Private Equity Meets AI: Practical Insights for Portfolio Success

Sean Mooney: [00:00:00] Welcome to the Karma School of Business, a podcast about the private equity industry, business best practices, and real-time trends. I'm Sean Mooney, BluWave's founder and CEO.

Today, we have a special crossover episode from our sister podcast, "Best But Never Final," where Lloyd Metz, the Managing Partner of ICV, Doug McCormick, Managing Partner of Iridium, formerly known as HCI, and I, are joined by James Aylward, the Chief Product and Technology officer of BluWave, and Nathan Plummer, Co-Executive Director of the Venture Cafe Global Institute, a leading community at the heart of AI.

In this conversation, we talk about how AI got here so quickly, where it's going, and how you can and should proactively get going now activating AI. Enjoy.

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

Doug McCormick: Great.

Lloyd Metz: Good. Good to see you, Sean. Good to see you, Doug.

Doug McCormick: You too buddy.

Sean Mooney: Today we thought we would talk about the [00:01:00] topic that is on everyone's minds.

Lloyd Metz: NFL Draft?

Sean Mooney: Geopolitics. Let's go. Let's go.

Lloyd Metz: What are we talking about?

Sean Mooney: No, we are not going to do geopolitics. Let's talk about something more about the future that at least I choose to view through the lens of abundance here. As we talked about last time, we thought we would, it would be really interesting to dig into the ever present topic of AI, but not just the topic and buzzy buzzwords: where's it come from, but actually how do you do the stuff in the current today?

Does that sound like a good plan, guys?

Doug McCormick: Love it.

Lloyd Metz: Absolutely. Much needed conversation.

Sean Mooney: 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, an acquaintance and colleague of mine, James Aylward, who is the Chief Product and Technology Officer at BluWave. James, thank you for joining here.

James Aylward: Thanks, Sean. It's exciting to [00:02:00] talk about AI, and let's get into geopolitics as well.

Sean Mooney: Yeah.

James Aylward: It's a little hard to talk about AI without a little geopolitics too.

It's shaping the world we live in dramatically right now, so let's get after it.

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

James Aylward: 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 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 [00:03:00] approach hasn't changed. From there, I went to a number of different companies, mainly on two-sided marketplaces where we're trying to match supplier, which could be content engines generally, to demand. 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.

Sean Mooney: I think for our listeners who know about BluWave, 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 [00:04:00] also going to 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?

James Aylward: Absolutely. So 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 people in the world.

And one of the inspirations for me living where I live in Boston, I love to get along to the Cambridge Innovation Center. 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 Sydney-sider at that, [00:05:00] 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?

Nathan Plummer: Sure. Thank you. And thank you everyone for having me as well.

I think the Australian contingent in Boston is running strong now, and I think that's played a big role in getting the invitation from James. But as James mentioned, gotten to know him through his involvement in Venture Cafe and Cambridge Innovation Center. My role is Co-Executive director of Venture Cafe Global Institute.

Despite its name, we don't actually serve coffee, but it's named up to the concept of a coffee house because venture cafe's run 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 [00:06:00] 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 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 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 [00:07:00] 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 for having me as well.

Sean Mooney: 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 an aside, one, I just thought both of your accents were 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 a school at this little school outside of Boston. Where is that [00:08:00] school, Doug and Lloyd?

Doug McCormick: Come on Lloyd, your lead.

Lloyd Metz: Okay. First of all, Sean, you're the one who brings it up, but we both went to Harvard Business School.

Sean Mooney: Oh, I forgot about that. Okay.

Doug McCormick: Hey, 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 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.

Sean Mooney: But it's better, 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, right? And so as we jump into the conversation, let's set the foundation here. So James and Nathan, briefly, as we get to the next core part of our conversation, what was AI like 5 to 10 years ago, was it even a word?

James Aylward: It was definitely a word, it was artificial intelligence. It's been around since, AI was at the Dartmouth [00:09:00] 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. 10 years ago we had a lot of conversations about, is this really AI?

And there was plenty of examples of things that I considered to be really AI in the world at the time when things like Google Maps is an example of AI, 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 what the ground truth answers were to a data set, make sure you had it all feature conditioned, the data had to be groomed really well. Had to not over fit 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 all changed just overnight when ChatGPT came out two years ago.

It's a Gutenberg moment. So crazy to [00:10:00] see an LLM 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, it just unlocked a whole bunch of opportunity for everybody overnight.

Sean Mooney: 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 5 years ago, how many of them were doing deep AI kind of initiatives versus application software?

