Insight Enterprises, Inc. (NSIT) Earnings Call Transcript & Summary

Welcome, and thank you for standing by. I would like to inform all participants that this call is being recorded. Parts of this call may also be reproduced in JPMorgan Research. If you have any objections, you may disconnect at this time. I would now like to turn the call over to James Morgado. James, please go ahead.

Thanks, Sam. Welcome, everyone, and thank you for joining our AI Tech Talk with Insight Enterprises and JPMorgan. I'm James Morgado, Senior Vice President of Finance and CFO of Insight North America. Leading the discussion today will be Matt Jackson, Insight's Global CTO; and Joseph Cardoso, Vice President, Equity Research at JPMorgan. Before I turn the call over to Joe, let me get some of the legal stuff out of the way first. So as a reminder, all forward-looking statements that are made during this conference call are subject to risks and uncertainties that could cause our actual results to differ materially. These risks are discussed in greater detail in our most recently filed periodic reports with the SEC. All forward-looking statements are made as of the date of this call, and except as required by law, we undertake no obligation to update any forward-looking statements made on this call, whether as a result of new information, future events or otherwise. Now with that out of the way, I'll turn the call over to Joe. Joe?

Thanks, James, and good morning, everyone. So today, we have Matt Jackson, Insight Enterprises Global CTO and SVP of Solutions to discuss AI applications as a follow-up to our Friday's tech talk on AI infrastructure. Before we get started, I also have a couple of housekeeping items. First, I just want to point you to JPMorgan disclosures that Sam, our operator just placed in the chat room or chat function on Zoom or separately, you can find that on jpmm.com/research/disclosures. Please when you have the opportunity, I would encourage you to view it. Second, if you have any questions you'd like to ask Matt, please feel free to use the Q&A function in Zoom today. I will be more than happy to read them on your behalf and get them answered for you. So with that, let's get started. Matt, first off, thank you for taking the time today. We had a great conversation or discussion with Juan on Friday and looking forward to having one today.

Yes, looking forward to it. Thank you so much for having me.

Yes, of course. I'm sure folks are very familiar with Insight , but maybe you can just start off by giving us a quick background on yourself and your experiences just to kind of set the stage here.

Yes, absolutely. Happy to. So, to start with my career hands on keyboard software developer, working with enterprises to modernize applications. This is back in the '90s and 2000s, kind of during the big tech bubble then. And have really evolved my career by helping to both build solutions for clients, but also building companies and organizations and teams started the company in 2010, called BlueMetal, which was acquired by Insight in 2015. And since that point, really been helping Insight transform from the inside, building out our services capabilities, really launching advanced services like [ Atmod ], data and AI, cloud, and then this year took over as Global CTO. So setting the strategy for our portfolio and our offerings, basically what we're doing for our clients and trying to make sure that Insight stays ahead of new and innovative technologies.

That definitely sounds like an interesting journey in -- so obviously, it's even more interesting now, right? We -- especially going to this year. Obviously, you probably have had you're head in it a little bit earlier than we have. But AI obviously has been a hot topic this year, particularly from an investor standpoint, right? It's obviously getting a lot of attention from the investment community over recent months. So maybe you can just start off by outlining what's the key difference between traditional AI and generative AI? Because I -- as of -- from our perspective, obviously, the generative AI is the new and hot topic, but maybe we can just level set and just kind of parse out the details between the two.

Yes, absolutely. And AI is not new. So I think you go back to some of the earlier days of computing, really in the 60s is when the foundation of AI was established or at least the theory. And it's evolved continuously ever since. So you talk about traditional AI, and we think about things like machine learning, which is really training models based on large data sets to do fairly straightforward predictions to deep learning, and that's when you start to introduce neural networks to do more predictive analytics, and you can start to get larger data sets involved. So we've been using these for years to help companies do predictions around financial markets, risk around insurance, customer buying patterns, supply chain issues. So AI has been core to what we've been doing for a number of years. The difference, though, between all of those technologies and generative AI is really the scope of what is being influenced. So if you look at machine learning or even deep learning, they're typically trained against a single use case. It could be a complex use case. Like I said, it might be trying to -- we did a large project for a health care company trying to estimate the length of stay that somebody would be in the hospital. So we could get better activities around treatment plans. Very complex, huge data sets. But ultimately, it was a very narrow scope. We're asking it to solve one problem, giving it one use case with a data set and an expected outcome that we're looking for. Where generative AI really kind of broke the mold is that it can handle a wide variety of use cases. Now it's still mostly use cases. The generative is an important part of the naming here because I think people think it can do everything that it's all of a sudden artificial general intelligence, and I'm happy to talk about the differences there. But what I really try to inform clients and people that are interested in this is it can handle a wide variety of use cases where you're generating content. It could be text, like e-mails, I'm sure we're all using it to help ChatGPT or whatever to help us write things. Images, there's a ton of stuff out there to help create AI-based images. But where we're seeing a ton of productivities around software development and code or even test data. And so any of these scenarios where you're looking to generate content whatever -- almost whatever form you're looking for, it can handle a wide variety of these use cases. And so I think that's the fundamental difference is both the breadth of what it can do, not a single, but many use cases. As well as its ability to really generate content that is -- that mimics what humans would generate. It's really the closest thing we have seen to mimicking human behavior and human creativity.

That's interesting. And so obviously, I wouldn't be surprised to know the answer to this question before I ask it. But it seems like that would be just incremental, like you just see incremental interest from customers. But I guess like what are you hearing from customers today around their interest in leveraging generative AI? And are you actually seeing customers using it today? And if so, what for, like can you provide us some examples of where you're seeing customers deploy it?

