View Auto-Generated Transcript
The below transcript has been auto-generated for your convenience. Please reference the source video/audio for direct quotes or to clarify any errors.
[00:00:00] Chat GPT: The Fastest Growing AI Technology
Yamini Rangan: Companies that were on the leading edge got to the leading edge by putting technology and features from AI in front of customers. The customer feedback loop was what got Chat GPT to be one of the fastest growing technologies this year, and it was the customer feedback loop that we needed to focus on.
Connor Jeffers: Hello and welcome to Go-to-market with AI, a podcast for sales, marketing, and customer success leaders using AI to scale their growth operations. I'm your host, Connor Jeffers. Hello and welcome. Yamini.
Yamini Rangan: So excited to be here, Connor.
Connor Jeffers: I am incredibly excited! I was joking to some folks on my team that we started this podcast for the sole purpose of I wanted to talk to you about AI stuff for as long as I could because you were one of the first people that I ever had a conversation about generative AI with. And I remember because you were like, "oh, you saw this?" And I was like, "yeah, it's super interesting". And you're like, I have this big stack of books. I'm reading everything. I am obsessed. And then we didn't get anything else that we were going to talk about done, because we spent the rest of the time talking about AI things. And I've just been wanting to go deeper than that ever since.
Yamini Rangan: Exactly. I still remember that conversation and I'm still reading a stack of books. But it's been a very eventful one year, Connor. Right?
Connor Jeffers: The longest year that I feel in technology history of how wild things have been.
[00:01:29] Generative AI and its Impact on Small and Medium Businesses
Connor Jeffers: So where I'd love to start is, do you remember when this first specific to generative AI, as opposed to AI generally, of when this came onto your radar and when you started to say, oh, this is very interesting and something that we should be paying attention to, and I should be developing expertise in this arena?
Yamini Rangan: Yeah. So two distinct points, and I'll tell you both. First one is fairly dated. I studied computer engineering way, way back, and one of the most fascinating things for me when I was doing that was neural networks. Now, when we're studying neural networks, it was like this whole concept of the potential for neural networks to be able to think. And I was fascinated back then. And so just along the years I looked at, like, Imagenet, when that happened, the transformers paper, when it came out in 2017, I was like, wow! things are progressing in a way where something big could happen. Right? And when I was studying computer engineering, it was like nothing big could happen. And so I'd been fascinated by it. So that's like point in time in terms of touching base on where the world is going. And it's kind of simple. You take a neural network. You give it all of the data and information, and then you use a very simple way for it to process all of that information. And you predict the next word, and you begin to predict it with a level of precision that it actually makes sense. Right? That's the concept of it. And so I've been fascinated by that. But I'll tell you, obviously, November 30 last year was big. And it was big because of the, not just the technology, because we've been having advancements in technology, but because they put it in the hands of customers, and customers started using it and using it for practical applications. And that is like a huge moment in time. And I still remember the same time of the year. We're kind of a couple of weeks before Christmas right now. I was in a car with two friends and my husband, and they were like, what's going on? And I literally, for an hour and ten minutes, did not stop talking about generative AI. They had to eject me out of the car because I was so excited about what this means for small- medium businesses, what this means for marketing, for sales, for service. And I can't wait to geek out about all of this with you. But it was just this moment in time in December last year where I was connecting the dots, and then we could see how impactful this was going to be in exactly what we are doing.
Connor Jeffers: Yeah, I remember very similarly. I actually think my mother in law, or my future mother in law was in town and we were at lunch and I was talking about, this is going to be amazing. You can do all these things. And she was like 'Oh that's really cool, but that's really far off'. I'm like, 'No, it's now. It's right now!' You can do this today. It's crazy. And I think the thing that I remember, I spent so many hours, and I'm not an artist, and I spent so many hours with Dolly and just doing the extension components. And this is unbelievable. And it's funny to me, reflecting on that and then seeing the video components that can happen now and how extensible all of this has become has been really breathtaking to watch, and I feel very inside of it and at the same time can't possibly keep up with the blistering pace.
[00:05:11] How HubSpot is Embracing AI and Embedding it into Their Product
Yamini Rangan: I want to talk to you a lot about, where are you experimenting? Because you just have done a ton of research on the applicability of AI and marketing, sales and service. And I read that report and it's like, just great insight. So, yeah, let's get in.
