Boomi World 2024 | Matt McLarty | Boomi

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Matt McLarty

CTO

Boomi

Boomi aims to put companies on solid ground for AI integration

Amid new acquisitions focused on productivity, innovation and artificial intelligence, Boomi LP is maintaining its commitment toward being a cradle and foundation for new technologies.“Rather than us going into a room trying to build everything ourselves, we can help provide a place for them to run and generate value,” said Matt McLarty (pictured), chief technology officer of Boomi, which means ‘earth.’ “We’ve always had a great platform around connecting things and integrating things, but I think here’s a place where that specialized knowledge to build those AI functions, we can really provide a fertile ground. We provide the earth. We plant the seeds. We grow the garden

play_circle_outline Boomi World 2024: Igniting Excitement and Energy at the Conference in Denver, Colorado

play_circle_outline The role of iPaaS and integration platforms in the Gen AI ecosystem.

play_circle_outline Driving Success: A Strategic Approach to TAM Expansion, Product Levers, and AI-based Quick Delivery

play_circle_outline Invitation to visit Boomi.com and highlights of Boomi World 2024 event in Denver.

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Matt McLarty | Boomi

Matt McLarty

CTO Boomi

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>> Good afternoon and welcome back to beautiful Denver, Colorado. We are midway through day two of Boomi World 2024. Very excited to be joined. My name is Savannah Peterson here with John Furrier, and we have a fabulous guest joining us this afternoon. And Matt, real quick, before we get to Matt, John, this afternoon, how are you feeling?

>> Feeling great. Got a great energy. The pace of the conference is right perfectly in line with what you want to have. A very intimate crowd here, about 1,000 plus people, a lot of customers, so you can see really people digging in. When people are having intense conversations, you know they're getting into the topic. So it's good. When you see a lot of that going on, you know it's successful.

>> You can tell that business is happening here.

>> Yeah, they're getting some business out of this. You can tell.

>> And speaking of exciting things, Matt, you've had an incredibly exciting day. Tell us a little bit about your morning.

Matt Cain

>> Well, the morning started with the keynote, so I got to do a nice cameo in Steve Lucas's keynote, where we were unveiling so much stuff. I was talking specifically about our API management acquisitions, which is extremely exciting.

>> Big news for Boomi today.

Matt Cain

>> Of course, let's face it, the big show was Coach Prime there. That was something else. So I got my picture taken with him, met with some of our best partners, analysts. It's just been a whirlwind, but it's been a fantastic event so far.

>> So as the CTO and on the product side, Boomi is in the iPaaS market integration is big part of. You starting to see the AI world spinning in the generative AI world, starting to see things get refactored reset, in some cases, net new build outs of new applications as generative as a new category because it's generating stuff. It's not like pre-programmed database. It's like a bunch of new things. Where's the connection between iPaaS, integration platforms of service, and where Gen AI will pick up? Connect the dots for us because you can leverage a lot of what you have and get net new capabilities and value extraction. Unpack that for us.

Matt Cain

>> There's so much to unpack there because I think what we're seeing is that even AI in general, what is AI? So I think we can say GenAI is maybe a little more well-defined, but companies are already at various levels of adoption of good old fashioned machine learning, and then along comes generative AI. So the reality for a lot of our customers, a lot of companies out there is they're just spread across this historical spectrum of all these technologies. And they're like, "Wait a minute, we were just trying to catch up on this, and now you give us this?" I think that for us... And we've gone through this process too, because there's lots of different ways to look at AI. There's how we use AI to drive more productivity in our platform, how we use AI for our own company and the way as other companies do it, but we really looked hard at, wait, what is our role in this whole ecosystem of GenAI? If we put ourselves in our customer's shoes, how can we help them get the most value out of this? Because there's so much noise. There's so much hype. It really is groundbreaking, mind-bending stuff. How can we add value there? And there is a huge role. I think that even in the GenAI ecosystem, which is evolving more quickly than any tech trend we've seen, if you think about what you need to do to get value out of, it's, well, you need to use your own data, because unless you're writing limericks about cats, you're going to want to ground these AI models with your data, your context. So you need to connect to the data. Okay, that sounds familiar. You need to connect to the models. And how do you connect to LLMs in that type of context? Well, it's by API, right? Okay. Connecting by APIs, that sounds familiar, right? You need to then also think about the outputs that you get from these models, how do they then get translated into action? Because it's one thing to have an output of a bunch of languages, like, "Hey, you could do this, this, or this." It'd be nice if you could just actually go and do this, this, or this. So you want to take actions. And if you're going to take actions, you typically need to connect to an application or another data source, or even call an API somewhere. So you put all these things together, and then you think about orchestrating the flow of all that, putting the data in, building the context, calling the model, taking the action. That sounds like the world that we've been living in for a while. And so this idea of GenAI orchestration, I think is going to become more prominent, but it's going to become pretty obvious that a platform like ours, this is a building block, a foundational element for what you need to do.

