Let's tempt him to leave the doors open because we always talk about having fireside chats, but this is like having an oceanside chat. Like, that's not too bad, especially on day two of a conference. For those who don't know me, I'm Ryan MacWilliams , Midcap software analyst here at Wells Fargo, here for the ninth annual Wells Fargo TMT Conference. With me today from HubSpot is CEO Yamini Rangan. Yamini?
Thank you, Ryan. Thanks for having us.
Absolutely.
It is a great location.
People were complaining on day one. They were like, "Oh, it rained today." I was like, "This is, like, I'm used to New York City in the winter right now. This is amazing.
Right. We'll take it in California.
I'm jealous. You know, it's been an interesting time in software coming back after my break and launching coverage. You know, it does seem like it's kind of a rainy day in software right now, but that's a lot of opportunity. I'm excited to hear about what HubSpot and that you're doing with AI. For investors in the room, we're going to be speaking mostly around the product to start, and then maybe some of the earnings at the end. If you have questions, let me know, but we won't be taking questions directly from the room. Email me at Ryan. MacWilliams@WellsFargo to get those in. Yamini, just to start, you know, with your investor day, there's been a lot of new products from HubSpot and AI at this point. What would you say, like, differentiates HubSpot's AI offerings?
Yeah, we just had our annual conference a couple months ago in September, and we did launch a lot in terms of AI. It's an exciting time to be in the industry. Even though it feels rainy, it is actually an exciting time to be in the industry right now. Just to give context, our AI strategy is to take something that is super powerful and apply it to the segment that we serve, which is we serve small, medium businesses, and we want to take AI and apply it in a way that can help them grow. That is really the strategy. In order to do that, we are embedding AI into all of our products. You saw a number of feature releases there. We're building agents that can help do work.
We have three, three featured agents that we launched at our conference, which is Customer Agent, Prospecting Agent, and Data Agent. We have a world-class Breeze Assistant that forms like the copilot that every go-to-market employee can use. That has been the strategy, and we kind of launched a lot of products to drive that. The reaction has been very, very positive. I mean, we can talk about AI adoption broadly and where we are seeing, you know, green shoots in terms of consistent usage. You know, if we step back, you asked a question, what differentiates our AI strategy? The first thing is we know SMBs, and we've taken an approach of taking super sophisticated technology and making it very accessible to SMBs. That's been the business that HubSpot has been in, and we're doing exactly the same thing with AI right now.
That is the number one differentiator because we know SMBs, we know what they need and their day-to-day job and their day-to-day challenges, and that's like number one. The second thing I would say is context, and I'm sure we'll talk a lot more about it. The way to think about HubSpot is we bring the context of every sales conversation, every campaign that was sent out, every email that was generated, every deal that was closed. It turns out, while AI is really good at generating insights, it needs context. You know, Ryan, if you go to, you know, an LLM and say, "Write me an email," it's going to write some email that might not actually convert, you know, the right outreach for you. If you had the context of all of these conversations, over a period of time, then it generates much better responses.
Our differentiator is that we have 280,000 customers using us for marketing, for sales, for service across the customer journey. We have become a platform that small, medium businesses rely on to drive growth, and that context helps make AI much, much better. You know, the combination of the domain expertise plus the context that we bring in helps us drive AI adoption and give value back to our customers.
A lot to touch on there.
Yeah.
On the domain expertise side, it's always so interesting to me when I hear, like, the bare cases on it, on what AI can do versus, you know, application software. People are like, "Oh, what if you can recreate this exactly?" But it's like, "Okay, but what happens the next day?
Yep.
Right? Like, who's going to think about, like, what's the best ways for customers to make more money off their own customers, right? Or new use cases from a customer service standpoint. I was like, "That's what HubSpot does.
Yeah.
Like, you have hundreds of people and, like, decades of experience in doing this. It's like, it's not static in terms of, like, "Okay, this is what a large language model can do." It's, "Okay, how do we continue to advance on the platform for what customers need?