Connor Jeffers: Sure. So where I'd love to start is one of the things that we're amazed about is in our research and when we talk to a lot of organizations, everyone's very, 'this is really exciting'. 'We aren't sure what to do'. 'We aren't sure how to do something'. And we'll get into sort of all of the whys of that. But something that's been unbelievable for me to have a first row seat too is how much HubSpot has embraced this technology and deployed it throughout the organization and throughout the products. And when I talk to the product teams, there's not, oh, this is the person in charge of AI stuff. It's every team, every product, every organization is working on how to get AI built into what they're working on. And the scale of HubSpot is vast. If I just look at LinkedIn, you're almost 9000 people globally and you and the rest of the HubSpot leadership team have managed to say we are doing this and we're doing this now. And how have you approached the deployment of that into the entire organization and into how you build product?
Yamini Rangan: Yeah, so I'm going to tell the backstory on this. I think that is as fascinating as what you are seeing and perceiving in terms of the progress we have made. So November happens and then of course Dharmesh is so excited. Dharmesh Shah, our Co-Founder, so excited. And he probably, I'm talking about it and he's probably not sleeping, working on all of this stuff. And then around February we were like, okay, this is game changing, which means we need to change everything that we're doing this year from a roadmap perspective. And we talked about it with the leadership team. They were on board. But then one of the things that we did is, okay, great, how do we exactly go and do it? We interviewed probably at that time who we believe to be the sharpest minds in AI, in research, in companies like OpenAI, in other companies that were beginning to embrace it and were in the leading edge. We went out and reached out to like ten just amazing experts that we could find and we brought our entire leadership team and we just went on this listening and learning tour and just Andrew Ng, folks from OpenAI. Just amazing, incredible people that we were talking to. And a couple of things that we took away from all of those conversations in the March timeframe. One, the time delay between research and actually product manifesting. That research was not months, not years, it was weeks. And so every single person said from something coming up in generative AI from a research and academic perspective, to that translating into product was just happening in weeks. And so what we took away was that the product innovation cycle needs to really speed up to kind of embed that that was number one. The second insight we took away is people could predict what was going to happen maybe ten years from now. The world looks very different and things are all kind of driven by intelligence, but no one had a clear path of how that was going to transform in the next one to two years. And everybody was like, we'll know this is going to have huge impact. We don't know exactly how it's going to have impact. And that was a huge takeaway for us. And then the third thing, which was probably very insightful, is that companies that were on the leading edge got to the leading edge by putting technology and features from AI in front of customers. The customer feedback loop was what got ChatGPT to be one of the fastest growing technologies this year. And it was the customer feedback loop that we needed to focus on. So three big insights that came out of research and we immediately kind of went and we said, we're not going to deliberate, we're just going to do. And we pivoted the whole company to think about AI. And across all of that, one clear decision came up at HubSpot in 2015. We said, every company that is a scaling company needs a great CRM. So we're going to build CRM. That was the high conviction move back in 2015. Any small medium business that needed CRM, we're going to make it easy. And we had the same conviction coming out of all these discussions in March, which was, every company now needs an AI driven, smart CRM, and we're just going to build it and make it available. And that's where you see that AI is deeply embedded within our smart CRM and AI is deeply embedded within each of the hubs. And we just believe that we need to democratize such a powerful technology and build it in and embed it in across all of our hubs as well as the platform.
Connor Jeffers: Yeah, I mean, I think that the thing that resonates with me the most, and I think I'm generally drawn to technology, I'm generally excited about the future. I've been in the CRM universe my entire career. And I think the piece for me that became so obvious is having been somebody who used CRMs, designed CRMs, built CRM s, this immediately felt like something that much the same way. I think on the consumer side, people are seeing AI impact on video and it resonates instantly. I feel that's as extreme and I think if you are somebody who has not spent a lot of time in the CRM universe of products, it's as impactful from my perspective as when you see it interface with video of saying, whoa, this is going to change how everybody uses these systems. It's going to change how everybody uses these products.
[00:11:20] HubSpot's core CRM and its interface with the customer platform
Connor Jeffers: And something that you just mentioned is HubSpot and smart CRM being independent from all of the hubs. And I think one of the things that makes HubSpot really unique is the core CRM is unified as you have this base CRM and then you have every hub on top. Before we get into what's so exciting about each hub and what hubs can do, where do you see this interfacing with the core CRM itself that underlies all of the HubSpot product functionality?