>> It's interesting, if you look at some of the trends is to validate that, then ask a question. I want to get to the point. A lot of the rag or the retrieval augmentation generation is popular because that's what people can get their hands on, the data. And yeah, this use case, you can write a book, write a blog post, do some marketing material. Okay, great productivity, but you're getting at something that's interesting. We've seen this movie before. You mentioned some of the highlights. Remember the old big data days, right? Ask good questions. That was the innovation. Now with GenAI, you can actually get to something quickly to get to the next thing quickly. So the progression is now accelerated on the generation side, meaning if I get more data faster soon, that might even not even be the loop. That might be the GenAI working on its health. Okay. If that happens, what has to happen to make that work from a customer standpoint, how do I get the best of the GenAI without going too over the top or getting too out front of the wave become driftwood. If you miss the wave, you lose market opportunity. So take us through the timing of that. So, yeah.

Matt Cain

>> It is a real delicate dance, because if you remember... We can remember this stuff, right? The HD, high-definition television, I think Sony an HDTV in 1990. It's like, oh, look at that. It was like 15 years before it really-

>> Took off....

Matt Cain

>> to the mainstream off because all the pieces had to line up. The reality is today, you could try and just create completely autonomous systems using GenAI technology, but we're still... Human in the loop is probably a good idea at this point, but we can at least sort of forecast where things are going, and this is where the delicate dance comes in because I think you got to be really smart about understanding where things are going, but then even smarter about charting the path to get there. So what we're telling companies, and we're doing workshops, because we kind of lived this life in Boomi where we've collected all this metadata over the years with the integration patterns, and we've used this in machine learning model. So for us, this is just the next step, but look for the things that you can kind of bet on. And one of those things is you are always going to need to connect to your data. You need to irrigate the canals into the data that you have because that's going to what's provide your specific context. So that's good work. That's high return on investment work. You are going to need those connections into the applications and user touch points that you have, and then you're going to need a lot of flexibility to deal with all the volatility you're going to have in the GenAI landscape where there's going to be . We look at all these models today and we're like, "Wow, OpenAI, ChatGPT, GPT-4 is blowing my mind," and meanwhile OpenAI is saying, "That's a joke. Wait for GPT-5." And it's going to be all these companies coming up. So it's the combination of do the heavy lifting now to get you the stuff that's long term, like connecting to your data, connecting the sources, and then build in the flexibility so that you can plug and play.

>> It's interesting. We have a team up in San Francisco right now for RSA, big security conference. The theme coming out of there, Savannah, is I just want slow down. There's more tools. And we actually did some research and proprietary research. We reported last week that the tool vendors are actually increasing, not decreasing.

>> This is actually the question I was just going to ask him about.

>> A year ago, you'd say, "Okay, let's consolidate around the platform," but yet there's more tools coming out because there's more problems. Then there's net new things, and the CrowdStrike was saying that.

>> Exactly.

>> So the question here is that you're kind of bringing up the point where the customers are trying to learn what to do. Operationally, you can only go as fast as your ops. So on data, do you put the operational bottleneck out front as the PACE car to figure out the system architecture to get you an AI? Because what you're getting at here is that, okay, APIs run 80% of the internet traffic, check. That's infrastructure, critical infrastructure. But the observability and governance changes because now you say, what are you observing, one? And two, governance has to change, because if data's freely available, you can't put the genie back in the bottle once the governance is not paid. So take us through the impact of what does the customer have to learn table stakes to set up operational systems, because I think we agree data, okay, cool, check. API, check. So what are they going to do? How does observability and governance change?