Exactly. I mean, look, coding has become easier, but expertise is still important. I do think that the domain expertise and being able to apply something to a certain segment and make it easier for them is still where there is value that is getting created. We think that, you know, HubSpot today is adding way more value for our customers than we did three years ago because of AI. It doesn't just go away, you know, with AI.
I think that's a great point in terms of, like, there's a difference between, like, the coding advances that we've seen from large language models, because that's a publicly available data set.
Yep.
Right? That has Stack Overflow and has a more deterministic outcome, right? Like, code is either right or wrong. You can debate, like, a better way to do it, right? That is like chess. It is a much easier type of problem to solve. Where, like, how does a B2B organization correctly address this one use case for their customer? That seems a little more challenging.
That's exactly right.
When it comes to HubSpot, you know, you have a large customer set that you've been working with for a long time. Like, they're going to be asking you for certain AI solutions that you can help with. How does your data advantage also help build, like, a more holistic AI workflow?
Yeah. I think before we go into the workflow, maybe fundamentally, what is different in an agentic architecture that enables all of these workflows, right? If you take it down to the foundation of this, you can do much more with unstructured data. Like, CRM and customer platforms have always had structured data in roles, in records. That's what we are good at. An example is like a customer record. You have the name of the customer and the address of the customer, the revenue. That's a customer record. That is what we've always been good at. Now, with agentic, becoming an agentic solution, you need to be able to handle unstructured data.
The conversation that we just had, the transcript of a Zoom, you know, call, something that you said that is out on social that we can now grab, that is all the unstructured data. And what AI made it possible is to process all of that unstructured data and add it to the same context that you have. That's the one big change at the data layer that we need to. Our solution, it was easier for us to go from having all that structured data to now capturing emails, Gmail, you know, like Zoom transcripts, video calls, audio calls, all of that, and add that unstructured data layer. The second thing that you need within an agentic platform is really orchestration. It's not just about dumping raw data, but having the context across all of this data. Where do you get feedback?
What is, was that answer good or not, which is evaluation? Where do you have the memory of the questions that you have asked in the past? The orchestration layer becomes really, really important with evaluation, feedback, and memory. That is what we have built. What is changing in terms of how applications work is it used to be that you would go and point, click, and navigate to something. Now you can have conversational ways of asking software to do something for you, and you have agents that actually do it. Just to be, like, really clear, what has changed is the amount of data that you can process, the level of orchestration that you bring, and how you can have a conversational, you know, way of interacting with software.
That is literally what we have built over the last couple of years in terms of the foundation. And so, you know, then it becomes like, how do you enable workflows? Well, because we have much better unstructured data, now the workflows become much better. So, instead of using a CRM where someone had to go and write a, you know, contact and create a contact and say, "I met Ryan for the first time today, and this is the contact, and this is the conversation," instead, you can process all of that through the unstructured data that you're able to capture. So, workflows become much more dynamic, and workflows become with much more context of unstructured conversations that you're having. And that enables, you know, much better output for our customers.
Yeah, we were just talking about that in regards to what we do every day.
Yeah.
Before you log a call and you're like, "Oh, I'll put a couple notes in. It doesn't matter." Now that you know it's going to help you down the line, you're like, "Okay, maybe I'll add more detail there.
Yeah, exactly.
Your data set gets even stronger. One of the funny things I keep running into is, like, you know, we talk about, like, "Oh, AI is going to do this, and it's going to write all my research for me." Sometimes it's just like, you know, summarizing an earnings call is like the most helpful thing, right?
Yeah.
It's like some of the unsecured use cases are actually getting adopted faster or customers are more interested in. So far, when it comes to your customer base, is there any use cases that might have surprised you or things that your customers are more interested in at this point?