Yes. Yes, it's interesting. The demand is incredible. I have -- I don't -- I actually had -- in the last month, I've had one client meeting where we didn't talk about generative AI, and we all came out of the meeting and were like, wow, that was the first in a while. It was actually an advanced networking project. It was really interesting, but we just didn't happen to touch on generative AI. But in the last month, I was keeping count of the number of clients I've spoken to, either in kind of large group settings or one-on-one. And it's -- once it got over 100 clients in a couple of weeks, I kind of lost count. And so we've gotten feedback and questions from a tremendous variety of clients from the largest enterprises to small and medium-sized businesses. And wherever we see one of these kind of revolutionary technologies hit the market. There's usually this kind of there's the early adopters and the people that want to wait and see. People want to do an ROI analysis and find out what the impacts going to be. But the #1 question I get from virtually every client with generative AI is how quickly can I get it? Not how much is it going to cost, not what's really the ROI? What's the use case? It's just how quickly can I get an environment set up where my users can start to leverage, especially the chat capabilities, the generative text capabilities in a secure and private way. I think that's the key at the end there. Is that anybody can go and sign up for whether it's Bing or whether it's the open AI version of ChatGPT or [ borrowed ] or you name your public generative AI capability out there. But most of the companies and most of the clients that I'm talking to, they want to leverage that but in a private secure environment. And they know their users are using it. They know that it's -- they can't prevent this from happening even if they come out with the best policy in the world. So they want to find out how quickly can they stand something up internally that they can give to their users to kind of take them away from the public sphere and get them working internally. And I can talk about all the different use cases that we've seen, but I think what's most interesting is just that they are so excited about the productivity gains that they're going to get from these capabilities. And there's further use cases that we're exploring. But right out of the gate, I think it's just people have seen an immediate impact. I think because it hit the consumer kind of spear so quickly and just explode it was what the -- fastest to 100 million users of any technology ever. And so people just want to get that productivity gain and get that capability in-house as quickly as possible. And from there, we start to explore other capabilities. But yes, we've already started to deploy this. We've had it internally for I don't know how long weeks, if not a couple of months now. And we've got an accelerator to get our clients using it. And so we've already started to deploy it with clients. We're using it, but we've also started to talk about more advanced use cases, which get really interesting.

So maybe the other side of the coin there is what we're hearing is concerns around generative AIs, right? Particularly customers viewing it as maybe a disruptive -- disruption to their business. I guess, have you seen any customers express that concern and like who do you think from your perspective is more at risk around generative AI disrupting their business practices? And I guess who's better positioned to benefit from generative AI, at least from your perspective?

Yes. There's like 3 questions there. So I'll break it apart. I think the first one, let's just start with the immediate risk to almost anybody that uses this technology. So we've done polling with hundreds of clients and gotten some really good feedback. We're going to launch it in a few weeks. But what we found was the #2 concern,#2, that clients had was security, Right? Data privacy. And we thought that would be far and away #1. But it was actually #2 in the polling we did. And it's a very -- I mean when I say#2, it was a close second. So it's a big concern. And that's, I think, why you see so many people that want to stand up these private instances either on private infrastructure, as you probably talked to Juan about or even within one of the hyperscalers. But ultimately in a private tenant where it's their data and they can control that. So privacy and data security is really important. But the #1 risk that people had was really quality of work and the impact to human creativity. So what they're afraid of is that as people start to use this technology more, they're going to become overly dependent on it, and it will lead to a degradation in the quality of the work. I mean, you've seen some of the articles that have been written by this stuff. It does mimic a human, but is it really as good as the best human, right? And so both in terms of the output and what that might do to the quality of either the customer experience or the employee experience, or over time, one of the biggest risks is just that people become overly dependent on it, and it stifles creativity and innovation. And so that was surprising. I actually found that it was good that people were thinking about that. I think that was the right focus. Yes, security and data privacy. That's like a check box you have to check because it's a huge risk if anything happens there. But I was impressed with kind of the thoughtfulness that people were applying to the broader topic of what does this do to humans and how we think and how we work together. And that was the #1 concern. So I think that to answer part 1 of your question, really, any organization should be focused on those areas. How do you introduce this functionality without stifling innovation or creativity. How do you do it in a secure way. The second question though is really who is going to potentially be disrupted by this? And I think that's where you look at industries where there is generation of content, right? I know within our organization, one of the biggest questions is if this makes developers 50% to 100% more productive. What does that do to our business model, developing solutions for clients. In our case, we came to the conclusion that there is such a backlog of app modernization and enterprise app dev that has to happen. That this actually improves our value prop. We can do things faster for clients, work through their backlogs faster. I doubt we're going to run out of applications that need to be built or modernized. But I think in other industries where you maybe have seen, it could be creation of graphics, marketing content, editorial content, even over time, what this could do for animation and movies, right? You can easily imagine a world where the Netflix is just automatically generating cartoons for your kids. And there's no humans behind it. And so I think that there's the real concern that within certain sectors that rely heavily on content generation that this could be a big disruptor. The last part of the question, right? Who's going to win? One of the debates that we had early on, is this a sustaining innovation? Is this something that's going to benefit the existing big players in the space today like the hyperscalers, the chip manufacturers, people that are already in the AI space? Or is this a disruptive technology that's going to create the next set of unicorns that pop up out of nowhere, that disrupt industries, long-standing industries. And the conclusion we came to is it's a little bit of both. I think you've already seen that the level of investment that's necessary to develop this technology to build these foundation models and these LLMs and come up with this technology, it's massive. It requires lots of people spending lots of time, lots of compute, right, and actually a lot of lawyers, too. Because as you see this stuff rolled out, especially when we look at what's happening in the EU or even Canada, and I'm sure it will hit the U.S. eventually. The dialogue is there, but they haven't actually started the legislation. You're going to need -- if you're rolling out some of this technology, you're going to need teams of lawyers working with local governments and regulators. And so I think that in that sense, the people that build the core infrastructure here, the Microsofts, the Googles, the Amazons, the Nvidias, the AMTs, it's going to benefit them. Even the OEMs who are going to build private infrastructure on top of this stuff. That's going to be a sustaining innovation that concentrates a lot of the benefits to those hosters and the people that can kind of get to market quickly and sustain the investments that are necessary. But I think it's going to create an ecosystem where startups can build on top of that. Just like you saw with the Internet and actually cloud is a great example. Where would some of these large SaaS providers be without cloud, right? If they didn't have an environment to build their product on. And so that's where I think you're going to see the disruption. It's not in the major kind of technology providers that exist within private and public infrastructure. But you're going to see it in the start-up. So building AI-enabled applications that displace traditional applications, especially in vertically aligned applications, you might see a manufacturer. Somebody that's been in the space, building manufacturing software, retail software or health care software for years. And then if somebody comes in with an AI-enabled version of that and displaces them. So I think that's where you have to watch out. If you're a kind of a legacy ISV, especially if you have kind of a niche that you're in, you're probably ripe for displacement. And those are actually the companies that we've been talking to and they're saying, hey, how quickly can we enable our applications to leverage this technology, right, so that we can build better customer experiences, more efficient workflows, better designs if we're in R&D so that we don't get displaced by the next ISV that builds on top of this technology.

