.

Speaker D: My take on it is that the algorithm, they're pretty generic at the moment. Yeah, they're all coming out with better ones all day long. But even an algorithm from last year will probably help you out with most of what you're trying to do. Won't be as good but it'll help the algorithm only going to get you so far. The um, compute power is sort of defined by Nvidia and a couple other companies at the moment who own the data centers. And what's unique and different is the data. So it's kind of an interesting way for private equity to view the world is from data up uh and see how you can get that to market in an interesting and new way. That's actually how I do org design generally and wherever I've been is like what is the shape of the data and then how do we build the teams around the data to create the experiences? Which sounds nuts because normally you start with the customer use case and work backwards but the magic is really being able to differentiate yourself from the rest of the market. The way I've seen it work really well is where you have data and they don't. And you can make magic happen in that zone.

Speaker B: In the last fund that I was at, we would almost exclusively focus in data enabled businesses. And the line we had was like data itself. It's like ingredients in a bakery. It's flour, it's salt, it's eggs, it's water. In itself, it's pretty commodity. It's not that value. But where you make the money is when you take that data and make cakes. Uh, and so how are you going to use that data? And so I think what every business can do, whether, uh, you're a tech company or a data business enabled business or a manufacturer, is how do you use that data? And so as I think about manufacturers, what's the data you do have? You have very exceptional insights into your customers, how they buy, when they buy, where they buy, what the ratios are, what the trends are, how your team performs, how your teams perform over time in time, how they interact together. You can get data about how the market is performing. You can get data on the digital landscape. So you can get all this data. And every business is a data business. Most people just have a hard time structuring it, accessing it, and using it to create insights. And every company can and should be doing that. And then it's the really exceptional companies that then productize that data and turn it into something that's not only useful as a business, but then something that they can use to create revenue.

Speaker A: So Sean, sounds like you've done this. How do you get started? If I'm a business owner, you're talking about data. First of all, what is data? Are you talking about the numbers in my accounting system? What are you talking about? And then how do you get organized?

Speaker B: If you think about where we're really spending time in private equity, which is where we had spent time previously, with some sense that this was coming, was the biggest challenge that most companies have. Their data exists in all sorts of different silos. It's incredibly hard to access, it's usually dirty and wrong, and it hasn't been organized in a way that can be used. And so last year we would get all sorts of calls saying we want to do neural networks, something like that. And the first question we'd ask is like, do you have a data lake? Have you brought it into a place where you have a Data strategy? And 9 out of 10 times the answer was no. Well, we have the data in our CRM, we have the data in our ERP system, we have the data in uh, our marketing automation system, but we haven't brought it together. And so really what we spent a lot of time on last year going into this year is like, let's get your data organized, let's get it structured, let's get someone in charge of it and then you can do those things. Probably five years ago we started spending on time like how do we get our data structured? Knowing that someday the robots were going to be smart enough in 2016. Like the whole concept of blue wave, it was going to be a self service system that didn't require humans, which means I wouldn't have to manage people and understand the art of humanity. Whoops. And so that was the big pivot. But we're like, okay, well someday the robots will be. So how do we put it in a way that's at least structured enough? And so the first thing we're spending a lot of time with people is like, get your data strategy together. But it's the least sexy, most unfun part that no one wants to do. In my mind at least, it's one of the most essential parts. James, what would you think about that?

Speaker D: Yeah, 100%. I mean, I think data is a differentiator for you. Having the data in a way that can be easily used by these AI tools makes a huge benefit for the whole company. Of course, you have to satisfy actual human customers at the end of it too. So don't ever lose sight of the actual business. But the way I see data playing a big role in that is you can satisfy those customers in a way that no one else can. If you lean in and hook up a bunch of the AI tools to your proprietary data and enrich that proprietary data with outside data in a way that sets you up to delight the customer. It's kind of business. It's been that way for forever.

Speaker A: Can you say more about what does that mean to organize your data in a data? Like what does that sound like? What does that look like? Can you describe it, uh, without using the word data?