I mean, a ton. I'll tell you, I got started in sales, like, multiple decades ago. Back then, you know, the hardest part of the job was you'd get a set of accounts. You know, let's say at the beginning of the year, you got like 500 accounts. You just never had the time to look at 500 companies, look at five people per company of who was the right contact, and, you know, are they going to buy? Are they ready to talk about your product? Like, that was all manual work, Ryan.
Yep.
What has been fascinating, you know, with AI is that the prospecting use case, you know, is specifically one where you can now use AI to go and grab the right information about each of your 500 prospects that you have. Did they get funding? Are they adding marketing reps or sales reps or, you know, service reps? Are they mentioning initiatives that you can help? This is all, you know, kind of unstructured information and structured information that you can now get. Based on those intent signals, you can say, "Next Monday morning, these are the 10 that you need to talk to based on the intent." That is so much more value than we have ever been able to deliver. I think the prospecting use case, the other ones are pretty obvious, like support is done, right?
Like, everybody knows that AI can be used to, you know, resolve support inquiries, and it's getting better and better across the different channels that we're supporting marketing in terms of content creation. To me, like, you know, sales as a function is fundamentally changing of where you spend time and where you get leverage out of AI. It's just been fascinating to see our customers adopt that and get value.
Have you heard any pushback from salespeople who are saying, "No, I'd rather dig around for all this information"?
Oh, God, no. Actually, salespeople, the things that they don't want to do is dig around for information, put in notes of the calls that they have had, you know, show the work and activities that they have done to the manager so that they can, you know, contribute. Like, those are the kinds of things that salespeople do not want to do, which AI is actually really good at doing. What salespeople enjoy doing is being in front of customers and having deeper conversations. When you take a lot of this, the extra work that you used to do, and you make AI really good at that, then the time in front of customer and the relevance of the conversation that you're having with customers goes up, which means your close rates need to go up.
I think that's the exciting part of what AI can do for sales.
Yeah, you can spend more time on actually selling.
Exactly.
All the backend work. And as a former customer support agent, I can tell you a lot of those use cases.
Oh, yeah.
I'm okay with.
You start in support? That's awesome.
Yeah, it was for student loans.
Oh, wow. Okay.
You know, buy-siders are tough, but they're not as tough as people calling in about their student loans.
That's true.
When it comes to, like, you know, your new consumption and credit usage model, for me, like, you know, I'm still really interested in apps here because, you know, I've used Cursor and Cloud Code, and when I'm using those services, I'm clicking AI Do It For Me constantly, right? Something like Cloud Code, developers use like $6 a day worth of token usage. That's something around like 40 or 50 times you're clicking the AI Do It For Me button, right?
Yep.
When I think about that, it's like, "Oh, well, what people live in all day and the platforms they use all day," right? They'll start to, like, do the AI Do It For Me button for whatever, you know, it is, Atlassian or HubSpot or what they're used to. In terms of, like, that credit motion that's newer for HubSpot today, can you just discuss what you're seeing, like, the early customer trends and use cases where that's occurring?
Yeah. And just to, you know, maybe step back, we talked about the AI strategy, which is embedded AI agents and Copilot or Breeze Assistant. Our monetization strategy is hybrid. We monetize AI both through seats as well as credits. For the example that you just gave, you know, within HubSpot, if you are a Sales Hub user and you're clicking multiple times today to summarize the email and get me the next follow-up email, which you can do today, you can say, "Summarize that call and give me the follow-up email." That is part of seats, and that does not consume credits. I just want to make sure that people understand that part of the way we monetize is through seats. Now, the credit specifically is for agent work that we do. Our Customer Agent resolves support tickets and consumes credits.
Our prospecting agent does the account research that we just talked about, and it consumes credits. Our data agent brings in data and cleans up data that consumes credits. There are a handful of agents as well as Data Hub that consumes credits. We launched, you know, in June for all of our customer agent customers, and it moved into install base in August. It's early days. In terms of credit consumption, customer agent is number one leading because it's been in general available mode. Our customers, we have over 6,000 customers resolving over 60% of their tickets using the customer agent, and they're consuming credits. The second area is prospecting agent. This is where I do see a lot of promise because this is a known age-old problem for sales that we are now able to solve.