Sounds super interesting. Very, very interesting to see the #1 risk.

Yes. I was -- I mean, I found out like it was good to see because I feel like people are really thinking about the human impact here.

It's a bit SciFi movie-esque.

Oh yes yes. No, Nobody was talking about when we reach artificial -- well, people are talking about artificial general intelligence, this is the first step there. And so to go back to kind of the question about how generative AI different than some of the other technologies. It's not artificial general intelligence. But because of the breadth of the use cases, the thing it's missing is the ability to set its own goals to ask its own questions. When that happens, and it will, then we're really -- we've dipped our toe in the artificial general intelligence. And that's where we get into SciFi stuff. We're talking a few years of that stuff.

Looking forward to having you back then.

Just the bot version of me.

So maybe we can go on that -- to follow up on that and just talk about like what's in the market today? Like what are you guys seeing? Like who are the players, who are the -- what are customers leveraging in terms of these generative AI offerings today? And like what are they specifically addressing? And like maybe you can even touch on how you see them maybe evolving, not necessarily longer term, but maybe just in the medium term, like what does it look like today? And what will it look like in a year from now in terms of what you're seeing from these cloud service providers or what have you?

Yes. Yes, it's interesting. I think that the most obvious thing that we're seeing in the market today is the productivity tools, right? So whether that's just the chat capabilities and whether it's the opening of public ones or if it's now that some of the hyperscalers are starting to roll out their own versions of those, whether they're public through things like their search engines or private within the enterprise. That's the first thing to hit the ground. People are already using it. It's interesting. We surveyed our clients again and over 70% of them had already come up with policies around the use of generative AI. And basically, the other 30% we're working on policies are intended to create them. And so everybody sees this being part of the workplace in the future. So whether you're Microsoft and the copilot tools, those are going to roll out incrementally. Not everybody can get access to those right away because Microsoft got to build the infrastructure for this tough to scale. Google Workspace, another great example where they have announced a lot of these generative AI capabilities just woven into the productivity suites and stack. So if you're writing an e-mail or a Word document or a PowerPoint presentation, whatever it is, having these general AI capabilities. I think that's the first thing you're going to see, right? And that's almost immediate. Like I said, it's as soon as people can get access to it, they're going to start using it. And so as fast as the hyperscalers can roll this stuff out, I think in this space, Microsoft will be the dominant, Google will be #2. And they address kind of different markets for productivity with Office and workspace, but they're going to be the dominant players in that space. Not that there won't be others. They'll be CRM tools, Salesforce will come up with their stuff, plenty of productivity games that you're going to see across the whole ecosystem. But I think what gets interesting is the next wave, where we start to see AI-enabled applications. So this is when companies start to leverage these foundation models, maybe build their own or tune their own foundation models or large language models. When they start to pull in their corporate data. So one of the things that we've done is we've got our Insight GPT setup, but we've started to pull in private data sources through things like cognitive search. So when somebody goes in there, if I go in and I go to our private chat environment, I can say, hey, pull together some case studies that include this that and the other thing. And it will actually go out there and pull together those case studies. So they'll look for our private data and pull it all together in a very secure private environment. And so you're going to start to see people layer in their own data, layer in their own models. And then ultimately, I think you're going to see applications get launched. So these could be new SaaS-based applications. They could be industry-specific applications. They could be internal enterprise applications, that leverage this stuff natively. And I think that's where you're going to see maybe not the -- I mean the productivity gains you're going to see through even just office is going to be tremendous. But I think you're going to see some real game-changing applications get launched in the next 3 to 5 years. I think about what we do within health care and the ability to predict medical imagery, right? To help diagnose cases that maybe you couldn't do before or manufacturing and the ability to come up with new product designs that people wouldn't have even imagined because it's impossible to understand the whole set of data that you might need to interpret to come up with a new design. And so I think as compute gets better, obviously, NVIDIA has come out strong at the beginning here, others as well around GPUs. As that compute gets loaded into the hyperscalers, and you can run these more advanced models and train them and embed them in applications. You're just going to see a whole new use cases pop up that we couldn't even imagine before. So I think that's kind of how it will roll out. And like I said, I think the hyperscalers have a great play there. I talked about Microsoft and Google, but all 3 of them have great API-level services for this stuff. So Amazon as well is coming out with almost like a marketplace where you can buy foundation models from stability AI or other providers. So they're basically creating this ability to kind of ingest other foundation models and Google has a whole library of foundation models that they've created that you can plug into. So if you're a manufacturer, if you want to evaluate supply chain issues. They've got models that are already tuned for that type of stuff. And then obviously, Microsoft with the open AI investment, probably hit the market first and has the strongest offering out of the gate. And so they're going to be the winners, but I think that what you'll find is everybody will benefit from the productivity stuff, but the ones that really invest in embedding this in their core applications are going to benefit the most.

