Why ElevenLabs Built a Team Around Adoption

Speaker A: Welcome to AI for Business Leaders podcast. I am the founder and CEO of the Other Group, a strategic advisory firm that partners with commerce and AI technologies to unlock revenue opportunities and solve go to market challenges. With 20 plus years scaling revenue teams in consumer brands and high growth technologies like Shopify, I've spent my career at the intersections of commerce and technology. This podcast explores the future of AI through conversations with founders, investors and operators who are building what's next. They will share strategic insights as well as tactics that we can all learn from. Welcome to another episode of AI for Business Leaders podcast. Today we're going after a very critical part of AI adoption. Uh, which is why I'm really excited for this conversation. We have Vanessa Piazante, who is currently the leader of global adoption at 11 labs. Welcome to the show, Vanessa.

Speaker B: Thanks. Very happy to be here.

Speaker A: Yeah, so both of us belong to an AI community called AI Circle. That's how we met. I've attended and also watch the recording that um, Vanessa has hosted for the community. Her experience being embedded in a creative AI technology company is really, really valuable. That's why I'm really looking forward to forward to this conversation. Let's dig right in. Looking at your LinkedIn and also background, you have done a variety of things. Working world simple, then ADA and now 11 labs. So set uh, the tone a little bit. Can you walk us through the thread that connects them all?

Speaker B: Yeah, so I took, I actually, I always say it's unconventional but then I talked to so and uh, so many people have had a very similar path where they started in one thing and then they completely pivoted at one point. Uh, but started my career in finance. I worked on a trading desk in equity trading and thought my career was going to be in investment banking. And that was what my passion was through university, studied finance and was an athlete. And I just loved the competent. What I thought was, you know, a very competitive, high intensity, high reward environment. And so I did get a lot of those things. It was super high intensity. You worked really, really hard. Um, but the one thing I didn't love was just the culture, the red tape and I just didn't see that as being what I wanted to do long term. I didn't really know what the next step was. But long story short, I met a lot of people that worked in tech and this was like 2014, so a height of a lot of really amazing companies in Silicon Valley. I ended up in San Francisco just traveling. So I decided that I wanted to go into TEC and so wealthsimple I actually landed there when There were about 12 people and started there only because. Well, one of the main reasons was I wanted to leverage my finance background and so I was exclusively looking at fintech companies and um, wealthsimple was doing some really incredible things that I really bought into. So yeah and, and you know it. Fast forward to today. I've spent the last 10 years leading and building uh, customer facing teams from scratch. When I was at will simple, I kind of did a mix of everything and the team was really early. I was answering custom support calls, I was helping people get their RSP set up which is like the Canadian version of your like 401k. And it was uh, you kind of did everything and anything but it was like start manual and then you try to automate it. So that's what like the, the underlying theme there that I kind of took throughout my career. The other thing there that I, I really learned even though it was really early career was the value of experimentation and taking risks. There was a ton of really amazing leaders that were at the company that you know, pulled off some really cool creative campaigns. We were one of the first companies to take out an ad during the super bowl in Canada and they did a whole blog post on like the economics of doing a Super bowl ad as a Canadian company on Canadian television networks which anyways, all those things were really, really cool and they, they ended with a lot of Canadians. And you know, fast forward to ada. I also built out their enterprise customer experience. Fun, uh, lots of similarities there in terms of experimentation. Really high caliber people. I would say like very talent dense. Talent dense was not. Or talent density was not a term that we used back then but I would definitely say it was one of the, the more talent dense organizations. And funny enough there's quite a few people that have landed at elevenlabs that used to work at ada. So ton great talent out of North America there and actually globally as well. And you know the, the ties between Aida and ElevenLabs, you know Aida was super ahead of time in my opinion. They started shortly before the pandemic and really, really accelerated when every single company overnight had to figure out their digital first strategy. And so we were really the company to help them do that from a automation customer experience perspective. We just did text at the time so it was chatbots but that was needed. We uh, worked with some really, really incredible companies that had to scale their own operations and we were really like a bit of a savior for them. So you know that, that level of intensity was something that I really, really building at that phase of, or that piece of growth was really incredible. And I'll say, you know, the reason I landed at 11 labs was because I was trying to chase that pace again. I landed at a few companies in between that didn't have the same level of intensity. And I was really looking to get back into an AI native company that had, that had the talent density and was solving a problem I was really passionate about. Ultimately, I think the. When 11 labs pivoted to conversational AI and agent, it really spoke to me because I had done a lot of that at Aida. And um, I would say over the last, you know, almost two years here at 11 Labs, we've gone through a lot of the same journey that I experienced there, but at a much faster scale than what I experienced. So yeah. And here I am today. I built out our customer experience function on the uh, international side so everything outside of North America and now stepped into a new role leading our brand, uh, new adoption team.