Speaker D: The thing is, the, uh, underlying expertise in a business is often in the brains of the people that walk into the business all the time. The way to get to access that expertise on a scalable way is to make sure it's in a way that a computer can read it somehow. So if all your stuff is currently on paper or in spreadsheets, putting it into a computer program that locks your data into readable rows and columns is a great way to do it. And it doesn't have to be sort of the sort of boring customer contact. That's great to get that CRM data as well. But there are ways that we can bring in written strategy documents. Now, CLAUDE can Read that or ChatGPT can read it and marry it up with your data and tell you whether your strategy makes any sense or not. Like, where are the weak points in your business given your sales data, given your customer data, and bring that all together into one conversation that happens in English or whatever language that is native to you in a browser. Try have that conversation two years ago you'd have to have a team of data, uh, analysts and you go and ask them a question and they come back with something two weeks later and it's kind of what you're looking for, but mainly wrong. And then you go back again and just the cycle time is insane. If you have your data set hooked up to your Claude or ChatGPT or Gemini, you're going to be so much smarter and so much quicker and learn as a company way faster than anybody else can in your segment.

Speaker B: I'll add to that. Let's just say you have, uh, an accounting system, otherwise known as an ERP system, and you say, I want to hook my accounting system up so we can do analytics using claude. Or you name the analytical program. If you just hook it up to a data lake, and this would be like snowflake or DataBricks, uh, are two of the big providers, or Microsoft fabric. If you just put it into there, it's going to be like an export you've probably seen from your accounting system. It's going to be thousands and thousands of revenue transactions that just go on forever in a big file. Well, you need someone to be able to structure the data and say, okay, you're going to build tables. I need the data actually broken out by months and I need it broken up by products and I, uh, need it broken up by customers. Someone's got to tell the system that's how it should be organized. Today that still takes a person and then something's going to get changed in your netsuite and all that's going to break and then you have to do it again. And so part of the challenge is like to really get a lot out of it. You need someone who's like a data architect to organize the data in useful ways that the AI can then say, oh, this is revenue by product, by customer, and this table means that. And it can reason through it. But if you just put a stream of consciousness in from Netsuite, even the AI's mind is going to break. And so you got to have someone at least if you're going to play for tomorrow, who's going to organize that and be tasked with putting the data in some sort of structure that it can infer and interpret what is going on. That takes time. Now the good news is to do that you can start by using like an outsourced provider. And this sounds like a commercial, uh, but it's not meant to be. There are a lot of really good groups who can get you started quick on this. The good news is there's also some tools that are just coming out now that are a lot simpler. We're just saying, like, hook up your ERP system and your CRM system and we'll be able to, without even creating a data lake, let you just do your revenue and revenue and product by customer by margin, yada yada, analysis, and give that to everyone in your company so they can all have a sense for how your customer, product and margin trends are going. So there's new tools that are coming out that are making this easier and easier for every person. But in my mind, like you can't do any of it without someone having to spend time with data. And if you don't know how to do it, I would go and rent someone. It's not that expensive to help you get going. And that's how we started it, we rented it and then we internalized it.

Speaker C: Hey Sean, I'm curious how you think about that approach versus all of the software companies that are providing that data for you that you're going to then organize are also developing their own tools. And so there's the race of do I use the tool of the existing system I have versus do I create

Speaker B: my m own tools?

Speaker C: Is there a hybrid model?

Speaker B: I think all of them are trying to put an AI layer for analytics in, but it's almost like a hospital. None of them want to talk to each other because they all want to own the entire universe. And so they're going to make it really hard to integrate with each other by design because most of Silicon Valley still has this like zero sum game mentality. We've got to win it all. And so they're all putting up moats. Except for, and this is James and Nathan. Dial me in. But this is at least my maybe cynical perspective here is that if I were to do it the way we did it was we brought in Snowflake and we bring in all of our data from the different tools we use because then we can own our destiny and not be beholden to one tool.