So you brought up an interesting point at the end of the day. It's like the delivery rate. You mentioned Amazon Marketplace in terms of them trying to deliver that. That kind of leads into my next question, and it's how important is the channel during this kind of technology cycle, right? And so I guess, one, like how do you see insights roll or maybe even broader the channel in terms of dispersing this to a broader ecosystem. You talked about having all these meetings where everyone's bringing it up to you. And I think what's probably even more interesting is if you can like look back at prior technology cycles and maybe there's not one that's an apples-to-apples example. I guess the easiest or low-hanging fruit would be cloud generally, right? But maybe you can just talk to, has there been an incremental focus on the channel being able to deliver this? Because it seems like that's where the -- maybe the air pocket is as everyone's hearing about this, but they don't know how they can leverage it in their business today. So do you see kind of incremental weight pushed on of the insights of the world in terms of bringing this out to customers and kind of showing them what the capabilities are and how they can leverage it.

Yes, absolutely. Like I said, we've had more conversations on this topic than any other disruptive technology that I can remember in my career. So whether you talk mobile or cloud or -- I came in a little after the Internet, but even if you go back to that kind of the '90s or even earlier. So I think when we look at this, there's a lot of -- it's not just the technology implementation. Like there's varieties of complexity, right? If you just want to stand up your own private chat instance, that's one thing. Eventually, Microsoft through O365 is just going to enable the ability to upgrade to get copilot capabilities. Our teams are already leveraging GitHub CoPilot to help right code today, right, and seeing the productivity improvements. So I think there are some things that are low-hanging fruit. But what you have to remember is that this is not just about deploying technology, right? It's about understanding the impact to your organization. And I think that's where we, at Insight, we're obviously in the channel. But the investments that we've made over the years in real services capabilities, both technology services as well as things like product management, organizational change management. This is disruptive to companies. When you introduce this technology, it raises questions about what level of employment do you need? Do you need X number of people in these different offices anymore? Or is it just a productivity improvement. Is this going to power exponential growth within your organization because your existing team is so much more productive. Those are not things you want to find out accidentally, right? Those are things that you want to be thoughtful about that you want to map out a road map to introduce this technology. You want to understand how it's going to change workflows within the organization. How it's going to change the way that people use technology, right? Your enterprise systems, your third-party systems that you've stood up. And so I think there's obviously a channel play there in helping -- if the value there is really helping clients understand how they can get access to this technology, who they should go with, how they should deploy it. We're a critical component to that thought process and that kind of decisioning process. But I also think that there's this -- like when we talked about risks, there's a human impact discussion that needs to occur. There's a security data privacy discussion that needs to occur. So I think the channel is critical, and in particular, where we can deliver value-add services, advice and guidance to our clients as they adopt this technology, that's the real differentiator. That's what they really need. They need access. They need somebody to help them deploy it. They need somebody to help them figure out which provider to go with based on their kind of unique use case. But more than anything, they need somebody that's going to hold their hand through the process that's done it before, right? We've already done it ourselves. We've done it for clients already. And so we can talk to them about the pitfalls, what to watch out for, how to proceed, what the best practices are, even just designing a policy around this type of stuff. So we're really, I think, at the early stages of that. But like I said, it's picked up faster than anything I've seen before. You mentioned cloud, and that's -- that's probably the best analogy. I mean mobile might be everybody that had a website had to create a mobile version of it as soon as the iPhone was launched. And so you see these kind of S curves of innovation, where you have this disruptive innovative technology and then everybody rushes to adopt it and then it levels out to the next one. This is definitely one of those S curves. And so we have to be there to help our clients through every step of that journey, right? Because it's not just turning it on like a light switch. It really is thinking through what are the use cases? Where does the right technology apply to -- like I talked about earlier, it's not every use case. It works best for use cases where people are actually generating content, generating things. So identifying the technology that can support that. identifying then how the organization is going to change to adopt and get the best use out of this technology and then ultimately deploying the technology. So there's a lot of phases to get this right. I think that's where, like I said, channel and especially channels and services comes in to play.

Interesting. So I guess from -- on that note, where is Insight today relative to making Generative AI solutions available for its customers? And I guess -- the crux of my question there is the way that I kind of think the go-to-market rolls out, and you can correct me if I'm wrong, is probably you partner with customers on projects that you guys are kind of working on in tandem with. And then as soon as you kind of end up creating some type of solution, you probably look to basically bundle it and almost make it like an off of the shelf kind of offering, right? So I guess, if I'm correct in characterizing that, like where are we in that journey? Are you seeing more of the R&D/experimental phase still? Or are you guys actually having those bundled offers today because you worked on some early inning projects. And so therefore, you do have some off-the-shelf offerings today. Can you just kind of elaborate on that?