Speaker A: Yeah, this is really awesome. I think this is for me at least the value of having conversation versus looking at somebody's background through like LinkedIn profile or just a piece of paper. Right. Like if I can just ask the question. It's really interesting because now I can see the thread. If somebody start a career in uh, investment banking, obviously there's a, it's kind of like a good training ground for when you, when you were talking about intensity. Right. It sets you at least a baseline of what like hard work actually looks like.

Speaker B: Yeah.

Speaker A: You talked about and it makes a lot of sense. It sounds like a lot of places that you've been with. You were taking on roles of building from 0 to 1 or 0.5 to 1 or 2 and so on. And that ah, is sort of the stage of intensity. What, what does that mean for you as far as looking for that intensity? What does that intensity mean to you as far as pertaining to 11 labs?

Speaker B: Yeah, I think there's a few things there. I think intensity comes out in a few different ways in at 11 labs. It's not just, it's not the intensity that like, you know, you're, you're super stressed all the time and you know there's someone looking over your shoulder watching your work. It's more of, you know, everyone's on this like mission driven journey where we're all bought into the mission of what we're trying to achieve. There's actually like very little, uh, I would say a lot of people have tons of autonomy Here. So you, uh, I was talking to colleagues the other day and we're just saying how like no one is actually asking us to do a lot of these things. It's very like we're very self sufficient and high agency and you know, you see a problem and you want to go and solve it. And I think the competition that we're, that we're experience at any AI native company, but especially if you're, if you're building models, it's, you know, you have to, you have to be intense. It has to, you have to like work towards building the best model or going to market the quickest or going to market in the most efficient way. So there's that level of, you know, it's, it's really, you know, high agency and the pressure that you might even just put on yourself. Also think that the caliber of people. Yes. Is, is really, uh, really high here. But I would say biggest thing that I've seen as differentiating people that are great versus people who are like good but you know, they might not have that level to survive in these environments. It's like that sense of urgency because that sense of urgency is so critical and you either you don't, you can, I think it's like an inherent thing that you have like, it's like, okay, you have to do this thing, like why not now, why not tomorrow? Um, versus like in a few weeks from now. So I would say that everyone here has that sense of urgency. If there's something to solve, it's like we need to solve that now. Customer is reaching out, we need to respond to them as quickly as possible. Not right away but. And you have to prioritize, of course. But how, how do you like bring that sense of urgency to everything that you do? And I think that creates a kind of an intense environment for a lot of people.

Speaker A: This is really interesting. I'm also a fan of subjects and topics around people and management. We'll probably save this for part two. There's quite a bit of stuff we try to cover, but so most people who work in tech and also AI, they hire the alumin labs, they understand the product and they follow the path and journey. Those who are not as familiar with the company. Can you tell us a little bit about what 11 Labs is? And also you know, from an agent platform standpoint, what is it actually being used for in production and what does it deployment usually look like?

Speaker B: Yeah, so 11 labs actually we're a research first company so we actually build and train the models that we're bringing to market on the voice side of things. So actually a lot of people still know us as just like the voice provider voice models, but we do so much more now. When I started, we were just on the brink of launching our agents platform. But I would say that a lot of our earlier customers would come to us because I always like to use examples as to, to illustrate what, what we do. But um, everything from dubbing, we have amazing dubbing models for customers who want to dub either, you know, different movies from, in different languages or broadcasts things like that. Funny enough, our founders actually stuck. That's one of the reasons they started. They're ba. They uh, were high school friends from Poland and they were sick of going to watch the movies that were all with the same voice actor that I

Speaker A: heard, I heard that story of the podcast. That's funny.