Speaker D: Look, I think there's going to be a couple different winners with different strategies. What I'm experiencing with Court at the moment is it seems pretty open. You can connect to a whole bunch of different tools and data and use Claude as your brain or your AI center that brings with it some data exfiltration risks and some data security risks around it. So you really need to identify the data that you can't let Claude have access to. On the other side of that coin, there are people who are very, very data security sensitive and have work in highly regulated industries where HIPAA is a thing or FINRA is a thing. And they're probably going to hook up to that Microsoft model where it's really difficult to collaborate externally, but you can get a lot of stuff done internally overall, that's how the broader market will change. What I'm excited for is the Cambrian explosion of little companies that are gonna kick up and start up with two or three people across the world because they have access to compute power that they never had before. What that looks like, if you want to be ahead of that curve or in that curve and not left behind, is get really good at, uh, Claude or ChatGPT. You can go off and learn. There's a whole bunch of different learning resources out there and hook up your data to it and just act like a 5 year old. Keep asking as many questions as you possibly can of your data and you can do it in English and get back answers in English in 10 seconds. You'll be smarter, uh, in three days than you ever have been once you hook all that stuff up.

Speaker B: I think it's an excellent point. Every company needs to have a data strategy. And James brings up a good point. You have to be careful about not only the data that you have, but the exfiltration. Can someone in your team basically just download all your data and take it with them? There goes your moat. Right? So you have to be thoughtful about that. That's candidly still the hard part today is letting these tools kind of select what they can use and what they can't. And then I think there's a training element of every employee in your company needs to know how to use these things and learn and actually build something to prove that they can use it. Because I think there's a lot of people buying a lot of subscriptions to Claude into ChatGPT and to you name it or Gemini. And no one uses it other than kind of superfluous stuff. They're probably getting 10% of the power of it.

Speaker E: Yeah, I think the AI literacy side of it is huge. That's what we're seeing now. And really over the past six months that's exploded this demand for how do we actually grapple with these tools and how do we apply them within our own business. And so part of that is really just for one, especially if you're a business leader, it's thinking about, um, trying to understand and be educated on what these tools can do and are good at today, are currently good at and what they say not good at. And so you're identifying and really thinking about the key areas of impact within the organization. But I really do think for most organizations it's kind of a top down and bottom up. So in terms of bottom up, it's at every single level of the organization, people having education and awareness of various tools and being open to thinking about how those tools could be applied within their role. Because often you'll find like we actually went through this within our own organization and I've seen others go through similar. When you start to go through that education process, people are naturally going to be resisted because they think about how does this impact my role. And for many cases it actually makes their roles more interesting. If you think about many jobs, it's actually removing a lot of the stuff, the boring stuff, and enabling them to focus on the higher value impact work. And so that is the top down piece which really goes to the business leaders here is identifying then and creating a kind of strategy around it. Not just random tools happening everywhere, but how we're utilizing these tools and then thinking about where are we today? And it's not just about doing things faster, uh, more efficient, cheaper. It's actually about being able to transform and create more value and do things we're currently not doing. And I think if that comes from the top down people's jobs, their engagement with their AI becomes a lot more interesting and something that would actually accelerate the kind of transformation of the organization.

Speaker D: Yeah, one way to do this really badly is to hire a data scientist and put that next to a business expert and expect magic to happen. At that point it doesn't work. Uh, I've done that personally. Total disaster. What does work is when the business experts that have been there for 10, 20, 30 years, I start to understand a little bit about what's possible in the world of AI. And you can unlock that with a few hours worth of education online. And then they start saying, hey, is this a good idea? Is this a great idea? And invariably it becomes a great day. Start painting with more colors in their palette and then you might want to go and hire a data scientist to get all this done. But it's the intrinsic business value that your experts have in your business that you want to unlock in a way that we've never been able to unlock before through technology.

Speaker B: I think James is spot on on that. And I'll give you kind of like a practical example. You're gonna see a thousand different tutorials. You can watch them, but it is kind of just like mind numbing. But I think Claude does some great ones where they just make them free. You can do the basics to get a base load. I felt like I was pretty far ahead for a while. Then I felt like I was really far behind on the latest stuff. And I'll give a shout. I have no connection to this person. But someone just showed up on my Instagram feed named Sabrina Romanoff. A lot of her stuff was like, this is kind of interesting. I just found myself watching her YouTube videos then. And so I was like, rather than doing these tutorial after tutorial, I bought a refurbished Mac because I didn't want to screw up our whole company, which is probably like 50% chance. James was like, no, no, no, please don't play on it. And I was just like, I'm going to do her little course and just going to build something on Claude code to learn it. And A, it was a blast. And B, suddenly I'm like, now I know how to use Claude code because I was actually like making something. And so little shout out. If you go on X.com, go to uh, AlphaDrop Daily. You can see I have an automated multi agent media channel that's just posting on actionable news on the private equity industry and it just runs itself. It came up with the name, the strategy, the approach and it just auto runs and I have to dial it in. Every week it gets a little better. It's pretty amazing. I think I'm up to four followers. But that's like a great approach to do is just learn by doing. I think where James is going, going. I'd be careful about doing that like within your own company systems though.