Matt Cain

>> I think a reality, and this sort of folds back to some of the acquisition news we put out there, last year, we ran a campaign talking about killing complexity, right? It was fun. It was fun. Complexity is the enemy. I always felt a little bit uncomfortable because complexity is not our enemy necessarily. Complexity can be our friend. Imagine the complex problems that we solve, right? And we live in a complex world, so embracing the complexity and learning how to sort of dance with complexity... I'm talking a lot about dancing here. I'm not sure. It must be

>> And limericks. I wonder if you dance to these limericks

Matt Cain

>> I don't know, maybe a little bit of a rhythm problem there. But I think that understanding that the heterogeneous landscape of enterprises is just always crazy and is going to be that way, and it's going to ebb and flow. Accepting that is important. So when we're providing platform to the market and providing the accompanying tooling to the market, it's done with the respect that we know the complexity of the organizations that we're dealing with. And if you take the data problem as an example, we're talking a lot this week about what we're doing in the data management space. So I think that AI and GenAI specifically presents a really new, maybe not a new problem, but really underlines this problem of contextual data. So I think there's been a lot of work, heavy lifting in organizations around consolidating the data, centralizing the data for data science purposes, which is really important work and has been, but that's in service of a particular aim. When you're working with GenAI and when you want to take action and get business value the most, you need to be really precise with the data. So the way I've been characterizing it is we've had this supply side view of the data world for a while of get it... Where is all the data, how can we get it centralized in the data lake, and so on? I think we need to take the demand side view of the data now and really think about... Because data is an interesting thing, right? This is in the book that we were talking about. The economics of data. It doesn't have any value until it's consumed and used in a particular way. Otherwise, it's just sitting there.

>> In fact, it costs you money if it's just sitting there.

Matt Cain

>> It costs you money. Is it an asset, or is it liability?

>> Yeah.

Matt Cain

>> And so if that's the case, then start with where you're going to realize value from the data, work back. And that even says maybe don't start with GenAI. Start with a business problem, work back to where GenAI might come up with a novel way of solving it. But a lot of times, as the data comes into it, you're going to need slices of data. And in fact, you might need less data, but more accurate data. We talked a little bit earlier about the density of the value in the data.

>> Faster data. Yeah.

Matt Cain

>> So I think having that demand side view is important, so that's kind of how we're looking at the world. So I would say that we have a broad view of... We have a big picture, we have a platform story, but we're very focused on getting those slices where we're going to deliver business value as quickly as possible.

>> A couple of things I kind of want to bring it back, but I'm really curious. So a lot of what we're talking about is AI readiness. Two things. One, can you define what AI readiness is in a little nugget? I think you've defined some of the steps there, but what does it really mean? And then what percentage, if you can gauge this, or even just in your own perspective, what percentage of the enterprises that you're dealing with today are actually AI ready?

Matt Cain

>> Yeah, I'd say it's a spectrum. It's not like a Boolean thing. It's not like you're either AI ready or you're not. And I think that one of the things that's probably given companies heartburn right now around AI is I just spent more than a decade digitally transforming. Was it all for... Now, is everything going to get thrown away with AI? And the industry, we don't do it ourselves any favors because everything's a silver bullet. You don't need a code anymore. We got GenAI. We'll see how that goes in a few years when people have to maintain the code. But in all seriousness, my main reassuring message is if you've been following that digital transformation path where you've been actually digitizing, organizing, creating those channels, getting your data in order, knowing where your data is, getting your processes automated, if you've been on that journey, you're going to be way more ready to take advantage of AI, because anyone could use the conversational interface, but the end goal is not AI. The end goal is getting the business value out of AI.

>> And you're only as good as the data you have too.

Matt Cain

>> Only as good as the data you have, and only as good as how quickly you can connect that data to the places that drive your business models and drive your revenue and productivity.

>> Savannah and I have been talking on theCUBE over two years, almost two years now, Savannah, around cloud, thinking about love of clouds, the horizontal scalability of the cloud. Well, okay, apply that to data. Not always intuitive to say, "Hey, let's horizontally scale our data," when it's traditionally been not scaling horizontally, mainly because of latency and risk.

Matt Cain

>> Like anything in technology, things are optimized along different dimensions. And I think that, as I was saying, I wrote a whole bit on this a while ago about the... There's benefit in consolidating and centralizing the data in particular areas where you want to experiment, you want to get serendipity, and so on. But when you are demand-driven around the data and you need the right data at the right place, the right time, it takes a different type of optimization. So that's why I think if people... As we talk about data management, we... Again, this is how complex systems work. There's layers, layers of abstraction. That's our focus really, is how do we get the right data to the right place at the right time, whether that's building context for a model, whether that's taking the appropriate action based on the outputs of a model, whether that's just straight up doing an old-fashioned automation that a company needs.