Speaker B: You heard it. So it's, so it's just really interesting. Like that's how it, the, the starting origins of it. And then you know, we moved to our text to speech models and speech to text. So you know, we have everything from customers orchestrating their own experience via our models on the, on the custom experience side of things to um, people that are using this in, in different animations and media. And so we've, we've really like evolved the way what we can do. But I would say that the number one thing that we're focused on now is our agents platform. And it's not just for customer experience, but across like any, any customer that wants to give their brand a voice and their customer experience a voice. So that could stem across your customer support, but it also could be in your outbound strategy. Your inbound strategy. We have tons of customers that are using, you know, uh, debt collection or triaging inbound leads from, from a sales perspective. So booking appointments. I actually used one of our customers platforms the other day that has built an agent to um, book appointments for the doctor. So there's a ton of different use cases there. And so we're really helping customers, you know, deploy what good looks like and where they want to automate their own customer experience or their own brand experience. In terms of your last point of like, what does a deployment look like? It could look, so it could look like so many different things. I would say the most, um, the normal one is, you know, deploying for more of a customer experience use case. And you know, we have everything from mom and pop shops to large global brands that are deploying Elevenlabs. And so the level of effort and what deployment looks like will be different for each of those organizations. I think the interesting thing is, you know, just seeing how that that space has evolved over the last, you know, eight, eight to 10 years and working with different partners as well and integrations and legacy players that, you know, moving from them to, from a legacy player to a brand new way of working. So it's been interesting to watch that journey, especially having seen the earlier stages of that transition um, while at ada.

Speaker A: That's really good. I know. I think at a high level there's been a lot of AI technology companies that are helping the frontline customer experiences. Right. Uh, different enterprises or call centers or customer support. In your opinion, when the agents or the voice agents are being put in front of millions of customers at that point, what changes from experience standpoint? As you know, let's just say if I was the customer, am I calling instead of having a human on the phone and now I have voice agent. What are some of the benefits of that?

Speaker B: There's a ton, a few that I would highlight is a lot of our customers have of operations that only run at certain parts of the day because they're not able to staff at certain, you know, night. And being able to run your agent 247 especially I was looking at a customer earlier and they have like inbound triage for emergencies, for like healthcare emergencies. And being able to run that overnight to triage to the right person that is maybe able to help in an emergency is, is really uh, critical. And so being able to offer that to people, to your customers at any time of day is really incredible. I would also say that the ability to like launch your brand in so many different languages. Imagine you know, you're, you have your grandmother that only speaks, doesn't speak English and they're able to reach out to them in their native language. And so I would say like not just in North America but in other parts, parts of the world where like different dialects exist as well. And being to have an agent that speaks a specific dialect or the specific language. In India there's 19 languages there. So there's just so many different benefits from that perspective. And then the last thing I think is the flexibility of you know, I think this has been the case for, for agents, chatbots in general for a while of using your humans wisely. So how do you reduce the redundant tasks that agents are maybe answering today? Allow them to work on the higher value content, the higher value inquiries and routing when it actually has to needs a human versus having them spend hours on low value Inquiries and not really like folk being able to answer some of the higher value complaints or ability to like close more deals or whatever it is. So there's a ton there, but those are the three that typically stand out to me with our customer base.

Speaker A: Yeah, that makes a lot of sense in the world that we're in right now. It's really easy to build products, right. Uh, it's easy to sort of just come uh, up with the next uh, version of the models or adding features. And I think on the commercial side it's harder to tell volume and also quality at the same time. Which kind of going back to why you know, your new role exists, uh, when it comes to adoption. Right. Really connecting the both worlds and your role is very important. And I also don't think a lot of technology companies have those roles as well. Tell us a little bit about like your new role. How did it come about and why is it important at this, at this time for 11 labs?