Yamini Rangan: Yeah, I can't wait to get into this. You picked this up very early as we were kind of like building the vision. So our vision is to build a customer platform that's deeply embedded with AI. And the customer platform has a system of record and a system of engagement. The system of record provides one single customer record. And you said this, right? You and I have been in CRM for the longest time. Connor, what have we always struggled with? Well, there's a little bit of customer data here in marketing. There's a little bit of customer data there in service, and you got to integrate, you got to build this and you never bring it all together into one single record. And I would claim, and I would love to debate if you think there's something else. The single biggest differentiator with HubSpot is our customer record. And the fact that you can look at campaign information, the last sales conversation, the last support ticket, and all you need is that single customer record. That's the most powerful and differentiating feature of HubSpot. And that's our viewpoint, that that system of record becomes even more smart with AI. And you can customize it, you can extend it, you can drive much more granular capabilities with it, but that is powerful. And so part of what we have done is over the last few years, we have invested in this system of record layer, which is quite distinct from the system of engagement. Which is what, If you're a salesperson, and you need to do prospecting, you need an engagement for it, but that's distinct from the system of record. And so we've done a lot of work.
[00:13:30] The Value of Integrated AI in CRM Systems
Yamini Rangan: I'd like to ask you, what do you think has been the biggest, maybe the most innovative thing that we have done there, from your perspective. But I feel very strongly that these two are distinct. They have unique value, and we've been investing in the system of record as much as we have been investing in the system of engagement.
Connor Jeffers: I think the delineation there is really powerful. It's something I spend a lot of time with, whether it's prospects that are thinking about, hey, we're evaluating HubSpot or it's market analysts, and it's people who aren't as embedded in the CRM space of understanding. The core differentiator is most organizations that are not using HubSpot as a connected CRM have multiple different products, and then they spend a lot of money and time and energy integrating all of those things together. And so what leapt out to me from the AI research that we did is that people found when the AI is integrated and core to the tool that they use, the efficacy of that is so much greater than if it's some side application that's integrated in. But I think if you think about CRM itself, if you have a CRM layer that is deployed across all of your systems of engagement, regardless of the team that's using it, the impact of that AI is far greater than if you're trying to connect it to all of these ancillary systems. And in our research, what we found is teams that have singular platforms that have AI connected in have much better outcomes than those that are using sidecar AI systems that are integrated into the AI that they're using.
[00:15:03] The Power of Customization in HubSpot's Core CRM
Yamini Rangan: Absolutely, absolutely. And I don't know if you remember after inbound, and we've since launched HubSpot AI, and HubSpot AI is in the platform as well as the hubs. I actually asked you this question. Hey, what is the most exciting thing that you heard at Inbound? And your answer to me was the power in your core CRM and the level of customization within the core CRM. That was your answer. Why was that so important?
Connor Jeffers: I think the reason it's so important is every organization is different, and what every organization wants to achieve is different. And I think that the ability to tailor the core CRM infrastructure and platform and your system of record to how your business works and then to use different systems of engagement to deploy that data and deploy that customer record, I think gives you superpowers. And I think that so many companies think about this from a lens of, I want my sales team to do better, I should go buy sales technology versus I want to acquire customers, serve customers and communicate to my customers more effectively. And generally those people have completely disconnected systems of record. And so it's really difficult for people to know what's going on. And I think that there is this wave that's happening in CRM software. And generally of the more connected of a customer record we have, the better experience we're going to be able to deliver. But I think that you see this rise with everyone saying, oh, let's go build CDP or let's go build reverse ETL and data warehousing. And let's try to solve the problem of stringing everything together versus what if we had a system of record that is extensible, it's customizable, and you can be adding a lot of these engagement functionalities on top, I think is a very different way of thinking about and solving that problem. And I think something that a lot of people miss is you get HubSpot's core CRM for free, but also included in every other product. And the value of that core element is something other organizations are selling, just the database. You can have a log into the database and that's the offering. And so I think it's really powerful.
Yamini Rangan: Yeah, absolutely. You get it. You get it.
[00:17:13] AI in CRM: Enhancing Go-to-Market Effectiveness
Connor Jeffers: In terms of how you're thinking about AI in CRM. I think one of the things that you had said to me in a conversation we had was most people are going to experience AI at work, which I think we found in our research is that's where people first experience AI. There's motivated teams we find go-to-market teams in particular are the ones adopting AI at the highest pace. And those that are adopting AI are performing a lot better. So I think that's a feedback loop that accelerates itself. Why is CRM and CRM platforms the area that you're most excited about for AI to manifest and how people experience it?