>> And a customer's environment matters too, because there's no general purpose environment. Not every customer has the same exact.

Matt Cain

>> Yeah.

>> Yeah. And so I'm kind of curious. We're talking about... I actually really like what you brought up earlier about getting comfortable with the chaos to a degree and embracing complexity as a potential lever for innovation. There's a lot of ways to look at it. You're advising and helping a lot of different types of companies navigate solutions right now in this space and navigate their AI readiness. How do you internally at Boomi tool select? Like John was mentioning, there's more and more tools coming all the time. How do you pick your winners?

Matt Cain

>> That's a great question. I think for us, we make some big bets. I think that we've got some really strong partnerships. We have a very strong partnership with AWS and very harmonious with a lot of the tooling that they provide in terms of our platform. I think though, we also... Knowing how volatile the space is, this is maybe the biggest thesis of the book, I'm putting out in the fall, is how do you make those small bets and spread them broadly so that we can experiment with all these new technology? And we see our role. Steve talked about this idea of an agent garden where we talked about the powered Fintalk agent that we're putting out that was in our keynote, but we see... For a lot of these companies that maybe have unbelievably awesome tools that are using a specific area, in general, they're going to need to be connected into these enterprise data points. They're going to need to be connected to the end points where they're going to actually take action and really make their tools and technology shine. So that's where we're trying to create more of an ecosystem so that those companies, number one, have a better route to market and a better route into the enterprise, and they're in the right context for their tools to add value. And at the same time, that helps us. Rather than us going into a room trying to build everything ourselves, we can help provide a place for them to run and generate value. I think it's a really strong place for us. We've always had a great platform around connecting things and integrating things, but I think here's a place where that specialized knowledge to build those AI functions, we can really provide a fertile ground. Hey, Boomi means earth, right? So we provide the earth, we plant the seeds, we grow the garden.

>> And make sure the high nutrient value in that data. My final question before Savannah gets hers in is around Boomi's technology innovation strategy. You're the CTO. IPaaS is still growing at 35% CAGR, compound annual growth per year. That's just in the classic old school iPaaS, integration platform of service. You got the GenAI market kicking in. Your clients have a lot of data, have a lot of workflows. That's the new IP. We've been saying that. Where does the TAM expansion go from an enablement? What technology levers are you guys going to be pulling to increase the success TAM? Because you guys like successful companies that have been very successful over time, have a great community, loyal customer base, love the product. We've seen that movie before. Salesforce, ServiceNow, to name a few. Boomi has that same kind of characters, so you got a lot of opportunity with that built-in energy. What's the product and technology levers you see to connect the TAM, increase the TAM?

Matt Cain

>> Again, I think we can speak in generics and say GenAI has all this potential everywhere, and we're just careful to not go down a rabbit hole. That's going to cost too much. Time is the precious commodity here, but I think we do have a strategy around optionality, right? I think we're getting... As is evidenced here, we have a great rhythm of, you know what? We have the ability to get quickly into market, get tight feedback loops. You look at what we've done around our AI products, even since launch in the fall, it feels like it's longer than that, but it was in the fall we launched Boomi GPT, and it was a bit of a experiment around, here's one way that you could use GenAI to... It's probably the most obvious way to help generate these processes that are the core of what our customers build. And then we went out to the customer community and got all this feedback around what they would like to see. And just creating those feedback loops and having the ability to deliver quickly, I think maybe it's not the one lever to pull, but it's having the optionality to pull a lot of levers without a long wait time, right?

>> Use product market fit.

Matt Cain

>> The slot machine doesn't need to spin for too long.

>> Yeah. Product market fit.

>> All right, last thing, I'm going to give you a very short 30 seconds to plug that book. You keep teasing that's coming out in the fall. We'll follow up with you more robustly.

Matt Cain

>> Well, when you're at Boomi World, so yeah. So I have a book called Unbundling the Enterprise with my co-author, Stephen Fishman, that's coming out on IT Revolution, it was originally October 1st. I think they pulled it forward into September something. But if you go and look up Unbundling the Enterprise, you'll find it. But in the meantime, that can wait until September where others... I would invite everyone to go to Boomi.com first and just see the-

>> Well, I love it.

Matt Cain

>> Yeah. Yeah. How is that for a spin.

>> Killing the close, Matt. Killing the close, Matt.

Matt Cain

>> Stick the landing here, yeah.

>> Yeah, that was beautiful. You can have both. You can have both. You can have a glass of champagne in your hand as you stick that landing.