That is the second area. The third is intent signals within data. Those are the areas that we are beginning to see. Look, it is early days. We believe that, you know, AI monetization for us is hybrid monetization, both through seats as well as credits. We are seeing kind of all the right signals.
I actually appreciate that distinction as a part of your pricing strategy. It's like if you're doing work in tandem with, like, HubSpot, like, then that's a part of the platform, right? But when it's doing work for you, that's where you can start to monetize those credits.
Exactly. You know, for example, within Marketing Hub, you can create content. You can remix content. That is all just part of Marketing Hub. If you take agents that do actions for you, that is where it consumes credits. You know, I will probably keep repeating this, that our AI strategy is hybrid across both seats and credits.
That makes sense to me. You know, when it comes to software today, like, if I was a power user of HubSpot.
Yep.
I used it all day, every day, and then there was someone who used HubSpot, like, once or twice a day.
Yep.
You might be paying the same amount on a per-seat basis, right? I don't know if that is going to be the same way in the next couple of years, given some of the usage dynamics here. In terms of who is adopting AI first, that you've seen so far on your platform, is it power users? Is it SMBs versus, like, larger businesses? Like, or does it run the gamut? What's it?
Yeah, it's a good question. I'll tell you, it is not based on the number of employees. You know, in fact, the really distinguishing factor is, is there a, you know, C-suite leader that's pushing AI priority within the company? Like, I've looked at it by industry, and I've looked at it by segments, and we've done a lot of analysis. It's really not based on the size of the industry. It is like, is there someone there that is top-down pushing, you know, AI? Because I would say that, you know, I know there's a very different narrative with investors, but when we talk to customers, there's still a level of fear and uncertainty and lack of trust with where's the data going and how do I make sure that my company's data is not used for some LLM training somewhere.
There is just a lot of, like, mistrust associated with that. The number one factor is, you know, top-down kind of, you know, initiatives on AI. Once you get past, like, there is someone within the company that is looking for an AI roadmap, then, you know, I do see that there is a strong ops role. In order for AI to really drive value, you need kind of what we used to call RevOps back then. Now we call them AI Ops. You know, there is someone within the company, the power user is like an AI Ops user that trains the data, gets the right quality of the data, trains the data, uses, you know, the AI features, and then makes it available for everybody else. That role is kind of like the power role.
When, you know, we see customers that have these AI Ops roles and someone like a go-to-market engineer that is leveraging AI, then the AI adoption actually accelerates within the company. For a lot of our customers, you know, they're still at the stage of, we want clean data that we can trust, that we know is not being used outside of, you know, our company. Then we want to have a roadmap of a reasonable set of use cases that we can experiment, scale, and then grow with.
It's amazing how, like, just one champion can really move the needle and you can activate a lot of others.
Absolutely.
You know, we did a Wells Fargo offsite, and I had ChatGPT answer a question, and then I had everyone build the agent on their ChatGPT. I think I should have got commissioned about the number of ChatGPT paid licenses we sold after. Once you get started thinking like, "Oh, it can do this, it can do this," that makes sense to me. That's actually a good segue into, I think the Data Hub strategy is one that kind of needs more airtime with investors here where, you know, your customers are already trained to, like, put everything they can in HubSpot and then activate off that. You have rebranded that at the most recent investor day. Could you talk about how Data Hub helps your broader AI roadmap?
Yeah. Look, already today we've talked about the criticality of data and context for AI to work, and that is known. If you step back, Data Hub does a few things. One is it pulls data outside of HubSpot into HubSpot. We have things called data syncs that are kind of like integrations that pull data from across. Typically, we'll see HubSpot customers have anywhere between, you know, 8 to 14 integrations, but sometimes they're bringing data from more sources to bring into HubSpot. That's like Data Hub. It helps you do that. The second thing that we are finding is that data quality is exceptionally important for AI to be accurate. Data Hub actually helps you improve the quality. It can run a set of prompts to LLMs.