Speaker A: But Sean, to that point, what does that opportunity look like that makes somebody want to take the red pill? Because one could listen to this conversations like, you know what, I'm good with the blue pill, right? I'll just stay where I am. But, uh, the red pill is where you want folks to go. What's the opportunity that makes them want to do that?

Speaker B: I'll let James do the cooler examples because I won't be able to speak intelligently to them whatsoever. Here's a practical one. Everyone hires and hiring is really hard. And so we use a structured interviewing process here at Bluewave that was foundational from a group called Hunter, Hunter and Schmidt, where you do a skills interview, you do a traits interview, you do a workplace simulation and you test their traits and kind of like cognitive processing skills. It's like the book who, if you've read that by Jeff Smart as well, that process, every time we would hire to put together that pack would take hours and hours and hours for every new hire. And so what I did on, um, Claude is like, well, I wonder if I can turn this into a skill. And if you do the Claude 101 training, this is really easy. And so I just went through a job that we were getting ready to hire, and I just took them through the entire process in a chat that we've made and it walked through the whole thing. It was just a Socratic conversation. Then it took me the hours and hours and hours to do it. The next hire we were making, I basically went to that other one and say, turn this into a chat. It turned this thing into the most amazing product that gave me the questions to ask. The answer guide that we're looking for, the company description, the job description, what you're going to do in 90 days, what each of the different modules of the interview, the personality profile that we're looking for a person can go through and get the most amazing hiring package I've ever seen in my life in 30 minutes. And that would probably be like seven hours. And anyone could do this thing. And now we've used that for like multiple hires. And another company that I'm friendly with was hiring a CEO and I just walked them through it and just gave it to their board. And they're like, how did you do this? And like, anyone can do this. And then, by the way, my other thing is now what I do is I take the transcript from my interview and then I feed it into Claude with that whole hiring pack. And I say, be a thinking partner and help co grade the results against the scoring grid. And it gets a little crazy at times, but it's pretty damn good. And it always adds to what I think. Like, you can't delegate thinking on it. The most important thing we do today still is hire people. And it just like anyone could do that with a little bit of skill and then just like curiosity and creativity to just go through the process.

Speaker A: Uh, that's a great example, Sean, is that available across your firm to use?

Speaker B: Yeah.

Speaker A: And so I guess not only is it saving your time, it's saving however many people in your company using it their time. But I imagine it is also introducing more consistency.

Speaker B: We hire more consistently is a huge thing because everyone, even though we had this system, everyone would still do it a little bit different. And so you would have various levels of people kind of engaging in, and we call it the blue wave hiring way. And people would do it always a little differently. So you get different results. And so now you have like a way of doing things in your organization.

Speaker A: That's a great example. Thank you, James.

Speaker B: You want to talk about, let's say you get your data in place, you have a team that knows how to use these tools and actually get something out of them with really some basic training, and then just have your team do a project on it. Then what's some of the really cool stuff you can do? Maybe talk about. We call it internally. It's a different spelling and a different pronounce. Jarvis. How about for any of the lawyers in the room?