Matt Cain

>> Exactly, and the caviar.

>> Or a book in your hand, I should say rather. And the caviar, yes. You're my kind of person, Matt. Thank you so much for being here.

Matt Cain

>> Okay, thanks.

>> This has been a fantastic interview.

Matt Cain

>> It's awesome.

>> John, always a pleasure. And thank all over you for tuning in from home or wherever you might be hanging out today. We're here in Denver, Colorado. It's day two, Boomi World 2024. My name's Savannah Peterson. You're watching theCUBE, the leading source for enterprise tech news.

Matt McLarty | Boomi

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>> Good afternoon and welcome back to beautiful Denver, Colorado. We are midway through day two of Boomi World 2024. Very excited to be joined. My name is Savannah Peterson here with John Furrier, and we have a fabulous guest joining us this afternoon. And Matt, real quick, before we get to Matt, John, this afternoon, how are you feeling?

>> Feeling great. Got a great energy. The pace of the conference is right perfectly in line with what you want to have. A very intimate crowd here, about 1,000 plus people, a lot of customers, so you can see really people digging in. When people are having intense conversations, you know they're getting into the topic. So it's good. When you see a lot of that going on, you know it's successful.

>> You can tell that business is happening here.

>> Yeah, they're getting some business out of this. You can tell.

>> And speaking of exciting things, Matt, you've had an incredibly exciting day. Tell us a little bit about your morning.

Matt Cain

>> Well, the morning started with the keynote, so I got to do a nice cameo in Steve Lucas's keynote, where we were unveiling so much stuff. I was talking specifically about our API management acquisitions, which is extremely exciting.

>> Big news for Boomi today.

Matt Cain

>> Of course, let's face it, the big show was Coach Prime there. That was something else. So I got my picture taken with him, met with some of our best partners, analysts. It's just been a whirlwind, but it's been a fantastic event so far.

>> So as the CTO and on the product side, Boomi is in the iPaaS market integration is big part of. You starting to see the AI world spinning in the generative AI world, starting to see things get refactored reset, in some cases, net new build outs of new applications as generative as a new category because it's generating stuff. It's not like pre-programmed database. It's like a bunch of new things. Where's the connection between iPaaS, integration platforms of service, and where Gen AI will pick up? Connect the dots for us because you can leverage a lot of what you have and get net new capabilities and value extraction. Unpack that for us.

Matt Cain

>> There's so much to unpack there because I think what we're seeing is that even AI in general, what is AI? So I think we can say GenAI is maybe a little more well-defined, but companies are already at various levels of adoption of good old fashioned machine learning, and then along comes generative AI. So the reality for a lot of our customers, a lot of companies out there is they're just spread across this historical spectrum of all these technologies. And they're like, "Wait a minute, we were just trying to catch up on this, and now you give us this?" I think that for us... And we've gone through this process too, because there's lots of different ways to look at AI. There's how we use AI to drive more productivity in our platform, how we use AI for our own company and the way as other companies do it, but we really looked hard at, wait, what is our role in this whole ecosystem of GenAI? If we put ourselves in our customer's shoes, how can we help them get the most value out of this? Because there's so much noise. There's so much hype. It really is groundbreaking, mind-bending stuff. How can we add value there? And there is a huge role. I think that even in the GenAI ecosystem, which is evolving more quickly than any tech trend we've seen, if you think about what you need to do to get value out of, it's, well, you need to use your own data, because unless you're writing limericks about cats, you're going to want to ground these AI models with your data, your context. So you need to connect to the data. Okay, that sounds familiar. You need to connect to the models. And how do you connect to LLMs in that type of context? Well, it's by API, right? Okay. Connecting by APIs, that sounds familiar, right? You need to then also think about the outputs that you get from these models, how do they then get translated into action? Because it's one thing to have an output of a bunch of languages, like, "Hey, you could do this, this, or this." It'd be nice if you could just actually go and do this, this, or this. So you want to take actions. And if you're going to take actions, you typically need to connect to an application or another data source, or even call an API somewhere. So you put all these things together, and then you think about orchestrating the flow of all that, putting the data in, building the context, calling the model, taking the action. That sounds like the world that we've been living in for a while. And so this idea of GenAI orchestration, I think is going to become more prominent, but it's going to become pretty obvious that a platform like ours, this is a building block, a foundational element for what you need to do.