Nathan Plummer: That's been the biggest shift, I would say, and we collect data on 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? [00:11:00] And 8 to 10 years ago, or 5 years ago, even, when AI was referenced as a technology enabler that's being utilized, obviously there was smaller 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 just like a technology alongside just like how there's all of these other technologies emerging, people are interested in robotics and people are interested in quantum, and there's all of these range of technologies emerging, and AI was just seen as one of those areas. And I would also just say that those that were native to AI, it was restricted to very large organizations that had the resources and the capacity

to invest those tools at the time. And so that could be in a research capacity. It could be a university or really large corporations, but it was not being [00:12:00] seen in startups in the way that we do today, where effectively a majority, if not all of the startups that we're seeing emerge and we're supporting at Venture Cafe are either AI native or AI enabled in really some really deep and impactful way.

Sean Mooney: It went from a concept to kind of everything. Now we've seen maybe a little bit before on the internet.

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

James Aylward: Yeah, I mean I think compute data 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. Whereas, that changed the game and was what part of the key innovation for [00:13:00] ChatGPT, but what I'm experiencing right now is just an 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 auto complete lines of code in the software and development teams to having huge parts of the code base written by AI and then reviewed by humans.

Things like board 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 our customer's hands and [00:14:00] 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 BluWave.

Sean Mooney: And James, is it fair to say, like, from my perspective, similarly as a non-technical person, but maybe a curious person, is that the ChatGPT moment harnessed to some 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.

James Aylward: 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, [00:15:00] 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 then 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 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 do it."

Sean Mooney: At lightning speed.

James Aylward: At lightning speed, 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? [00:16:00]

Lloyd Metz: 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 BluWave, 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.

Sean Mooney: Maybe before we go into some of the applications, and we'll share what we're doing here directly at BluWave with the listeners here. If you looked at the broader market, maybe even not like tomorrow, but where were people using AI the second half of 2025, to today?[00:17:00]

What were the big applications that were being used in the world? It was obviously the thought partner, thinking partner stuff, but like what else?

James Aylward: We had some specialist breakthroughs on the graphics and video front. Google came out with Nano Banana too, which was amazing. So up until then a lot of AI images were wrong, or they couldn't handle texts 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 it 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 in the 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 thought partner, but it's all changed now with Agentic. It'll go off and do things for you. [00:18:00]

Nathan Plummer: 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 biotech hub, and the Kendall Square area in particular. And so it's applying AI.

It's not a game of as much, "Oh, 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 [00:19:00] before? And so if you think about, for example, drug discovery, uh, 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 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.

Doug McCormick: 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 [00:20:00] sectors that are heavily favored because people see the opportunity to use AI as a significant cost reducer or a 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, like 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 the venture community on a risk adjusted basis. And I'm not talking about like the investing in Claw, I'm talking about these smaller businesses that are really interesting tools.

James Aylward: I think the limiting factor now is not capital as much is it's [00:21:00] 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, I met this Italian guy from MIT who was tackling sickle cell anemia and had a really innovative way to do it. There actually might be like 5 people doing the same thing in Cambridge, so you can easily go off and build a solution. Now, what sets you apart?

What's the moat? It's going to be 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.

Doug McCormick: I feel like 10 years ago we were saying data is the new oil. It's the lubricant that's minimizing friction in the economy.

And today it's like data is the new [00:22:00] asset. The value of data's just gone off dramatically.

Sean Mooney: Went from being oil to gold, right?

James Aylward: Yeah. Or maybe rare earths. That's another way to, 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 doing that into some real revenue.

Lloyd Metz: 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? I don't know. Those are the questions that are coming up in my head when you talk about do you [00:23:00] 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.

James Aylward: 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 like, 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. I'm only going to get you so far. The compute power is 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 an interesting way for private equity to view the world is from data up, and see how you can get that to market in an interesting and new way. It's actually how I do org design generally. And where 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 [00:24:00] 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.

Sean Mooney: And the last fund that I was at, we would almost exclusively focus in data-enabled businesses. 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, right?

And so how are you going to use that data? And so I think what every business can do, whether you're a tech company or a data- 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, [00:25:00] 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.

Lloyd Metz: 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 like the numbers in my accounting system? What are you talking about? And then how do you get organized?

Sean Mooney: If you think about like 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 [00:26:00] incredibly hard to access. It's usually dirty and wrong. 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 it's, 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 our marketing automation system, but we haven't brought it together." So really what we've 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 5 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 BluWave, it was going to be a self-service system that didn't require humans, which means I wouldn't have to [00:27:00] manage people and understand the art of humanity. And 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?