Speaker B: Yeah, it's interesting because there was a period of time where a lot of companies were like, we don't need CS anymore, like we can automate all of this. And I even heard a podcast this morning, they were talking about it and sure the old version of what CS was, is, is not, not as important anymore and you can kind of COVID that with other functions automations. But, but I would say that. So when I started at ElevenLabs, our team from day one was a revenue team. Even though we were called co success at the beginning, we always owned the commercial side of things. So expansion, renewals, cross selling, including the negotiation and contracting, which isn't as common for a traditional CS team to own. We were always, you know, we always held a number and it was great for us because we were able to build on a really incredible team that drove a ton of revenue, ton of NRR for our business. But with that, that intense focus on the revenue side, we started to see this gap emerge on, you know, how are customers getting on boarded at the front end of the journey. We were trying to do a lot of different things but having one role do everything post sale is kind of impossible. And so I originally thought like maybe this gap is here because of the team being fully measured on net revenue retention and expansion. But actually after talking to a few other peers that uh, are CS leaders at other native AI companies over the last last couple weeks and months, it's, this is a problem everywhere. And so all of these organizations are starting to think about like how do we either solve this with the existing Team or spin up a new team. So at 11 Labs we decided to spin up a new team. We have uh, an incredible for deployed engineering team that works like boots on the ground with our strategic accounts and some of the larger enterprise accounts where you really need to work with them, um, side by side to deploy agents. And in a lot of cases we're building with them, um, building for them lots of custom uh, type of deployments. But for everyone else that doesn't need something custom and can kind of use our out of the box solution, that's where this, where our adoption team comes in. So our job is essentially like how do we get our customers from uh, they, we start like meet them when they start building to the point of production and beyond and think that you know, in the old world you may have had like someone in professional services or um, a customer, even a customer success manager working really closely with them but that's like not scalable. We have hundreds of customers that are coming on board and that we house that we have in our existing book. So the other half of our role is like not just helping them adopt the product as effectively as possible, but how do we scale that, how do we build the systems and the framework in a repeatable way that we can scale this across not just our agents, customers but our customers coming on board with our API product and our creative product. So um, we're in the very early days of this but you know some interesting learning so far is while the you know previously we were really looking at usage from a what good looks like, you know, if they've used this amount, this like they're in good shape. But in this hype of world of you know, like you said, like you said there, it's so easy to build a product. There's so much competition and so we're um, starting to also measure the breadth and depth of how they like actually enable parts of the product that we know are best practices for that particular use case as well as you know, is it in production and are we seeing um, the volume increase over time? Are we seeing more, are we helping more users within their organization build and getting like wide within each, with within each customer as well so we can start to inspire them on new use cases um, and try again trying to automate as much of that as possible.

Speaker A: That's really great. I have a soft spot for CS leaders and CS people I think just biasely obviously I was a shopify leading cs. I do see a lot of native AI companies shifting or evolving. The nature of the role given that it's entirely new. So sounds like you're doing the same. Have you guys had any sort of delineation in terms of where that handoff or where that collaboration can come in between your organization and C?

Speaker B: Yeah. So early days of figuring that out but the team we, and knowing that like even the, the rest of the CS team which we're all we're calling account management now, they were you know, very vocal about this being a gap gap. So I think they're righted that someone is going and solving it in terms of the collaboration. So I mean I think the messaging is important especially when you bring a new role into an existing organization that's uh, like and part of the team already being quite established just showing like how is this going to help them hit their, their own goals. And so we haven't changed in terms of what they're responsible for. They are still responsible for driving net revenue retention overall, uh, retention from a renewal perspective and cross selling. Cross selling has actually become a really big focus for us as a business. And so showing them exactly like this is how we're going to help you do that. You no longer have to worry about the front end of the journey as well as when we cross sell to new customers. You know someone being there to support implementing that or you know scaling the content that will help the customer implement that effectively. And so there I can already tell like we've only been a team for about four weeks, five weeks and like you know their excitement of having this like extra layer of support to get in our customer base so that the we, they still own the relationship. Uh, but we're going to work very closely with them um from a cohort perspective in terms of like scaling this is what we know works for this group of customers. We understand these use cases really deeply. So we're going to deploy this type of, of framework and motion for this type of customer and this type of use case and then partnering with them on the our existing book within each region and knowing that you know in India very API first market. How do we deploy either um, a cross sell motion for other products that we have or become experts in deploying API first products. So there's a lot that we can do and I think each region will look very different but that's kind of how we're, we're starting and um, I think underlying thing is making sure that they see how this is going to benefit them and to be able to work together and identify opportunities together.