Yes. Yes. Well, I think you hit on kind of the -- I lead our portfolio team and how we launch things and bring them to market. And ultimately, I think of it in a number of different categories, but I'll focus on a few. So packaged offerings, where we have some IP. Where we've done it before. We've got a run book. We've built efficiencies around delivery. We're leveraging our offshore teams, we're leveraging that IP both to deploy as well as to run. Maybe we have managed services around it. And so we develop these packaged offerings and they can either be targeted towards the scale commercial segment where we can deploy them hundreds of thousands of times or they can be starting points for larger projects where we customize on top of them. But ultimately, that's kind of one category. And then the other category is where you start out really from an envisioning, right? There may be reusability. There may be assets there may be IP, but it might be a very unique use case for an enterprise, right? They're trying to do something for the first time that nobody has done before. So there's not going to be 100 or 1,000 examples of it. And those are more of the custom engagements where we do deep evaluation of the use case and kind of map out that journey for them. We're doing both of those right now with generative AI. So we've already built the accelerator. We've got it in some of the marketplaces today like on Azure. So we are able to deploy a generative AI environment for a client in 4 hours. Now you have to say -- you have to get permission from Microsoft. A lot of the stuff is in pilot. So there's like sometimes a 2-week wait to say yes. But once they say, yes, we can get this stuff stood up in a private tenant for a client, so they can use these chat -- generative AI chat capabilities and half of that. And so we're doing that today for clients. We're obviously leveraging it internally, and we've been working on that for months. And so that's where we saw this coming. We saw -- when it hit the consumer space, I mean, it was obvious that it was going to take off in the enterprise. And so we rushed to get a really solid framework in place that we could then deploy to multiple clients, and we were client #0. And so I feel really good about what we've got there. And that kind of answers the question that clients have of how quickly can we get that secure environment set up. But that is also the basis for these enterprise conversations where they say, that's great, but we want to pull in this data source and this data source, and we want to do prompt engineering to change the logic to fit with our custom decision banking framework or whatever they have. So it could be an insurance company that wants to model risk. It could be a banking company that wants to estimate market performance, you name it. And so that same framework that we built to be highly repeatable and deployable at scale is actually a starting point for having a broader implementation or conversation around other custom use cases. It could even be the middle part of a custom user interface on top of generative AI. And so those are the conversations that I think will take longer, and there's a development life cycle, and it could take months or quarters or years to get some of these products to market. But like I said earlier, I think the first one, the packaged offering is that quick hit productivity gain, and we're well positioned to help clients there. But the other one, I think, is going to lead to some really sustained changes within just how people work, how people interact with technology, how you interact with the companies that you work with, buy from. So I'm excited about some of those journeys that have just begun in that space.

And so when you think about the strategy around evolving Insights portfolio around all these different offerings, packaging, et cetera. Like how are you tackling that? Like how do you see it evolving as you go into the future? And then maybe like a second part of this, like who are the key partners that are dominating your share of mind as you kind of are looking to evolve your offerings?

Yes.Well, and this is where I think about what is the value proposition that we bring to our clients. And how is that unique in our space, right? And I think Insight is -- if you followed insight, you know we've been going through this transformation from that channel partner reseller to really a global solutions integrator. And for us, I think what's important is we need to be able to address our clients' entire need in this space. So obviously, knowledge of the hyperscale environment and the cloud providers and what they're offering, whether it's productivity, whether it's some of this kind of the APIs that enable custom models, whether it's the foundation models themselves. Having deep knowledge and partnerships with those hyperscalers is critical. But there's not a lot of organizations that have both that as well as the relationships with the OEMs and the chip manufacturers that are going to power both the cloud infrastructure, I mean, NVIDIA is selling like crazy into the hyperscalers and AMD and others in Intel. But also the OEMs that are going to build private infrastructure, whether you're running this in a data center. I mean, think about Juan probably talked about all the data centers that are going to need to be modernized to load up more GPUs than CPUs. So whatever infrastructure is needed to run these models. Not to toot our own horn too much, but like I don't think there's another company that has the depth and breadth in both the hyperscale cloud environment as it relates to generative AI as well as more of the private infrastructure and OEM environment as Insight, right? There's nobody that has that heritage of both worlds. And they're really necessary. If you think about the ecosystem that exists today, all of the big players fit into our category of top partners. And so for us, we're having conversations with each and every one of them about their product road maps. We're designing reference architectures. So when a client comes to us and they work with a particular OEM and a particular cloud provider and they want a hybrid environment, so they can run these generative AI workloads in the cloud, in the edge and the core. We can help them do that across that whole technology landscape. And so whether it's, like I said, product road maps, reference architectures are actually working to co-invest and developing IP. This accelerator that we've got in the market right now is built on Azure OpenAI. And so we can spin that up very quickly. We collaborated closely with Microsoft both to develop that as well as to bring it to market, getting in front of clients, evangelizing its capabilities. So I think that type of motion is going to be key to landing this offering. But I really do believe -- I can't think of another company that's got the breadth of capabilities, the channel, the partnerships, the deep services expertise around cloud, app dev, data, infrastructure to really solve these complex problems for clients.

Sounds super interesting. You might have touched on my last question, but it's a 2-parter. So maybe the second part will be a little bit unique. But before I get to my last question, I just want to remind folks we'll definitely hit the questions that are in the Q&A section after I finish mine. But obviously, if you guys have any other questions, please feel free to drop it in the Q&A box, and I'll read it on your behalf and have that answer it for you. So yes, just -- you kind of touched on this already throughout the conversation, right? You mentioned Microsoft a couple of times. I don't think it's anybody -- it's a surprise to anybody that Insight has a very close partnership with Microsoft. As you look forward in the context of generative AI, how are you seeing that relationship develop? Do you think it's going to be a tighter and closer relationship between the both of you as you kind of lean on each other to get these generative AI solutions out there to a broader customer? And then the second part of my question is, is there anyone in the ecosystem that could almost be like another Microsoft, right? Because obviously, AI, Microsoft that was originally developed or that relationship kind of was developed on this concept of getting cloud out there. And even if you look at your competitive landscape, there are some folks that are considered the large VARs of today, right? And they're not even leaning into cloud as much as some folks might even think of. So from that context, is there another -- like how should we think about the ecosystem? Is there another play into AI obviously, the low-hanging fruit here is like a NVIDIA relationship. But is there another like tight knit relationship that folks should kind of think about in terms of as the relationship develops that maybe they're not really considering because it's not typical for the channel to have that partner in that ecosystem. Just trying to get any thoughts around that.