James Aylward: Yeah, 100%. 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 [00:28:00] data and enrich that proprietary data with outside data in a way that sets you up to delight the customer, it's business, it's been that way for forever.

Lloyd Metz: Can you say more about what does that mean? To organize your data and a data lake, what does that sound like? What does that look like? Can you describe it without using the word data?

James Aylward: The thing is, the 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 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 [00:29:00] rooted strategy documents now. Claude can read that or ChatGPT can read it, 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 to have that conversation. Two years ago, you'd have to have a team of data 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 cloud 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.

Sean Mooney: I'll add to that. Let's just say you have an accounting system, otherwise known as an ERP system. And you say, I want to hook my [00:30:00] accounting system up so we can do analytics using Quad, or you name the analytical program. If you just hook it up to a data lake, and this would be like Snowflake or Databricks or 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 then I need it broken up by products and I 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 they 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 [00:31:00] 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've 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 outsource provider, and this sounds like a commercial. 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 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 or product [00:32:00] 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 why we'd started it.

We rented it and then we internalized it.

Doug McCormick: 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 my own tools?

Is there a hybrid model?

Sean Mooney: 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 the Silicon Valley still has this like zero sum game mentality.

We've got to win it all. [00:33:00] And so they're all putting up moats except for, and this is, and 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.

James Aylward: Look, I think there's going to be a couple different winners and with different strategies, what I'm experiencing with Claude at the moment, as 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 don't really need to identify the data you can't let Claude have access to. On the other side of that coin, there are people who are very data security sensitive and 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 [00:34:00] 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 going to 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 win that curve and not left behind, is get really good at 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 in three days than you ever have been once you hook all that stuff up.

Sean Mooney: I think it's an excellent point. Every company needs to have a data strategy. James brings up a good point. You have to be careful about not only the data that you have, [00:35:00] 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 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 and to ChatGPT and to you name it, or Gemini, and no one uses it other than like superfluous stuff. They're probably getting 10% of the power of it.

Nathan Plummer: 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 [00:36:00] business leader, it's thinking about trying to understand and be educated on what these tools can do and are good at today and currently good at and what they're say not good at.

And so you're identifying and really thinking about the key areas of impact within the organization. I really do think for most organizations it's 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 that 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 [00:37:00] 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 them, 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, 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 AI becomes a lot more interesting and something that would actually accelerate the kind of transformation of the organization.

James Aylward: Yeah. One way to do this really badly is to hire a data scientist [00:38:00] and put that next to a business expert and expect magic to happen. At that point, it doesn't work. I've done that personally. Total disaster. What does work is when the business experts that have been there for 10, 20, 30 years, 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, they 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.

Sean Mooney: I think James is spot on that, and I'll give you like a practical example. You're going to see a thousand different tutorials. You can watch them, but it is just like mind numbing. But I think Claude does some great ones where they just make them free. You can do the [00:39:00] 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 now I'll give a shoutout. I have no connection to this person, but someone just showed up on my Instagram feed named Sabrina Romanov. A lot of her stuff, I was like, this is 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. So, which is probably like 50% chance, James was like, "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. So little shout out if you go on X.com, go to Alpha Drop 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 [00:40:00] 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, and so, but that's like a great approach to do is just learn by doing is I think where James is going.

I'd be careful about doing that, like within your own company systems though.

Lloyd Metz: 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 conversation. It's like, "You know what? I'm good with the blue pill, right? I'll just stay where I am."

But the red pill is where you want folks to go. What's the opportunity that makes them want to do that?

Sean Mooney: I'll let James do like 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 BluWave that was foundational from a group called Hunter, Hunter and Schmidt, where you do a skills interview, you do a [00:41:00] traits interview, you do a workplace simulation, and you test their traits and like cognitive processing skills. It's like the book Who, if you've read that by Geoff Smart, as well.

That process, every time we would hire to put together that pack, would take hours and hours for every new hire. So what I did on Claude is like, well, I wonder if I can turn this into a skill. And if you do the Claude one-on-one 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 and it took me the 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 answers 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 [00:42:00] 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. 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.

Lloyd Metz: No, that's a great example, Sean. Is that available [00:43:00] across your firm to use?

Sean Mooney: Yeah.

Lloyd Metz: 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.

Sean Mooney: 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 engaging in, and we call it the BluWave hiring way. And people would do it always a little differently so you get different results and so it's now you have a way of doing things in your organization.

Lloyd Metz: That's a great example. Thank you.

