Buy, Build, Deploy: Using M&A to Drive AI Transformation — Prathik Naidu, Long Lake

Hey everyone, my name is Prathik Naidu. Uh, I'm a founding partner at Long Lake, and today I'm excited to talk a little bit more about why acquiring businesses is a unique strategy to deploy AI into the real world. So to start out, very familiar experience to many of you. Let's say you're building an AI product, and now you're trying to sell it into the enterprise. Well, where do you start? You found a pain, you built an agent for it, and it works, but for one very narrow use case. It's a

great starting point. But now you have to actually get it into production. And that might involve dealing with security review, internal coordination, integrations with twenty-year-old systems. It's a lot of hard work, and this is typically the first wall in order to deploy AI into an enterprise. But once you get past this, now you have to get the team to use it. So you typically will iterate on the product interface, go through trainings, get feedback from users. And there's a typical question that comes

up, which is, whose job is it to actually drive up that adoption? Is it yours as the AI vendor, or is it the client that you're working with? And then let's say you actually go through all this, and now you're trying to figure out, how do I actually get revenue for the agent that I just deployed? You'll typically work through a few different questions. Do you sell tokens or seats? Maybe a bit of both. How do you deal with price sensitivity from your client? And how do you actually then measure ROI? And the bigger picture

question here is, as an AI startup deploying into the enterprise, how do you capture the value that you create from your deployment? This all might sound very familiar to many of you. It certainly is to me, and these walls of deployment reflect a pretty common challenge, which is you're building on the outside, and that makes deploying AI really hard. And importantly, it's not the model. The models, as you know, are getting incredibly good. Intelligence is, uh, exponentially increasing. So what makes

this really hard? And I wanna talk about a few different observations of why deploying AI into businesses is challenging. The first is that organizations are incredibly complex. On paper, your org chart might look something on the left. You know, you have your owner, you have teams, managers, and this is a standard team structure. But the reality is, organizations are far more nuanced. There are decisions that are entrenched in a web of team members, oftentimes folks

with decades of experience. They're interacting with the sales team, the operations team. The owner oftentimes leans on them. And so building an agent as a third-party vendor is difficult because you actually need to embed into the organization.

The second reason I wanna touch on is you can't deliver the full value of your deployment if you don't understand the nuances of how a business actually runs. So a vendor might integrate with your systems of record, like a CRM or a ticketing system, um, or even, you know, document repositories where your SOPs, contracts or, you know, old email threads live. And this is really valuable source of information, very useful. But the reality is businesses, especially in, in complex domains, run on human

judgment. It's the exceptions, the edge cases, the tacit knowledge, the, you know, quick questions that get, get asked to the expert that really matters, and that's really what governs a lot of how a company runs. And that's the operating reality. You need to work with the people doing the work to understand what great work actually looks like.

The third reason that I wanna highlight is that even if you are able to deliver end-to-end value, it's pretty hard to capture value as an AI vendor. So let's say you land a seven-figure million-dollar contract, which is pretty impressive. But how do you think about the enduring business value of that contract? Lot of big, big questions to answer. Will customers keep renewing? Can you maintain pricing as models improve and as alternatives get cheaper? But let's say, as part of that million dollar a year revenue engagement,

your client actually gets a lot of value from it. In fact, maybe five million dollars a year in EBITDA impact. And so what's interesting about this is that with an established business, you have years of operating history, enduring client relationships, and a proven business model with real economics. And so you're adding earnings to a business with value that you can already reason about. And so, you know, using a, you know, roughly conservative ten X multiple, you can actually generate for your client fifty million in enterprise

value, which is really interesting. And so there's this question here, which is, you know, as a vendor, you did all the work. You've deployed your agents, you went through all the walls of integrating with the systems, working with the team, but the largest impact actually shows up in the P&L of the business that you actually worked with.

And so these are the typical walls and challenges of deploying AI outside in. So why not just go build that firm and build that business yourself with AI at its core from day one?

This is a very valid strategy, actually. You can improve the whole workflow, not just a narrow slice. You sell the work itself, not just one specific tool, and importantly, you own the outcomes, so the P&L and the value that you're generating is yours. And many of these firms are gonna be amazing. We think this is a great strategy. But I also wanna call out some of the challenges that come up with this approach.

The first is you don't start with any customers. It's hard to get distribution. You don't have any minimal-- Uh, you don't have any, uh, brand reputation, so that makes it challenging to, to grow. And it's hard to hire expertise, especially in, you know, niche, niche industries and important industries where clients might need multiple specialty offerings. So take an example here. If you are the leader of a health system and you're planning a new surgery building, do you hand that project to an AI architecture startup or the firm that

you've been working with for twenty years who knows the standards, the nuances of how you actually design your buildings, and you can trust them to deliver the full scope of your project? It's a very different kind of execution model. And so the important insight here is that the best firms have spent decades earning their customers, building brand and trust, and building an incredible team to deliver great service to their clients.