Speaker D: Yeah, it's an internal tool. It's inspired by Jarvis. But we've hooked up the Snowflake data, uh, and also worked out how to bring in other data feeds that complement Snowflake. Plus work out how to add our layer of security around the Claude, access to certain data and look at it from different Personas within the company to make sure that everybody has the right access of data. That's harder than you think, because one thing about CLAUDE and chatgpt, they're very, very helpful and they will help to get around any security stuff you put in place. So you really need to be very, very strict with Claude. But what happens is you can talk to Claude internally and get the answer to any internal question you want to know. Do you want to know how the sales team's going? Or what is the best opportunity for us tomorrow based on the day that we got yesterday? And it spits out every morning to every person, hey, let's keep focusing on X or Y because we're hitting that goal or not hitting that goal right now. You can do that by having a conversation literally with Claude to this system we call Jarvis internally. And it took about a week to set up. It's not like six people working for six months on this. This is one or Two people configuring stuff on the back end for a week and upskilling the entire company. It's nuts. We're also being able to harvest real time voice calls and information internally and being able to put that into transcripts and be able to really hit it hard with, hey, what would our top clients say about this? What would our new customers say about this? What would a uh, really highly skeptical engineer think about this approach? Jarvis, uh, will go and beat up any idea so it's really well honed and then be able to put it into an ROI calculator and stack rank any idea in the company within seconds to work out whether it's the best idea going or not. And if it's not, then we won't work on it. All of that was imaginary three months ago. This is nuts. I think with a red pill, blue pill, by the way, red pill might feel really uncomfortable to learn a new process and a new experience. But if you just coast on and keep going with what got you here, that's really scary. I think you need to weigh up how uncomfortable you might be in the future if you don't lean into AI right now.

Speaker B: So Jarvis, as a CEO, I literally can ask it anything about our company that I'm curious about and it gives me the answer. So I'm going to make our client coverage team really nervous because I was like, okay, give me the performance of the groups in force, rank who's quote, unquote, um, best and worst and let's figure out a way how to define what best and worst is. And it started actually running analytics on its own and deciding which data to grab. So as I think about if I were back as a private equity investor and I could do that across the portfolio company and have a thinking partner with it. And if my CEOs of the portfolio companies had that, it would be unbelievable level of insights that used to take months and months and hundreds of hours to do at your fingers tips. To me that would be like one of the ultimate sources of alpha and anyone can do it.

Speaker E: Now I would add one thing from a venture cafe perspective is we're going through this ourselves and our mission is connecting innovators to make things happen. So the value that we create is connecting to people who potentially are not meeting, but the opportunity at the intersections of what they do is really, really high. And so for most of our teams around the world, 80% of the work that's involved in that, uh, in producing that output is things like research, online research. It's Coordination. So the kind of email component, it's marketing. So both push content to website through to newsletters through to social media. All of that kind of stuff we're looking at how do we accelerate automate as much of those activities as possible so we can shift our teams from the connecting part to not just being actually a part of their role that they do around all of the tasks to get to the connecting that actually that's the bulk of the role and the kind of impact and the potential of that in what we can have is much, much higher. And I would just say to James point there, I would agree with the red pill perspective. It's more the risk of inaction rather than not doing so at this stage.

Speaker B: That is an excellent point. And the other thing I would add Nathan, that as you're talking about Venture Cafe that got me thinking about what's a way to play this as a business builder. It's be around other business builders and creators and innovators and where do you find that? And I don't mean this to sound like a commercial, but it is and should be. Go to places like Venture Cafe and get into these ecosystems where people are at the cutting edge so that you can understand where the future is going. And this wasn't planned so I'm not trying to uncomfortably flatter you all, but it's appropriate. If people wanted to get their forward thinking people within their organizations, their earlier adopters in their organizations, how do they find Venture Cafe, how do they know where to go, how do they get involved, those type of things.

Speaker E: Well, thank you for the opportunity. Always welcoming more people. When you come to Venture Cafe for the first time, you'll be a one the name tag and that's how the community kind of understands who's been there, who's there for the first time, who's returning. But you can go to venturecafeglobal.org and see our range uh, of cities that we're currently operating in. If you're in a city and say you feel that there's a need for what we do there, we'd love to talk to you as well. You can also do that through venture cafe global.org but I would also just say is many cities, I mean all cities have support functions like a Venture Cafe. And so if you're kind of grappling with my gosh, this feels so overwhelming. Where do I even start? The best thing to do, just be out there with other people who either wondering, asking the same question and find a community of like minded People who can, can work through this together, or often those communities will involve mentors, experts, people who have been through this and who can really advise you and give you direction on, on where to go next. And every city across the US will have various resources for you to access as well as obviously we have a big online world as well. But I would say that the physical component of, um, being able to develop a network that can guide you through this is really, really critical, 100%.