Yamini Rangan: Yeah, I mean, look, a couple very clear high conviction points for us. AI needs a ton of data and AI needs to be in the flow of work. And if those two things stand true, then we can actually drive effectiveness in go-to-market in areas where we have struggled with quite a bit. So our hypothesis we looked at, like generative AI. We said one, small-medium businesses typically don't have large language model experts. They don't have even just deep expertise in predictive plus generative AI. The combination of both, which leads to value, they don't have all of that. So let's actually democratize it. Then we also said, what does this mean if we bring this into the flow with the insights for a marketeer, for salesperson, for service person, and the level of applicability for each of them is just exceptionally high.
[00:18:43] AI's Role in the Future of Go-to-Market and Front Office
Yamini Rangan: But Connor, maybe one of the things like a year into this journey, I think we'll have few phases of AI, and we're in the very early stages of AI adoption within go-to-market and front office. The first stage that we're all in is really task based. And this is where what you're alluding to, instead of having like a point solution that generates an image or generates a blog, have it in the flow of your work. Where when your job is to create content and you can leverage technology like AI, it's just going to make it easier and better. And so everybody is now releasing beta features and general available features that actually drive those tasks within go-to-market. Whether it's a mass marketing use case of generating blog, or a sales use case of summarizing your call notes or support use case for deflecting a particular ticket, that's phase one. There is a second phase, which is it's no longer about these tasks, which will all become table stakes. It's much more about goals within front office. So think about campaign manager who has a budget of, let's say a million dollars and is trying to figure out how to mix the different social channels and how to mix the level of content that they have. What is the right combination of content and channels that maximize the return for a particular campaign? Well, AI can actually help with that, and that's where we are going in the next stage, which is like much more goal based or agent based technologies, where you can give a series of prompts and you say, here is the dollar amount, here are the channels, here's the type of content, help me land a better mix. And that is all in the flow of your work. It is not a point solution, it is not a go figure this out in a distant place. It is actually in the flow of your work. And that is going to be exciting. And I'm sure there's like a phase even beyond that that everybody is curious about. We'll get to that phase. I think the first two phases of table stakes, task oriented. Second is like agent pace and goal oriented, and then probably even more complex, stringing together of goals that will happen as we progress with AI adoption.
[00:21:04] The Impact of AI on Marketing
Connor Jeffers: So as you said, that, which I think is extremely insightful. What dawned on me is the two things that really form how I think about technology is you have the speed and the feedback loop of communication. And I think the dawn of a lot of digital work is the speed at which you can communicate with another person, and the speed at which you can get something back is really high. I think about saw Napoleon like a couple of weeks ago, but I'm going to send these letters. And so you have the general in the field is writing a letter back to the kingdom, and the kingdom is getting it. So that's a really long cycle time. And similarly, if you are working in an organization where you need an analyst, you need someone to go pull this data, you need somebody to analyze that data, they need to put together a report, you need to discuss that report, you need to come up with how it's going to impact and guide your campaign strategy. What ends up happening is not that none of those people have anything to do. I think, to your point, if you're a campaign manager and you're looking for that data and you're trying to make the best decisions in the flow of work, the efficacy and speed with which you can answer those questions, make those decisions, and actually get the manifestation of that work into reality increases dramatically, and therefore your productivity increases. And so my assumption here is that that just means everyone does way more stuff or everybody does way more quality stuff. How does that impact both what people are working on and what comes out the other side?
Yamini Rangan: Yeah, I mean, look, we had this conversation right around Inbound, and my point of view is this, there is a downside to all of this. If we take the level of intelligence that we have right now and we just take that speed, right, and just take that speed factor and we start iterating really fast and creating a lot of content, that's just spam. And the world doesn't need more spam. We've already have enough digital garbage coming at us that I don't think the purpose of AI is to just increase the speed and drive a lot of volume of content, which is just spam. My perspective is we're in the age of intelligence, and the most intelligent way to use intelligence is to drive connection. It's just not the speed, it's the effectiveness and the insight that comes out of AI. In marketing, if all you do is generate 20 more blogs, but the effectiveness of each of those blogs and how many people you're able to attract with those blogs comes down, then you've lost the purpose of marketing. And so for me, it is all about what is the level of connection that you can drive with AI. And in order to do that, you have to have all of the context data. We started talking about smart CRM and why having all of the data in one place to begin with, not integrated, not happening once every month, but just to begin with, have all of the data make sense, because when you feed that to AI, you just get much more insight rather than having point solutions that are not in the flow of work.