For example, if there's like a, you know, column of like funding data that you need, Data Hub will pull the right prompts and get the funding data for every contact that you have within your database. It improves the data quality. The third thing is it provides a workspace. We call it the data studio to now manipulate the data. You've broadened the data. The data has higher quality. Now, can you build workflows and sequences and automation with that data so that your AI works better? It's almost like a foundational, you know, workspace for AI Ops and for RevOps to do much more with higher quality data. That is kind of one of the reasons why we rebranded it from OpsHub into Data Hub because you're really working on the foundational, you know, need for AI.
As we look into the future, we haven't talked about marketing, but in order for the marketing playbook Loop to work, you need higher quality data, and Data Hub provides that. That is the vision. You know, it's now one of those areas where if you're a Marketing Hub customer and you want to do AI, then you need to get Data Hub as a foundation for it. It's the same thing with Sales Hub. It's part of the multi-hub play that we have.
We have kind of touched a lot about the data advantages and the differentiators of HubSpot here. You know, it's interesting when people are always like, "Oh, we're going to build these AI use cases." I'm like, "Off what? Or how?" Right?
Yeah.
I mean, I think like the larger question that's been on top of mind for investors is like, you know, does HubSpot add AI features first? Or, you know, does AI, you know, end up doing a lot more of what HubSpot does? We've touched on a lot of these things today, but we'd love to kind of just like, you know, you've probably had this question over and over again over the last month, but we'd just kind of love to hear like all the things we've talked about today, like, you know, why you guys are better positioned in that world.
Yeah. I like the framing. Is it easier for, you know, a SaaS company to add AI, or is it easier for an AI native company to add like CRM? You know, it's a very, I like that question, but foundationally, I'd go back to the conversation that we just had, Ryan. We've already become an agentic platform by adding unstructured data, by adding a context layer that ties together all of that data, by building agents that can then take the context across that. We've kind of done, you know, a lot of the actual plumbing and the architectural changes that are needed to support, you know, becoming an agentic platform. If I were starting out as an AI native from scratch, I'd need to build still a CRM record, and I'd need to build, you know, the structured data.
Many of them are starting as point solutions. Let's say you start as a support agent and dealing with support tickets. The minute you get a question on a sales pricing, you know, then you don't have that context. You now have to extend and start building what are the products that you sell, what is the pricing associated with it, and what are the common questions and objections. You know, you have to go from structured to unstructured. You have to go from point solution to full platform. You have to build the full context associated on top of it. I would say a couple more things. You know, we're still finding that AI features get adoption with feedback.
We have the advantage of building an AI feature and making it available for thousands of customers to use that we can get the feedback and improve. AI development cycles are very iterative in nature based on customer feedback. If you're an AI native company and you're starting with 10 customers, you know, where are you going to get that feedback? There's an inherent advantage in agentic world that benefits from the scale of the number of customers. The final thing I would say is, you still need a partner ecosystem. You know, AI is still one where you require someone to look at your roadmap to help you with, you know, what are the use cases to start and drive. We have 7,000 partners within the ecosystem. They're all driving AI readiness.
If you are a new company that is, you know, getting started as an AI native, you also need, you know, that's why you see a lot of forward-deployed engineers, right? The model is like you not only build a product, but you also invest in forward-deployed engineers that go and sit in customer sites. We have that, which is an ecosystem that we have. I think like, there is platform advantages. There is scale advantages because of the distribution. Then there is an ecosystem advantage that we have.
During the break between the jobs, I was sitting around thinking like, I could probably try to build one of these, you know what I mean? As I did some research on it, it's like.
Did you?
I tried to build a project management tool.
Yeah.
That looked a lot better in my head than it ended up being on paper. It turns out like, you know, having the initial draft is a lot harder than having actual working SaaS, right?
Yes.