Speaker A: It's so interesting I think the world of uh, you know, a lot of native AI companies when you first start up, they're trying to figure out what that organizational chart look like. Is it more similar to like traditional SAS model or is it different? I think people are starting to understand that there's a lot of understanding, enablement, education that need to be done on the customer side of it. So you're shifting all that resources into more of that versus, versus just sales team. Hey, let's build a big sales team. Right? It's like, okay, well hold on, let's pump the brakes really quick because we need to make sure like the, the customers that bought product they're successful to begin with.

Speaker B: Yeah, yeah. And it's, yeah. And I, I would like, I would argue that we need to re. Evolve every six to nine months. I think that this, what we're going to be doing now, um, is going to be very different in six months from now. And I even put in our, the job description for this role. Like this is a new role. Expect the scope to evolve quickly because that's just you know, at working at any AI company today, like you, you pivot, you switch the responsibilities, you switch the scope because you have to be able to adapt quickly to competition, um, company strategy, model strategy. So I mean it's, that's just the nature of, of this world.

Speaker A: It is very telling of the type of talent that's also needed somebody who perhaps more suicide, who can be adaptable, different type of situation and go deep where they.

Speaker B: Right, yeah, absolutely.

Speaker A: If we shift the lens a little bit more towards like the customer side, it's still early days for your role and your team. But if there's any sort of example of use case of uh, what a good deployment look like with a customer just from a sort of end to end. Right. Because I can just imagine the complexity of that. There's also I'm sure multi stakeholder management on the, on the client side. So yeah, maybe all cases are different but if there's an example.

Speaker B: Yeah, I think you know, what we're doing, um, is going to look different from your traditional like enterprise deployment. And but I can, I can speak to both examples because I, you know, I've watched our FTE team lead a lot of really amazing deployments with companies like Stripe and Klarna and others. And I think you know, the work that they put in, I, we're, we're going to figure out like how do we scale some of that? Not all of it, but I, for those deployments, the things that are important and I think it's true for any deployment. And I don't think this is as like different from a lot of things that have been done in the past in terms of like what does good look like for deploying any type of software or AI within an enterprise. I think there's three pillars that to me are really critical to nail and one, one is obviously the product. I think that understanding like how does the product fit for that particular use case, can we do an out of the box type of deployment or is this going to be something that is going to need custom work and the level scoping that happens around that is going to be really critical. Um, but that's just the beginning. A lot of These start with POCs and trials of use cases that are maybe a little bit easier to deploy. I also think then the second layer to your point of the stakeholder management and the people side of things is really critical. So uh, it's not just about us and like us doing the work, but making sure you have really really strong counterparts on the customer side that can also one help you navigate the, the politics especially if it's an organi, a huge organization. The like what's, what's able to actually happen with their current tech, stack, security, compliance, all of those things like you really, really need to have all of those stakeholders mapped out and knowing like who are. It's not just one champion, it's like multiple champions that you need to build over time and in addition to the final decision maker. But that the whole you know, influencers champion decision maker and understanding that up front is so critical cycle and again that that's been cased for years and years in terms of like stakeholder mapping. And then the last thing I will say uh, especially for these larger deployments is the change management. I think now more than ever change management is so critical like just on the people side, it's not just on a technology side. It's like the enablement. How are your workflows going to change? How are you getting buy in internally to make those changes to the way that you're operating? How is this change going to impact your customers? Like all of those things are so important and I think with AI even now I remember it when I was at ada we had to go on site with a customer that was a call center out of Jamaica and walk them through, you know, why this is actually going to make their lives better and not take their jobs and because there's uh, that, that high level of anxiety on oh My gosh, this is going to replace me. And in a lot of ways, yeah, it's going to replace a lot of the work you do, but it's not necessarily going to replace you. So anyways, there's, there's so much there. Um, and I think any strategic deployment is going to look different, especially with, I've seen POCs that were running and then there's like two other POCs getting run with two others at the same time. So there it's, it's just such a different environment than what I've seen before on side in terms of what we're doing. I think what we've done over the last month is a lot of just data analysis of understanding. Okay, okay. What do best in class deployments look like based on what the FDE team has already done? What are the top features being used for the deployments that we deem as the most successful and what are all the features that are enabled, what does volume look like, et cetera, et cetera. And then coming up with like a work back from that, how do we work back from that point of getting every customer to that point and also having an opinion of how they should do this based on their use case. So we're trying to scale that out and so we're actually deploying our first cohort on next week. I have a very specific strategy of, you know, we have two specific use cases that we're deploying with our like they're our highest volume use cases. How do we get customers from, from point A to B in a scalable way where like we're coaching them milestone based triggers versus just like dumping a bunch of content on them and saying okay, you've done X, Y and Z but you haven't done this. This is why it's important, important for your use case. So, so doing a little bit more of that and you know, help working with our, our engineering team to build some of this into the product of the things that we see as like low hanging fruit. Um, and again it's all about scalability and then working in lockstep with our FDE team to like take their learnings and scale it and then take what we're learning at scale and give it to them to bring into their own uh, enterprise and strategic deployments.