So instead of being outside in and selling an AI agent to a company or trying to build from scratch, what if you could partner far more closely with incredible businesses that already exist through acquisition? And I wanna talk a little bit more about why ownership is such an interesting strategy here. Starting with on the engineering side, which is access, the ability to have your team sit down with the people who do work, understand what they do, build those relationships,

understand the key people, the veterans, the folks that have the twenty years of experience is super important to really designing agents that not just solve one workflow or one use case, but really tackle the full automation and service delivery opportunity for the entire business.

The second is data. Every business you can think of as really a live environment with real tasks, real edge cases, and real answers on whether or not an agent actually got something right versus not. And this is a huge opportunity to use that information, use that signal to actually improve your agents and models.

But importantly, on the operational side, incentive alignment is huge. Ownership solves for this. You only win when your team and your business wins, and there's no separate revenue goals, no distractions from other clients. It creates an incredible amount of alignment that enables you to uncover and solve a lot of the blockers to ultimately get to faster deployment and faster shared goals. Outcomes really matter here.

The other piece is trust, which we talked about. And, you know, importantly, rather than coming outside in or building from scratch, ownership has that trust element baked in from the start. You know, you have the brand that took decades to build with all the customer relationships, and it naturally plays into this idea of scale. Starting with a large base where you have those customers, you have an expert team that can deliver an incredible service, and the cash flow to continue to invest and compound in growth and new products is very powerful. And the important piece here is

these five characteristics aren't just independent benefits. They actually create something far more powerful, which is a compounding advantage where a combination of your agents and your people continue to act in live operations. You can understand how work is done, create real-world data that leads to real outcomes, and that learning all compounds together, and this flywheel is really interesting, and it comes with all the elements of ownership to, uh, you know, power this flywheel from, from day

one. And so what's interesting here is that ownership, uh, can compound rapidly over time.

So we talked about why ownership is, is really interesting, but what markets are ripe for acquisition, especially in the AI era? I wanna start with the software economy. This is a global software market. Most of us have spent a lot of our time and careers building in this part, and, you know, it's a pretty big market. But what's interesting is that software has always sold tools to people doing the work. If you zoom out a little bit, this is the services economy. It's a seven trillion-- seventy trillion global market. And the important insight here is that

owning the outcomes of a service end to end is far more valuable, and that's what's exciting to us. It's a huge m-market opportunity. But there's an important observation here, which is, under the hood, there's a challenge that services businesses typically face.

Labor costs typically grow with revenue. So, uh, for example, a community manager at an HOA management company can only run so many associations. An architect can only design so many hospitals or schools. So if you want more clients, you have to hire more people. And this is the typical challenge of, uh, of a business. And, you know, if you compare it to a software company, if you add a hundred dollars of revenue in a typical software business, you keep about eighty. So there's really low marginal cost of growth, which is why these businesses are

incredible and you can scale very rapidly.

On the contrary, a service business operates very differently. For every hundred dollars of incremental revenue, you typically have around eighty dollars or so of delivery and overhead. You have to hire, train, manage your team. And so many founders who have built incredible services businesses have really stopped chasing growth, not because they lack ambition and not because there's no market opportunity, these are massive markets, but because the challenges of scaling when you have to do a lot of the work for every hundred dollars, you're only keeping about twenty. And so you can

think of this as a pretty huge, you know, eighty percent marginal tax on growth, which makes it very challenging to both come outside in, start these businesses, and even scale them.

What's very interesting here is that AI actually reduces the cost to serve while also improving the quality of your service. So look back at our chart here where revenue scales, uh, proportionally to labor costs. But when you deploy AI, you can see here that you get actually operating leverage. So your labor cost no longer directly scales with AI. You're still gonna need to hire an incredible team of experts to deliver your service to your clients, but the service economics for these businesses start to look a lot more like software.

And so the thesis of acquisition, deep partnership with incredible companies, and leveraging AI to scale these businesses and improve the quality of service is exactly what we're excited about here at Long Lake. So just quickly, a little bit more about us. Long Lake is an applied AI holding company. We, uh, partner with incredible businesses, have raised seven and a half billion in capital from Elad Gil, General Catalyst, Alphawave, D1, and many others. We were started about three years ago. Uh, and our whole thesis is to

acquire best-in-class services businesses, uh, partner with them to transform their operations, help them grow faster, and ultimately deliver a better client experience. We've, to date, partnered with forty businesses across a number of markets, residential services, architecture, uh, HR, and, uh, many more. We're an amazing team of fifty-five, uh, team members across technology, finance, and operations. We're growing. Uh, we have a, an eclectic mix of former founders, folks from the military, Palantir, a lot of familiar names

here. And we recently just announced our partnership with Amex GBT. It's a six point three billion take private, uh, bringing us into the corporate travel space, which we're incredibly excited about. You know, we're modeled after a lot of the holding companies that have come before us, but with a really interesting observation that AI raises the ceiling for business transformation. And this is the lever of excellence that we're excited to go and pursue.