[00:24:17] The Importance of Data Quality for AI Implementation
Connor Jeffers: Yeah, and I think customer expectations go up. And unfortunately, I think that today, and to your point, before, the speed with which you go from research and understanding into productization is so fast. And previously, if you didn't adopt this technology and you were behind, you're only behind a year, two years, and the speed at which that acceleration happens is so vast that if you are not in an environment that you are ready to deploy and take advantage of that AI technology, that is probably the single most important problem to solve. And I think it starts with having that unified system of record, having a unified view of that customer, getting your data to a place that AI can interact with it. One of the things that I've talked to operations folks about is you are not going to be replaced by the AI functionality. But the importance of clean data connected systems process that generates consistent outcomes that you can measure and iterate and manage and feed to that AI is extremely important because you cannot feed spaghetti data to the AI and expect to get something valuable out the other side. To your point, it's a guessing machine. Its only value is the data that underlies it. And if you're feeding it with bad data, you will not accomplish anything.
Yamini Rangan: Exactly. I mean, music to my ears.
[00:25:35] The Impact of AI on HubSpot's Systems of Engagement
Connor Jeffers: So, okay, we talked about smart CRM. I think it's really important for people to understand the distinction between the smart CRM at its core and then the systems of engagement on top. When we say systems of engagement, that's marketing, hub, sales, hub, service hub. What are you most excited about? And where do you see each one of those hubs embracing, and where AI is going to have the biggest impact on how people are using each one in the future?
Yamini Rangan: Yeah, look, I think it's all, again, going back to our belief.
Connor Jeffers: Yamini loves all of her hub children equally.
Yamini Rangan: Exactly. Come on, you can't do this to me. I love all my children. If you think about our strategy, it's deeply embedding it in the platform and all of the hubs. But I'll say maybe the other way to think about it is what are the most known and what has the most impact and potential. And the hubs fall into different places within the spectrum. I think one of the first use cases that popularized generative AI in Go-to-market is just content generation and it's easy to see why. You take this incredible predictive machine and then you apply it to content and you get something better than what you could have written and that's easy. And so on the marketing hub side, being able to generate content, generate blog, being able to publish socially, we find that the use cases that have repeat usage this year within our customer base is social publishing. It's email marketing first, no surprise, and then it's actually social publishing. And people find absolute value in that. And that's not a surprise, I think, which has the most potential, I believe like sales and service have the most potential.
[00:27:19] The Impact of Generative AI on Customer Service and Sales
Yamini Rangan: And let's take service, for instance. If you think about how to simplify service, it is what are things that you can self serve or your customer can self serve to solve problems themselves? And what are areas with human support that you can serve your customers with. So self service and human service are the two components of service. And we've made as an industry some progress on the self service. We have had knowledge bases, we have faqs, we have communities. You can go and search content and you can do all of that. Generative AI completely helps us leap frog on the self service. How? Because you feed all of that data that you've now had, feed it into generative AI, and now you can have a basic english natural language conversation with it. You no longer are searching, you're not sorting, you're not filtering. And I think the biggest leap frog comes from taking all of this data and making self service go to the next generation. And it's really going to transform how we think about self service. And we're already beginning to see, we introduced, as you know, at inbound AI service agent. That's like one of the first things. But chat bots and everything that people are doing to leap frog with self service is big. The human service is also not getting left behind. There's just a ton that you can take those insights and feed those insights to your customer support or success person that has then a much better connected and insightful conversation. It just made their job better. And so I think what is interesting about customer adoption of service is that if there's an executive within the company who believes in the power of generative AI and are saying, okay, you got to go and do this, we're seeing adoption. Otherwise it's taking a little bit more time and it's like slow and not progressing. So a lot of this comes down to some executive, some leader believing in the ability for generative AI to leap frog their service. And I think that's exciting. And then sales is absolutely exciting. Like I said, I like all my hubs. In sales, it is conversation. Intelligence has gotten completely supercharged because now you can take it, summarize calls, get call notes and the next best action, but then you take all of the connected information about your customers journey, which product did they download, which web page have they looked at, what marketing campaign did they get exposed to? And all of your call notes. And then you can have a much more insightful conversation in sales. So I think all, but known to high impact they fall in different spectrum.
Connor Jeffers: What resonated with me the most about what you just said is I think some organizations will opt for this. AI is amazing because it's going to make it so I don't need any service agents and we'll just self-serve everything. And I think what customers want is I want to self-serve in all of the ways that I should be able to. And as soon as I need somebody, I want to talk to somebody now and I want that person to be educated, I want that person to be informed. I don't want to have to catch them up about what my whole journey is. And I think generative AI and service hub specifically has the ability to both empower that customer to have an incredible self-serve experience, but then also empower that agent to be able to deliver an experience that is hyper informed about that customer and what they need. And that's only enriched and furthered by the smart CRM data at its core. Which brings me full circle. It's like I'm really excited about AI part CRM in general, which makes me happy to be in the HubSpot universe.