Speaker A: Nathan, I'm pondering a comment that you made, and I actually think it's worth a longer conversation and sort of part AI, part human psychology, but you made a comment that you can't do nothing, you have to do something. And it's not obvious what the cost of doing nothing is. And I think that may be worth talking about because clearly you have a point of view. But I don't know if it's clear to all of our listeners. I'm not even sure if it's clear to me, but we should talk about that further.

Speaker E: Sure. Well, when I think about these kinds of things generally, I think history can be the best guide in these instances. And even if we think back not so long ago, with the rise of the Internet, which really kind of became what it started to take off in probably mid-1990s, particularly with Windows 1995, there would have been a time when the Internet in businesses was restricted to a couple of people in an organization and a particular function. And it was a very narrow function of what that meant. And for most people, that didn't apply to them. Right. That was this kind of outside technology. And there, uh, for many businesses, we're not a digital business. We know now there is no such thing as being a, uh, digital and being not a digital business. If you're a business, you inherently need to be digital at the core in some capacity. I think about it the same way in terms of AI. So if we are, uh, today at that 1995 Windows moment, with AI and the kind of tools being proliferated and really the growth starting to boom. The difference being with AI and the Internet is the Internet took some decades to really evolve in terms of speeds and what it could do and the tools, this is happening at an accelerated pace that is so much faster than what the Internet boom was. So if we think about the transformation that happened over those years and we apply that and think about, okay, if this new technology is going to have a similar, if not bigger impact and it's moving faster, I think it gives some perspective around the kind of urgency of really adapting and being on the forefront with this rather than it happening to you. Because the reality is whatever business you're doing, your competitors are going to be doing it and you'll be left behind if you're not.

Speaker B: For everyone here, whether you're motivated by loss or gain, uh, and those motivate different people differently. James and Nathan's point are spot on here and that the move you gotta do is take that first step and then take another one and another one because it's easy to get all wrapped up in like I've got so far to go. But if you take that first step, the second one becomes a lot easier. My reflection maybe on this conversation is there's a lot more to cover in terms of how do we practically and actionably do it. And I think we have information to share with our listeners here. So Lloyd and Doug, maybe we do this. Let's delve deeper into this and I think there's more conversation about how to actually do it and what to do to get those first steps going. There's conversation about where you can actually apply it in your organizations effectively. And then there's another conversation around what tools you can use to get started more quickly. Does that sound like a good plan there?

Speaker A: Absolutely. I think that's a uh, conversation we should definitely have.

Speaker B: Uh, all right, well, for Lloyd and Doug, I am immensely appreciative of James and Nathan joining us here today. So thank you very much. Nathan will include your contact information in our notes.

Speaker E: Thank you very much.

Speaker B: And James, you can find via any BlueWave outlet.

Speaker A: Thanks James. Thanks Nathan. To be continued.

Speaker D: I really appreciate it.

Speaker E: Thank you everyone.

Speaker C: A uh, special thanks to HCI Equity Partners, a lower middle market private equity firm focused on driving transformational growth through consolidation strategies by partnering with family and founder owned manufacturing, services and distribution companies. Uh, learn more@hciequity.com ICV Partners, an innovative lower middle market private equity firm supporting management teams of ah, leading companies at the lower end of the middle market. Learn more about ICV@uh, ICVPartners.com and finally BlueWave, the business builders network. Connecting the most proactive business builders in the world with the best of the best service providers for critical variable, on point and on time, due diligence and value creation needs. Learn more about BlueWave@bluewave.net. for further information on HCI, ICV and BlueWave and relevant topics discussed here in the episode, please see the episode notes for links.

Speaker B: The views and opinions expressed in this program are those of the individuals presenting and do not necessarily reflect the views or positions uh, of any other persons or entities, including those referenced herein. No representations, warranties, financial, legal, tax or other advice are made herein. Consult your advisors regarding any topics discussed during this episode.