So we talked about why ownership, we talked about services in particular. What services businesses, though, are actually attractive to own? And I want to start with, uh, an important observation, which is, as models continue to get better, the price of intelligence is rapidly declining, and it's pretty incredible to see. I think this is only going to continue. So if intelligence is getting cheap, what remains expensive? We really focus on mission-critical businesses, and it starts with a few key questions that we typically

evaluate these companies on. The first is, is there a customer base that trusts these businesses with real end-to-end outcomes? The second is, is there human judgment in the service that's actually being provided? And the third is that, are there, you know, regulatory reasons that govern accountability when making these acquisition decisions? And I think the important piece here is you have to own the outcome, and that's the typical characteristic of a lot of these services businesses. So to color this a bit more,

it's not just about drafting a newsletter to a homeowner, but actually handling the end-to-end outcome of an urgent leak. It's not just about extracting data from documents, but actually taking accountability for a complicated tax decision. And, you know, it's not just about helping you book a flight, but handling the last minute disruptions, which I'm sure many of you have faced before.

Businesses like these are rare. You have to fit all of these questions, uh, and many more, and also have a team that's excited about that partnership. And to do this, you know, you have to see a lot of businesses. Great investment decisions come with choice, so you have to keep a big funnel and then an incredibly tight filter. Our focus is on businesses that have incredible fundamentals from a business and market standpoint, but also have an awesome AI opportunity where we can actually go in and partner very

closely with these businesses. We see about five thousand, you know, deals a year, but again, are very focused and diligent on what we actually focus on and who we partner with.

Awesome. So once you've found your business and you've partnered with a company, what does P&L transformation actually look like? And I think a very simple way to explain this is actually through the visual of a J curve. So you start with your investment in AI deployment. You have your platform, you build your tech, capture the workflows, commit a lot of team time into making this work. And it's a lot of hard work. You have to make it work within the business. Thinking back to, you know, some of the challenges of building outside in, you have to partner very closely with their

team, iterate, validate your product, potentially train new models, look at all the different unique workflows and data that exist within that business. But once you make it up this hill, you'll start to see folks actually using your product, which is incredibly exciting, and you start to actually unlock a lot of capacity. Time savings, usage is going through the roof. They're producing the same output, fewer hours. But this isn't the s-- the end of the journey. Importantly, we want to actually capture value, and we want to generate real

business impact with our partner companies. And so you actually have to put that capacity to work. Think about incentive alignment, working very closely with our management teams and trusted partners to drive business outcomes, help with growth, improve customer and employee NPS. These are all part of the equation to actually driving impact within these businesses. And once you do all the th-those things, you get to real P&L impact. Higher revenue per employee, but importantly, a far better service quality for our clients. And I think what's cool about this is that this isn't just a

bespoke playbook that you run one-off for every single business. If you have the platform and the playbook to do both the AI deployment and the business transformation, this can rapidly compound. You s-- you get to see faster time to deploy agents and a faster time to P&L impact for all the new businesses that you partner with. And we see this at Long Lake across the forty-plus businesses that we've already partnered with to date.

Of course, it's not just about AI and business, uh, transformation. Under the hood, it takes a pretty unique team to actually own, operate, and, and transform. So you have to actually go and, and buy these businesses, have an M&A team that can partner with world-class services businesses, work with them very closely to build that relationship, build that excitement for the future. Then you actually have to go to do the work of building, and this is a lot of hard work. You have to fly out to Minneapolis, fly out to Portland, uh, go to London, spend time with the team, and actually build and push the frontier of how agents get deployed. Again, not

just for one narrow use case, but really for the entire operation. And then to actually do that deployment, you know, you need an amazing deployment and ops team to actually drive adoption, right? Think about that at-- that J curve, right? Don't just stop at capacity and time savings. How do you actually ensure that the work you're doing is driving real business impact for the, the companies that you work with and delivering a far better client experience, uh, for the service that you're providing?

So if you're excited about owning outcomes and partnering with, uh, incredible services businesses, feel free to reach out. Uh, we're excited about this thesis. We think there's a lot of opportunity to go continue to build in the services economy and, uh, feel free to chat more if there's any questions. Thank you.