[00:31:16] HubSpot Go-to-market with AI Episode Interview with Connor Jeffers
Yamini Rangan: So you've done a lot of research this year. So this year we started the year where most people did not even know gender of AI. And now my mom, my aunt in India, they're all asking about this and you've now done a bunch of research with customers on where they're adopting. You know, is the adoption happening or what do you think, you know, inhibitors of adoption from a customer perspective and where can we HubSpot as well as you a great partner within the HubSpot ecosystem, help our customer s?
Connor Jeffers: I mean, so what we're finding is marketing teams are adopting fastest and the marketing teams that adopt generally are able to achieve better outcomes. I think what we're seeing is the biggest blocker for people is they don't know where to start. And I think that there's a lot of excitement. What's interesting that you touched on is there's an executive in the organization. They believe in this. They know it's important. They want to drive it forward. We're seeing a lot of adoption and otherwise, I think what we feel is people are stuck on the starting block and they feel like there's a lot of opportunity. And in the research that we do, the biggest obstacles are we're worried about security. We're not really sure how to start using this. We are anxious about how this is going to impact, how we deliver our experiences. And I think for the marketing team, it's really easy because everything they're doing is not to say that marketers don't have stress by any means, but it's lower stakes of I'm talking out in the universe, I'm generating content versus a support team saying what if we tell a customer the wrong thing?
Like that's extremely stressful.
And so I think that the biggest blocker that we see is fear of getting started. What I would ask you is, Hubsot's done a lot of work on getting really accessible AI functionality into the somebody, whether they're current HubSpot customer or they're somebody who is maybe thinking about HubSpot, how should they start thinking about how they can be implementing AI into their GTM strategy if they're on the starting block and they're in the. I'm not really sure what I should do first?
Yamini Rangan: Yeah, I mean, look, you and I have talked about this. The questions about data privacy, compliance, those are real. And I think those are like table stakes. And every customer should ask those questions, should know that you should hold vendors accountable for a level of transparency in terms of how your data is used, which prompt data is going where you have to ask all of those questions. And if you don't get the right results, then push for it. So I would start with not ignoring the most important critical questions that are inhibiting customers from adopting and do it responsibly. Now, having said that, there are easy starting points. I think if in your marketing efforts, just as much as you focused on building website content, you should be focused on building conversational content, right. And having a chat bot on your website, and you provide the website content for your customers to come and sort and filter through that provide that same content in a conversational bot that now your customers can converse with you on your website. That's the basic starting point. We did this probably in Q2. Connor, we launched a number of conversational bots in terms of marketing to answer simple questions about our product, about our pricing. And we found that most of the folks that were chatting had like higher customer satisfaction than regular folks that were not using the chat bot. So I do think that you got to get started there. I think in service, which we spent a lot of time talking about taking your knowledge base, taking your faqs, taking your community responses and feeding them again into some kind of a bot, and being very thoughtful about where human assistance is needed versus where self-serve can drive. That's an easy place for you to get started. And then I think on sales, lots of places just providing tools for each of your sales reps to improve their ability to research a prospect, to have a much better conversation, that's a good place, like prospect research, company research, being able to summarize your call notes. Those are easy places to drive much more rep productivity within sales. And if you start there, then you're going to gain confidence in terms of your AI strategy, and then you can get to kind of like the next phase, which is non task oriented but goal oriented AI.
Connor Jeffers: So what I love the most in classic Yamani fashion is if I was to distill everything you just said, it was start with the customer and work backwards, which I think falls into the Yamani mantra of how to do all things. I could spend hours talking to you about everything AI. Thank you so, so much for giving us the time for sharing your insights. You have a lot going on, and the work that you do and decide to do with partners and in the ecosystem at large is one of the reasons that we love working with HubSpot. And thank you so much for taking the time to talk to us today.
Yamini Rangan: Thank you so much for having me, and I love the way you support our joint customers and thank you for the work you're doing in progressing AI within the customers that we serve.
Connor Jeffers: Absolutely. Happy Holidays. I'll talk to you more very soon. Thank you for tuning in to this episode of Go-to-market with AI. This episode was produced by Ryan Gunn, Jordan Mikilitus and Saasly.Video. Until next time, this is Connor Jeffers. Stay curious, stay innovative, and embrace our robot overlords.