As I talked to other developers, it was like CRM would probably be one of the most difficult things to build, given like, you know, the high data density, mission-critical workloads, and unstructured data. But it's such a big market opportunity that people are going to try.
Yeah, absolutely.
When it comes to like, you know, you have the data already with your customers and you're already training your models on that data, that also extends like the time that an, or, you know, a challenger would have to take in order to like recreate the same like use case based off your new training model.
Yeah. I mean, look, I think, you know, we've always been in a very, very competitive market. CRM has never been a, you know, winner-take-all market or a non-competitive market. It's always been the case. I go back to like, why do platforms win over point solutions? You know, one of the most common use cases for HubSpot is that we'll go to customers and they'll say, our data is fragmented across 15 different solutions that are point solutions, and we've lost visibility of our growth. That's like that continues to be the case in the agentic world. You know, when I talk to customers who have even attempted like five different agents, they're like, well, we now cannot manage this across all of these different agents. We want to look at your roadmap. If you have it, then we're just going to continue adopting with you.
I think like, it comes back to why platforms win over point solutions. It's the same reason why an agentic platform wins over point agents that are kind of sprawling across go-to-market.
I would love to touch on that point. Just like that complexity that you're speaking to, for me, like it's a very interesting dynamic of like, oh, I can custom build my own use case now. Like that's investors kind of thinking that's the case where, like, are you seeing customers being more willing to, like, you know what, I'd rather buy AI to start than build on my own.
Yeah. I mean, custom building has gotten easier, but think about our segment.
True.
Right? Which is a 500-person company and let's say a manufacturing company in the middle of the country that's trying to grow their business. Now they have to figure out Sonnet 3.7 and ChatGPT 5.1, the latest Gemini that dropped today for 10 different use cases, and they're building a custom, you know, application on top of it. That is not what an SMB wants to do. While there is a narrative that yes, custom applications and custom agents are easier, average, you know, mid-market company still focuses on growing their business versus let me run and adopt, you know, AI just for the sake of it and build a custom application.
You said this earlier, you know, there's a lot in terms of building an agent, which is the, you know, there's a front-end piece to it, there's a back-end piece to it, there's getting the right data into it, and there's this constant iterative process of getting feedback and improving it. In order for a company to do that, they really have to change their strategy and invest pretty significantly in, you know, either AI engineering talent or AI pods that just do that. That is not the conversation that we hear with our customers. They, in fact, want us to make it like easier for them to adopt. You know, they just trust us to, you know, do that because of the level of innovation that we have brought to them.
Absolutely. You know, the thing that worked yesterday is not going to work today.
Yep.
You guys have definitely been on the forefront of speaking out about how SEO is changing as a part of AI. You guys had an acquisition at XFunnels in regards to SEO. I love all the posts about it because we've been following it pretty closely for what we cover. Can you just talk about your own efforts to solve some of those top-of-funnel changes and then like how you're helping your customers with a new SEO?
Yeah. I mean, it's a topic that we can spend quite a bit of time on. I mean, if you step back, you know, we as an industry used to put content and get people to click on the blue links and have people come to our website. Then we captured their emails and then we nurtured, and then that became part of the marketing funnel. That is completely, you know, disrupted, right? Because AI overviews are providing the answers, and that is if you search.
Yeah.
You know, half of them are not even searching. They're just going to an LLMs to ask questions. Because of that, there is a fairly massive drop in terms of a very specific type of lead called content lead, right? Content leads are the ones that have really, you know, gone down. Now, in terms you asked the question about how have we navigated it. I mean, I'd say that in 2022, even before ChatGPT came on and AI overviews were part of our language, we saw that customers were spending a lot of time on other channels, social channels, podcasts. We're all listening to podcasts. You know, we saw that everybody is listening to like hundreds of podcasts now. Email newsletters. Starting 2022, we went through a process of diversifying our marketing channels. That strategy has worked.