Sean Mooney: James, 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.

And 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 it Jarvis. How about for any of the lawyers in the room.

James Aylward: Yeah, it's an internal [00:44:00] tool. It's inspired by Jarvis, but like, we've hooked up the Snowflake data and also worked out how to bring in other data feeds that complement Snowflake,

plus work out how to add a layer of security around the cloud, 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 helpful, and they will help to get around any security stuff you put in place. So you really need to be 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. You want to know how the sales team's going, or what is the best opportunity for us tomorrow based on the data 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. We're not hitting that goal right now." You can do that by [00:45:00] 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 client say about this?

What would our new customers say about this? What would a really highly skeptical engineer think about this approach?" Jarvis 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 [00:46:00] three months ago. Like this is nuts, I think with the 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.

Sean Mooney: 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 and force rank who's quote unquote 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 [00:47:00] 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 now.

Nathan Plummer: I would add one thing from a Venture Cafe perspective is we're going through this ourselves. 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 high.

And so for most of our teams around the world, 80% of the work that's involved in that, 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 [00:48:00] 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.

Sean Mooney: 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. It's like 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 [00:49:00] 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.

Nathan Plummer: Well, thank you for the opportunity. You're 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 understands who's been there, who's there for the first time, who's returning. You can go to Venturecafeglobal.org and see our range of cities that we're currently operating in.

If you are 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 Venturecafeglobal.org. But I would also just say is many cities, all cities have support functions like a Venture Cafe, and so if you are grappling with,

"My gosh, this feels so overwhelming. Where do I even start?" The best thing to do, [00:50:00] just be out there with other people who are either wondering, asking the same question, and find a community of like-minded people who 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 where to go next.

And every city across the US, we'll 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 being able to develop a network that can guide you through this is really critical.

Sean Mooney: 100%.

Lloyd Metz: 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 [00:51:00] 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.

Nathan Plummer: 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 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 [00:52:00] was this kind of outside technology, and there, for many businesses, "We're not a digital business."

We know now there is no such thing as being a digital and being not a digital business. If you are 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 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 [00:53:00] 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.

Sean Mooney: For everyone here, whether you're motivated by loss or gain, right, and those motivate different people differently, James and Nathan's point are spot on here in that the move you got to 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.

And my reflection may be 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. [00:54:00] 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 that, those first steps going.

There's a 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?

Lloyd Metz: Absolutely. I think that's a conversation we should definitely have.

Sean Mooney: Alright. Well for lloyd and Doug, I am immensely appreciative of James and Nathan joining us here today. So thank you very much Nathan. We will include your contact information in our notes.

Nathan Plummer: Thank you very much.

Sean Mooney: And James, you can find via any BluWave outlet.

Lloyd Metz: Thanks, James. Thanks Nathan. To be continued.

James Aylward: I really appreciate it.

Nathan Plummer: Thank you everyone.

Sean Mooney: That's all we have for today. Special thanks to Best But Never Final for joining. If you'd like to learn anything about the Best But Never Final podcast, the hosts, or the guests, please see the episode notes for [00:55:00] links.

Please continue to look for the Karma School of Business podcast or Best But Never Final podcast anywhere you find your favorite podcasts. We truly appreciate your support. If you like what you hear, please follow, five-star rate, review, and share. It's a free way to support the shows, and it really helps us when you do this, so thank you in advance. In the meantime, if you want to be connected with the world's best-in-class, private equity-grade professional service providers, independent consultants, interim executives, or AI enablement resources, that are deployed and being trusted by the best business builders in the world, including many hundreds of top PE firms and thousands of their portfolio companies, and you can do the same whether or not you're in the PE world, give us a call, or visit our website at Bluwave.net. That's B-L-U-W-A-V-E, and we'll support your success.

Onward.

The views and opinions expressed in this program are those of the individuals presenting, and do not necessarily reflect the views or positions of any other persons or entities, including those referenced herein.

No representations, warranties, financial, [00:56:00] legal, tax, or other advice are made herein. Consult your advisors regarding any topics discussed during this episode.

Private equity insights for and with top business builders, including investors, operators, executives and industry thought leaders. The Karma School of Business Podcast goes behind the scenes of PE, talking about business best practices and real-time industry trends. You'll learn from leading professionals and visionary business executives who will help you take action and enhance your life, whether you’re at a PE firm, a portco or a private or public company.

BluWave Founder & CEO Sean Mooney hosts the Private Equity Karma School of Business Podcast. BluWave is the business builders’ network for private equity grade due diligence and value creation needs