Yes, absolutely. So there's a couple of questions there. So I'll start with the Microsoft relationship and where we see this strengthening that relationship. So obviously, with them being really first to market around their partnership with OpenAI and launching the OpenAI capabilities within Azure as well as what they've done with CoPilot, that's already accessible within GitHub for developers and it will be coming out in the various productivity suites, Office 365 over time. They have probably got the most buzz in the market that kind of came out of the gate as the dominant player with the first mature offering. And as one of Microsoft's largest partners and Microsoft, obviously, being one of our largest partners, we've been working with them for a long time in this space so that we could both capture it. They can't -- the demand exceeds their ability to capture it right now or to fulfill it, I should say. And so we're working with them in the commercial space, really their scale to build these offerings like our IP that accelerates the adoption of open AI and Azure. So they are -- I don't want to say they're struggling, but they have such kind of inbound demand from clients that they need partners like us to help satisfy that to meet with the clients, to talk about what the product does, to talk about the right way to deploy it, to add in IP and the services to make sure that it's a successful deployment. They can't do that on their own. And so when they see this type of demand, especially in that kind of SMC space for Microsoft, if they need partners like us to help fulfill it. And we're perfectly established. We've been doing it for years, and we can scale up to support that. And especially with some of the investments that we've made, building out our teams offshore, a lot of this delivery can happen that way. And so we can build these packaged services and deploy them quickly to get clients up and running on the Microsoft Azure open AI and copilot capabilities. So I think right out of the gate, I've talked to global sales teams at Microsoft. We've done events in the field. We've brought clients in, we're doing a road show. So I think this is just going to deepen our partnership there. I think in the enterprise space, we're also a really large Microsoft partner. That's where you're going to see a lot of co-investment in big client initiatives over the next coming years where Microsoft fiscal starts in about 2 weeks, and we're already talking to clients about what those really large initiatives are going to be. So in their top 500 accounts, that's where you're going to see major transformation projects, digital transformation projects to modernize and tire suites of tools within the enterprise. And so that's where I think also our partnership comes in strong is that we've got really deep technical expertise, not just to help them scale. I mean that's really important. But when they're picking their biggest account that's going to build the most complicated enterprise system that leverages this technology, we're the partner they come to. And so I think that's really important in strengthening the relationship as well. And they've gone through a lot of changes. I think that they always go, are they more internally focused? Are they more partner focused? Obviously, they know they've got to get great product right now, but they've been great to get in conversations about what the partnership looks like, how do we go to market together? Because Microsoft knows that, that partner ecosystem Insight at the top of that list is really going to help them scale and capture this opportunity. So I'm excited about that. I think that is only going to strengthen and grow our Microsoft relationship. But you asked about kind of who helps in the space talk about NVIDIA. There's a lot of complementary partners. I think a lot of the chip manufacturers. I mean, a lot of these workloads drive consumption of NVIDIA product or AMD or even Intel and others. A lot of the companies that might have been a step behind, this is such a big market. They're going to catch up. So we talked to partners that might not be as far ahead as NVIDIA or Microsoft, but we look at their product road maps and they're going to get there, right? This is a big enough market, but I think there's space for all of the established players, assuming that they're thinking about it and they're innovating and they're investing to play a big part in this. So we talked to the chip manufacturers, but the OEMs, Lenovo, Dell, et cetera, that we've got great relationships with. There is going to be -- I think this -- and Juan probably talked about this, the amount of investment that's going to happen in modernizing data centers in building Gen AI appliances that can run at the edge, there's a lot of use cases that we see where people are going to want to run these models on private infrastructure. It might be connected to the cloud. It might be a hybrid. It could be Azure stack, it could be -- you name it. But the -- whether it's highly regulated industries, whether it's critical national infrastructure, defense there's going to be so many use cases that light up that whole ecosystem, where I think especially -- and it's not an either or, it's not cloud or edge or data center. It's an and conversation and the more flexibility you can create in moving workloads, you might train it in the cloud, run it at the edge of the data so you might train it in the data center run it in the cloud. You've got to look at cost, you've got to look at security. You've got to look at just the practicality of what you own and where you want to invest. And I think that's where Insight thrives, right? That's where clients come to us for that type of advice. So I think that ecosystem is going to be strong. I don't think there's a lot of people that can address it, but I know Insight can. But then I don't -- just because Microsoft was first, and obviously, they're our largest partner. But almost all of our enterprises are multi-cloud. And they use -- they tend to use different clouds for different workloads. They're not necessarily taking one workload and moving it across multiple clouds. But what I've seen that's really interesting is there's a little competition there. I talked about Microsoft with their productivity suite and Office 365 and Copilot and Google Workspace and what they're going to launch. But I actually think each of the cloud providers has come out with something a little unique which could create opportunities in this multi-cloud or many cloud type environment. So Microsoft obviously open AI. Everybody knows what that can do and their other investments that they've made in other form. They kind of put it under the umbrella of copilot, but it's the code, it's the e-mail, it's the presentations, it's all that type of stuff. But I mentioned Google has actually built out a library of foundation models themselves. And so Google is known for their expertise and data, right? A lot of our clients use them for cognitive services, data workloads. And so I think they've got a really compelling and somewhat different offering from Microsoft where they have started to invest in building custom models that really are only access via APIs. So this is where you want to create a custom app or a custom process. So I think they've got a unique offering there. And then like I mentioned with Amazon, they're -- obviously, Amazon, they're a large marketplace, and that's one of their strengths. And so they have started to differentiate by pulling in other -- like I mentioned, stability AI, A graphic, there's other providers that have built these models, other kind of ISVs and third parties. And they're kind of pulling them in under this umbrella, so you can get access to those models within the AWS environment. So I think all of the hyperscalers are going to thrive. Some might be a little ahead of others. And obviously, our relationship with Microsoft is extremely strong. But I think you've got to keep an eye on all of them. You've got to look at the chip manufacturers. And then ultimately, like I said, I think there's an OEM play. I didn't even really talk about software. Right? And so I think we're probably going to run out of time. I want to leave time for the other questions, but I've talked a lot about the hyperscalers and the infrastructure providers. But within the software space, I mean you talk about CRM tools. Is there going to be a CRM tool that doesn't have generative AI? Is there going to be an ERP system that doesn't have generative AI? Is there going to be any type of third-party system, whether it's health care, retail, that doesn't have these capabilities embedded in it. Within 5 years, I don't think there's going to be any major platform that doesn't have this. And so we don't have time to go through all of those. But obviously, Insight in the channel, I mean, Adobe, you name it, right? Everybody that we work with has this on their road map. And so I think it's an exciting time to be in the channel. It's an exciting time to be a technologist, and it's an exciting time to be leveraging this technology.