We have 10 YouTube channels, and the leads from YouTube channels are growing between 80-90% year over year. We acquired a podcast network, and we now have like over 100 podcasts within that network, and that generates leads. We also acquired email newsletters, which when we did, people were scratching their head saying, why are they doing this? That has also increased the leads. For us, the story has been how to diversify out of content leads into all of these different sources. Now, one of the channels happens to be AEO, and that is Answer Engine Optimization, where you show up within, you know, an LLM. That, you know, is still low. It's nascent, right? It's pretty early days in terms of AEO and how you show up in answers. The lead volume is still very low.
It's single digits, but the conversion rates of the leads from LLMs is much higher. It is 3x because you're doing a much thorough, you know, deeper questioning, and you're ready to convert if you get the right answer. That's kind of how we have navigated it. The thing that we are really good at is once we figure something out, creating a playbook and getting our customers to be able to adopt and get the benefit of the playbook. At our conference this year, we launched Loop, which is kind of the playbook of how to diversify your lead sources and drive, you know, a level of personalization. We launched that. We launched a set of products across Marketing Hub, Data Hub, and Content Hub features that support that playbook. We're early days, but kind of getting the new playbook out.
You know, I've now spoken to a lot of our customers post our inbound, and, you know, they've started doing some of this and it clicked. It's like, oh, I now see that here's how I need to diversify. Here's how I need to use AI for personalization. This is what is happening within an AEO channel. It is early days, but, you know, we feel like this opens up a big opportunity for us.
I mean, there's always something, right? It's like there's more complexity now with reach your customers where they are, more personalization. Like these are all things that they need someone to help with.
I think, you know, we're actually excited about it because if you look at the last two or three years in marketing, channels were saturated. ROI was really, really hard to get, and conversion was slowing. The playbook was like super difficult. If you're in, you know, marketing within a small medium business, you couldn't get even 1-2% improvement in any of these, you know, key metrics of lead conversion. Now there's a completely different way to do it. The level of, you know, return that you get from figuring one or two of these channels is just much better. There is just a lot more excitement in terms of what you can do with AI within marketing.
While everybody talks about the disruption of SEO, what, you know, maybe is less understood is that AI is actually creating a much bigger opportunity to optimize your marketing channels and strategies. And that's an exciting, you know, opportunity for us.
We only have a few minutes left, but just, and this might be a bigger question, for me, investors often ask me like, oh, you know, why don't they have all these like amazing products now? This is for every software company. I'm like, look, reasoning large language models came out at the end of last year. It takes time when you have hundreds of thousands of customers to put like really intricate AI products in. Besides there being a little more seasoning across all of AI within solutions, what do you think really picks up the adoption curve for your customers?
Yeah, I think that, you know, I would still say the technology is ahead of, you know, customers' ability to adopt the technology. I think you're absolutely right. LLMs came on board. You know, a lot of us, we understand the transformative power of AI and we've been building, but it's an iterative process. You got to get customer feedback to be able to make, you know, an AI feature better. That's one part of it. The other part of it is, you know, helping customers through the, do I have good quality data? Can I trust where my data is being used? Can I make sure that my prompts and all of the interactions are within my, you know, company's, you know, walls and not being used to train something else?
There is a level of just, you know, comfort around that, which is the adoption cycle. Again, it is not dissimilar to any other technology cycle that we have seen in the past. You know, I have been in this industry when, you know, I joined the industry in 1996, when, you know, that was like before we went into cloud. It was very, very similar. Everybody saw the value of it, but it took a little while for people to begin adopting. It is the same thing that is happening. There is, you know, just transformative value in AI, but adoption comes from building trust, better quality data, and then making it frictionless for customers to try something and then scale it. That is the process that we are in.
I'm really excited to see what comes next. I'm certainly not going to miss searching through our CRM for all the clients I talked to over the last, you know, two weeks.
That's exactly right.
Sooner to talk to you next. Guys, thank you for the time. Thank you, Yamini.
Thank you. I really appreciate it.
Thank you.