>> It's interesting, if you look at some of the trends is to validate that, then ask a question. I want to get to the point. A lot of the rag or the retrieval augmentation generation is popular because that's what people can get their hands on, the data. And yeah, this use case, you can write a book, write a blog post, do some marketing material. Okay, great productivity, but you're getting at something that's interesting. We've seen this movie before. You mentioned some of the highlights. Remember the old big data days, right? Ask good questions. That was the innovation. Now with GenAI, you can actually get to something quickly to get to the next thing quickly. So the progression is now accelerated on the generation side, meaning if I get more data faster soon, that might even not even be the loop. That might be the GenAI working on its health. Okay. If that happens, what has to happen to make that work from a customer standpoint, how do I get the best of the GenAI without going too over the top or getting too out front of the wave become driftwood. If you miss the wave, you lose market opportunity. So take us through the timing of that. So, yeah.

Matt Cain

>> It is a real delicate dance, because if you remember... We can remember this stuff, right? The HD, high-definition television, I think Sony an HDTV in 1990. It's like, oh, look at that. It was like 15 years before it really-

>> Took off....

Matt Cain

>> to the mainstream off because all the pieces had to line up. The reality is today, you could try and just create completely autonomous systems using GenAI technology, but we're still... Human in the loop is probably a good idea at this point, but we can at least sort of forecast where things are going, and this is where the delicate dance comes in because I think you got to be really smart about understanding where things are going, but then even smarter about charting the path to get there. So what we're telling companies, and we're doing workshops, because we kind of lived this life in Boomi where we've collected all this metadata over the years with the integration patterns, and we've used this in machine learning model. So for us, this is just the next step, but look for the things that you can kind of bet on. And one of those things is you are always going to need to connect to your data. You need to irrigate the canals into the data that you have because that's going to what's provide your specific context. So that's good work. That's high return on investment work. You are going to need those connections into the applications and user touch points that you have, and then you're going to need a lot of flexibility to deal with all the volatility you're going to have in the GenAI landscape where there's going to be . We look at all these models today and we're like, "Wow, OpenAI, ChatGPT, GPT-4 is blowing my mind," and meanwhile OpenAI is saying, "That's a joke. Wait for GPT-5." And it's going to be all these companies coming up. So it's the combination of do the heavy lifting now to get you the stuff that's long term, like connecting to your data, connecting the sources, and then build in the flexibility so that you can plug and play.

>> It's interesting. We have a team up in San Francisco right now for RSA, big security conference. The theme coming out of there, Savannah, is I just want slow down. There's more tools. And we actually did some research and proprietary research. We reported last week that the tool vendors are actually increasing, not decreasing.

>> This is actually the question I was just going to ask him about.

>> A year ago, you'd say, "Okay, let's consolidate around the platform," but yet there's more tools coming out because there's more problems. Then there's net new things, and the CrowdStrike was saying that.

>> Exactly.

>> So the question here is that you're kind of bringing up the point where the customers are trying to learn what to do. Operationally, you can only go as fast as your ops. So on data, do you put the operational bottleneck out front as the PACE car to figure out the system architecture to get you an AI? Because what you're getting at here is that, okay, APIs run 80% of the internet traffic, check. That's infrastructure, critical infrastructure. But the observability and governance changes because now you say, what are you observing, one? And two, governance has to change, because if data's freely available, you can't put the genie back in the bottle once the governance is not paid. So take us through the impact of what does the customer have to learn table stakes to set up operational systems, because I think we agree data, okay, cool, check. API, check. So what are they going to do? How does observability and governance change?

Matt Cain

>> I think a reality, and this sort of folds back to some of the acquisition news we put out there, last year, we ran a campaign talking about killing complexity, right? It was fun. It was fun. Complexity is the enemy. I always felt a little bit uncomfortable because complexity is not our enemy necessarily. Complexity can be our friend. Imagine the complex problems that we solve, right? And we live in a complex world, so embracing the complexity and learning how to sort of dance with complexity... I'm talking a lot about dancing here. I'm not sure. It must be