That's super interesting. And as you said, Scott, a couple of questions here for you. So we'll start off at the top. The first one reads, how will the data stack evolve to Gen AI trend? Will lake house players like Snowflake and Databricks proliferate? Will no SQL players like MongoDB become more popular among developers?

Interesting. I won't compare and contrast databricks versus Snowflake versus whatever. But I will tell you that when we look at these solutions, the #1 pull-through -- so not necessarily the productivity one. Well, actually, maybe even the productivity ones, when we're doing these chat capabilities and pulling in enterprise data. The biggest pull-through offering that we have behind generative AI is modern data estate. So what we're doing is we're talking to clients and they're saying, hey, how quickly can we stand up one of these chat capabilities either in our corporate Internet or through like Teams. And our answer is, hey, hours, not days, not weeks, not months. We can send the stuff up in hours. But then they ask, okay, but then how do I pull in my corporate data? How do I pull in either stuff that's in my enterprise data mart or data warehouse like you talked about like the Lake houses and like all of these capabilities to modernize data state are going to see increased interest and adoption. There are some questions about do you really need to cleanse all your data or cogenerative AI effectively allow you to skip some of the steps. Master data management, right? Like your -- do you really need to create perfect alignment between all your different data sets and normalize all your identifiers or can generatively AI figure that step out for me. That's a really good question, right? Like if we -- if you have 2 different systems that have Matt -- if I go to a hospital and I've got Matt Jackson in 2 or 3 different systems, that's a big problem for health care today. And there's a lot of work that has to go into aligning those. And generative AI maybe alleviate the need to do that? And what does that do to some of the software stacks that you just talked about. I think that's a legitimate question. But regardless, the investments that need to be made in modernizing data and making data available to these models probably outweighs any potential impact or decline in the complexity of implementation, right? It becomes easier to do this in some ways, but it becomes more necessary as well.

Yes. Interesting. Another question from a client goes, where does your AI value pop -- okay, let me rephrase this. Essentially, they're basically asking you, how does the competitive landscape look like relative for you guys versus your peers. Yes, basically, that's how it reads.

H Yes, absolutely. Matt, I think 3 things. The breadth of our capabilities and partnerships is pretty unique. I talked about that quite a bit. So I don't think there's other companies that have both the cloud partnerships and expertise, the OEM, the chip, expert. So there's nobody else has that breadth. So I think they're we definitely have a really unique value prop. If you've got a complicated environment, regulatory technology, you name it, even from the human workflow factor, I think we're well prepared to have those conversations. But I also think that we are very -- we're all in on this. So we have invested in ramping up our teams. So I won't say all of our developers are using generative AI because we need to work with clients to make sure they're comfortable with that. And there's risks involved there. But where our clients are comfortable with leveraging that where we have agreements in place, our teams are already leveraging that, and we're seeing productivity gains. For basic software development, we're seeing productivity gains and Microsoft published, I think, 56% is what they saw through a study using GitHub Copilot. We can validate that. We don't have that level of precision, but we're definitely anecdotally seeing those types of gains. But for things where you're modernizing the code base, you're basically saying, hey, we're going to upgrade this code base from X to Y. We're seeing 10 to 20x improvements, right? Orders of magnitude improvements in productivity. And so I think that's another area where we've got a value. When we're engaging clients and modernizing the application, migrating a data source or data state, modernizing the data state, we can now do that faster than we could before. And sooner or later, people will catch up because it's going to become table stakes. You're not going to win these projects if you don't have that level of productivity amongst your team. But I think there's a window here where the faster that you can introduce these capabilities and get your clients comfortable with using them, your value prop, you can do more for less, right? Or you can get more done faster than you could before. So I think that's an area where we're differentiating. And then like I talked about kind of the putting the emphasis on the human impact as well and just understanding what does this do to your product strategy, what does it do to your organizations change management strategy? I think we've got some unique offerings there as well. So kind of attacking it from all sides, but I think Insight definitely has a lead in preparing to help our clients transform around this technology.

For sure. The next question here is what's the most common first enterprise application that Insight is working with clients on it? Is it contact centers? Is it cybersecurity? Is it document processing? Is it unlabeled data management? Is it process automation? What copilots are they mostly implementing?

Yes. So I'll say -- I won't say most, but I'll give you an example of a few. So we look at all the different use cases. And so the basic one is actually just standing up a private chat environment, so people can say, hey, write me an e-mail that says X Y and Z because they might not -- depending on how quickly co-pilot rolls out, they might not get co-pilot for months or a year or more. And so just standing up an environment where they can allow their employees to go in and enter in data and get responses back in a ChatGPT like environment. That's the basic. And you'd be amazed at how much productivity gain our clients can get from a very small investment there. So that's #1. But then when you look at individual use cases, we look at -- is it more verticalized or horizontal? I'll say right now, we're looking at more horizontal use cases. The vertical will come and there's some there, but because the demand is so great, addressing some of the vertical use cases, document generation or give you a good example. Document processing, reviewing contracts for policy violations or terms and conditions that are missing that's a low-hanging fruit. You've got a library of documents, you've got standard policies, terms and conditions, validating that can accelerate contract reviews, contract development can definitely use the burden on some of the legal teams. HR, huge use cases there for employee onboarding, training, again, creating agreements, creating job recs, recruiting. So these are the areas kind of these horizontal looking at the businesses. And if you have seen some of the organizations that have come out and said, hey, where are we going to maybe drive productivity improvements or maybe look to hire less over time. It's a lot of these shared functions, legal, HR, recruiting, et cetera, where we're seeing some pretty big productivity improvements and just rapid adoption. But I think the verticalized ones specific to health care manufacturing, retail, we're having those conversations, but it's going to take a little bit longer to build the solutions, train the models, et cetera.