>> And limericks. I wonder if you dance to these limericks

Matt Cain

>> I don't know, maybe a little bit of a rhythm problem there. But I think that understanding that the heterogeneous landscape of enterprises is just always crazy and is going to be that way, and it's going to ebb and flow. Accepting that is important. So when we're providing platform to the market and providing the accompanying tooling to the market, it's done with the respect that we know the complexity of the organizations that we're dealing with. And if you take the data problem as an example, we're talking a lot this week about what we're doing in the data management space. So I think that AI and GenAI specifically presents a really new, maybe not a new problem, but really underlines this problem of contextual data. So I think there's been a lot of work, heavy lifting in organizations around consolidating the data, centralizing the data for data science purposes, which is really important work and has been, but that's in service of a particular aim. When you're working with GenAI and when you want to take action and get business value the most, you need to be really precise with the data. So the way I've been characterizing it is we've had this supply side view of the data world for a while of get it... Where is all the data, how can we get it centralized in the data lake, and so on? I think we need to take the demand side view of the data now and really think about... Because data is an interesting thing, right? This is in the book that we were talking about. The economics of data. It doesn't have any value until it's consumed and used in a particular way. Otherwise, it's just sitting there.

>> In fact, it costs you money if it's just sitting there.

Matt Cain

>> It costs you money. Is it an asset, or is it liability?

>> Yeah.

Matt Cain

>> And so if that's the case, then start with where you're going to realize value from the data, work back. And that even says maybe don't start with GenAI. Start with a business problem, work back to where GenAI might come up with a novel way of solving it. But a lot of times, as the data comes into it, you're going to need slices of data. And in fact, you might need less data, but more accurate data. We talked a little bit earlier about the density of the value in the data.

>> Faster data. Yeah.

Matt Cain

>> So I think having that demand side view is important, so that's kind of how we're looking at the world. So I would say that we have a broad view of... We have a big picture, we have a platform story, but we're very focused on getting those slices where we're going to deliver business value as quickly as possible.

>> A couple of things I kind of want to bring it back, but I'm really curious. So a lot of what we're talking about is AI readiness. Two things. One, can you define what AI readiness is in a little nugget? I think you've defined some of the steps there, but what does it really mean? And then what percentage, if you can gauge this, or even just in your own perspective, what percentage of the enterprises that you're dealing with today are actually AI ready?

Matt Cain

>> Yeah, I'd say it's a spectrum. It's not like a Boolean thing. It's not like you're either AI ready or you're not. And I think that one of the things that's probably given companies heartburn right now around AI is I just spent more than a decade digitally transforming. Was it all for... Now, is everything going to get thrown away with AI? And the industry, we don't do it ourselves any favors because everything's a silver bullet. You don't need a code anymore. We got GenAI. We'll see how that goes in a few years when people have to maintain the code. But in all seriousness, my main reassuring message is if you've been following that digital transformation path where you've been actually digitizing, organizing, creating those channels, getting your data in order, knowing where your data is, getting your processes automated, if you've been on that journey, you're going to be way more ready to take advantage of AI, because anyone could use the conversational interface, but the end goal is not AI. The end goal is getting the business value out of AI.

>> And you're only as good as the data you have too.

Matt Cain

>> Only as good as the data you have, and only as good as how quickly you can connect that data to the places that drive your business models and drive your revenue and productivity.

>> Savannah and I have been talking on theCUBE over two years, almost two years now, Savannah, around cloud, thinking about love of clouds, the horizontal scalability of the cloud. Well, okay, apply that to data. Not always intuitive to say, "Hey, let's horizontally scale our data," when it's traditionally been not scaling horizontally, mainly because of latency and risk.

Matt Cain

>> Like anything in technology, things are optimized along different dimensions. And I think that, as I was saying, I wrote a whole bit on this a while ago about the... There's benefit in consolidating and centralizing the data in particular areas where you want to experiment, you want to get serendipity, and so on. But when you are demand-driven around the data and you need the right data at the right place, the right time, it takes a different type of optimization. So that's why I think if people... As we talk about data management, we... Again, this is how complex systems work. There's layers, layers of abstraction. That's our focus really, is how do we get the right data to the right place at the right time, whether that's building context for a model, whether that's taking the appropriate action based on the outputs of a model, whether that's just straight up doing an old-fashioned automation that a company needs.

>> And a customer's environment matters too, because there's no general purpose environment. Not every customer has the same exact.

Matt Cain

>> Yeah.

>> Yeah. And so I'm kind of curious. We're talking about... I actually really like what you brought up earlier about getting comfortable with the chaos to a degree and embracing complexity as a potential lever for innovation. There's a lot of ways to look at it. You're advising and helping a lot of different types of companies navigate solutions right now in this space and navigate their AI readiness. How do you internally at Boomi tool select? Like John was mentioning, there's more and more tools coming all the time. How do you pick your winners?

Matt Cain

>> That's a great question. I think for us, we make some big bets. I think that we've got some really strong partnerships. We have a very strong partnership with AWS and very harmonious with a lot of the tooling that they provide in terms of our platform. I think though, we also... Knowing how volatile the space is, this is maybe the biggest thesis of the book, I'm putting out in the fall, is how do you make those small bets and spread them broadly so that we can experiment with all these new technology? And we see our role. Steve talked about this idea of an agent garden where we talked about the powered Fintalk agent that we're putting out that was in our keynote, but we see... For a lot of these companies that maybe have unbelievably awesome tools that are using a specific area, in general, they're going to need to be connected into these enterprise data points. They're going to need to be connected to the end points where they're going to actually take action and really make their tools and technology shine. So that's where we're trying to create more of an ecosystem so that those companies, number one, have a better route to market and a better route into the enterprise, and they're in the right context for their tools to add value. And at the same time, that helps us. Rather than us going into a room trying to build everything ourselves, we can help provide a place for them to run and generate value. I think it's a really strong place for us. We've always had a great platform around connecting things and integrating things, but I think here's a place where that specialized knowledge to build those AI functions, we can really provide a fertile ground. Hey, Boomi means earth, right? So we provide the earth, we plant the seeds, we grow the garden.

>> And make sure the high nutrient value in that data. My final question before Savannah gets hers in is around Boomi's technology innovation strategy. You're the CTO. IPaaS is still growing at 35% CAGR, compound annual growth per year. That's just in the classic old school iPaaS, integration platform of service. You got the GenAI market kicking in. Your clients have a lot of data, have a lot of workflows. That's the new IP. We've been saying that. Where does the TAM expansion go from an enablement? What technology levers are you guys going to be pulling to increase the success TAM? Because you guys like successful companies that have been very successful over time, have a great community, loyal customer base, love the product. We've seen that movie before. Salesforce, ServiceNow, to name a few. Boomi has that same kind of characters, so you got a lot of opportunity with that built-in energy. What's the product and technology levers you see to connect the TAM, increase the TAM?

Matt Cain

>> Again, I think we can speak in generics and say GenAI has all this potential everywhere, and we're just careful to not go down a rabbit hole. That's going to cost too much. Time is the precious commodity here, but I think we do have a strategy around optionality, right? I think we're getting... As is evidenced here, we have a great rhythm of, you know what? We have the ability to get quickly into market, get tight feedback loops. You look at what we've done around our AI products, even since launch in the fall, it feels like it's longer than that, but it was in the fall we launched Boomi GPT, and it was a bit of a experiment around, here's one way that you could use GenAI to... It's probably the most obvious way to help generate these processes that are the core of what our customers build. And then we went out to the customer community and got all this feedback around what they would like to see. And just creating those feedback loops and having the ability to deliver quickly, I think maybe it's not the one lever to pull, but it's having the optionality to pull a lot of levers without a long wait time, right?

>> Use product market fit.

Matt Cain

>> The slot machine doesn't need to spin for too long.

>> Yeah. Product market fit.

>> All right, last thing, I'm going to give you a very short 30 seconds to plug that book. You keep teasing that's coming out in the fall. We'll follow up with you more robustly.

Matt Cain

>> Well, when you're at Boomi World, so yeah. So I have a book called Unbundling the Enterprise with my co-author, Stephen Fishman, that's coming out on IT Revolution, it was originally October 1st. I think they pulled it forward into September something. But if you go and look up Unbundling the Enterprise, you'll find it. But in the meantime, that can wait until September where others... I would invite everyone to go to Boomi.com first and just see the-

>> Well, I love it.

Matt Cain

>> Yeah. Yeah. How is that for a spin.

>> Killing the close, Matt. Killing the close, Matt.

Matt Cain

>> Stick the landing here, yeah.

>> Yeah, that was beautiful. You can have both. You can have both. You can have a glass of champagne in your hand as you stick that landing.

Matt Cain

>> Exactly, and the caviar.

>> Or a book in your hand, I should say rather. And the caviar, yes. You're my kind of person, Matt. Thank you so much for being here.

Matt Cain

>> Okay, thanks.

>> This has been a fantastic interview.

Matt Cain

>> It's awesome.

>> John, always a pleasure. And thank all over you for tuning in from home or wherever you might be hanging out today. We're here in Denver, Colorado. It's day two, Boomi World 2024. My name's Savannah Peterson. You're watching theCUBE, the leading source for enterprise tech news.