Yes. Interesting. So the next one I have for you is, do you have examples of start-ups that are bringing AI-enabled software to the market and disrupting established players?

So it's -- so established players is tough. I'm not -- I won't name a whole laundry list of the start-ups and names in here. But if you look at like what we're seeing around automated assistance and bots. All of us went through this journey of building chatbots 5 years ago, right? And the chatbots of 5 years ago are nothing compared to the chatbots today. And so you look at where you can actually have like what appears to be a person that can actually converse back and forth and answer questions in a very natural way. You're starting to see more investments in actually like hand gestures, facial expressions. And so that's definitely disrupting like the customer service applications that we're building in the past, the chatbots that we're building in the past, everybody is -- and that's actually one of those horizontal use cases, replacing chatbots, especially for websites, product recommendations, customer support. So we're definitely seeing disruption there, where new players are coming in and displacing folks that maybe have legacy or traditional customer support platforms. Help desk is an area, right, where we're coming in, same thing where you can displace a lot of the companies that maybe we're providing help desk software or even personnel. You can see a lot of disruption there. This isn't where we play as much in the kind of graphic design, media creation, but I'm seeing a tremendous amount of disruption there, right? So whether that's graphic design. We see -- we're seeing productivity improvements because we have people that design mobile apps and design websites. And they're leveraging these technologies to improve the speed and quality at which they can develop new designs. But for folks that are dedicated in that space, we're seeing a lot of displacement. And I won't even get into kind of the marketing and media space, not necessarily our area, but obviously, seeing a lot of construction there.

No, for sure. Next question here says, do you think Gen AI will disrupt software business models in terms of profitability, having to invest in infrastructure/data models, negative on gross margins, having to recruit and pay less, lower on -- OpEx, consumption-based pricing versus subscription, et cetera. So I guess how do you see the go-to-market and kind of the monetization of that changing with this generative AI use cases run software?

Yes. Well, it's early. And so I think there's some -- still some things to be figured out. Do the productivity -- like one of the big questions, if we can develop code a lot faster, in the near term that we can probably drive higher margins, right? We can deliver more for our clients for less cost. So that's great for the business. Does that eventually result in pricing pressure, right, where clients realize that these productivity gains and they want some share of that. Absolutely. That's the natural kind of adoption curve and the maturity curve. So I think the key is there's opportunity to be had now in adopting these technologies and getting the near-term benefit. Eventually, I think from an economic standpoint, it's going to get priced in, right? The value that you place on certain jobs where maybe AI can do a pretty good job replacing those roles is going to be less. And so I won't name some, but some have come out -- some companies have come out with announcements that they're going to either lay off or not hire X number of thousands of people in certain roles. And so I think there's going to be economic pressure, wage pressure on those roles. But I think from a -- from an organizational standpoint, I believe this is going to drive significant top line growth. I think the demand is so great that you're going to see -- I mean we're already seeing it in the market. I read an article the other day about like why are we -- are we in a bear market or not, bull market. And they're like, we should be probably in a recession except AI is keeping us a flow, right? All of the investment that's being made in AI today and the appreciation of companies that play in this sector, right? A lot of the ones I've talked about today, but Insight as well, I think there's an enormous opportunity to grow for profitable growth over the next few years. Because there probably is wage pressure, but there's also more than anything, just enormous productivity gains, Almost everybody across the organization, especially in tech, where we're writing code, where we're building applications we're seeing productivity gains. We're seeing top line growth as a result of the investments that people are making in this space. And so I think there's a lot of near-term benefit. I can't -- James is going to follow me on the risk if I give anything specific to insight, but I think just generally, those are the trends that you're going to see, and you're already starting to see them.

No. For sure. I don't worry, I'll make sure James stays in this place.

This is broad commentary on site, but -- but I mean like just look at the market cap of some of the places -- the companies that have come out with AI technology and the productivity gains that are obvious.

I'm still listening, by the way.

We were just testing. So. Let me just check time. Okay. So maybe this is -- there's enough time for this last one here. Are you seeing any material traction for non-LLM-typeAI i.e., more quantitative focused in CPG specifically pricing more broadly?

Well, I don't know about the pricing question, but the demand, yes, I think there's always going to be kind of associated demand for this technology because we'll talk to clients and they'll say the buzz around generative AI strikes up a conversation. We're like, okay, but that use case is actually better served by deep learning, machine learning, more traditional, as we talked at the very beginning more traditional AI mechanisms or maybe the real problem that they have is that they have a distributed data state that needs to be modernized. And so I think we're seeing definitely kind of pull-through demand. [ Atmod, ] data state I mentioned data state like cleaning up the data, but then [ Atmod ] whether that's leveraging generative AI to accelerate the pace of [ Atmod ] or whether it's building new applications that expose generative AI experiences, there's pull-through for all these different capabilities. So that's why I think this is such like mobile, like cloud, like Internet, such a pivot point or an inflection point for this industry is that this isn't just going to power investment in generative AI. It's really going to power investment across the IT stack.

No. Got it. So we did perfect timing here. So let me end the call here. I know there's a couple of questions left in the Q&A. So I'll get through those and circle back and then hopefully, we can get Matt some -- get you guys some answers, but we'll get back to you over e-mail. With that, I wanted to thank you, Matt, for taking the time today. James, Ryan from Insight as well. Thank you for taking the time today and setting this all up. I appreciate the time of all of you participating in the call as well. And with that, we can close it up.

Great. Thank you so much. Appreciate it.

Great. That concludes todays webinar. Thank you all for your participation. You may now disconnect your lines.