Speaker A: It's so funny listening to you describe the processes and what you guys are looking at, just the transformation itself. I feel like the, the learning of psy psychology has become more and more important across commercial organization all the way from sales, all the Way to post sales. Right. Because so much of it is because we're so new with the technology. It's so much about influencing behaviors and changing behaviors and changing mindset.

Speaker B: Absolutely. It's yeah it's really, it's really interesting to uh, to see that all at like just a. Such a faster rate.

Speaker A: One thing I think a lot of people always are interested in is AI adoption is one thing. For a lot of non technology companies or leg companies in legacy industries industries they're always wondering for native AI companies what does that AI adoption look like? If you could talk a little bit about it from a commercial organization. I'm sure the coding team is using uh a bunch of AI tools but for commercial organization how have you guys adopted AI internally?

Speaker B: Yeah, I mean I think we've evolved that as well over time. The I would say like our whole company, the most common tool everyone's using is cloud code work. Everyone is literally using it for every, for everything. And you know really interesting use cases. I mean you have your basic ones of triaging your inbox and, and creating alerts for yourself and things like that but some more tangible ones that people have used and I've actually created one that aggregates all of the products updates across. We have so many different Slack channels and like aggregating all of the product updates that are relevant to our team and dump in sending it to them every week just to have like little bites of size and pieces of information and then the customers that are. It's relevant to and to be that together really easily. It's gets it pulls from GONG calls and product feedback and things like that. So it ties some of the new launches we have to the pro to the customers that uh, it's most relevant for um and all the way to you know creating a ton of different assets and dashboards and just things that we wouldn't have been able to create um in the old world by ourselves and you would need support from teams that didn't have the time. So we're using a ton on Claude uh and a ton of other tools like half the organization has created like N8N workflows and um, I think you're, you're you know very common uses of like Gong which they have a lot of AI features and things like that big Slack organization. So anyways a lot but these More like legacy SaaS companies have built a lot of AI capabilities internally but we're trying to, we try to bring it all together into Claude and um, the different MCPs that we have available with all of the different products as well. So that is I would say like bucket one, bucket two is like we have gone full on in like how do we use our product to solve our own problems? So we've, we've really embraced solving tons of problems with 11 labs, whether it's through our creative platform or our API products product, but more so with our agents platform. So our talent team has created an AI recruiter. So anyone that wants to talk like learn more about 11 Labs and the roles open, they can talk to our AI recruiter which is one of our GTM lead recruiters, Becky. It's her voice and so you can actually chat with Becky. I was inspired by that and actually created my own agent for my new team. So um, people that were interested in joining, they could learn more and you're able to like give so much more content contacts versus like I don't have time for a 20 minute call with hundreds of people but it had about 900 conversations and like 40 hours of call time with people around the world and was able to answer questions about how uh, you know, how I operate, my leadership style, what requirements for the role, how to stand out, things like that. Um, we have the aicm, the AI cr and these are also things that are like helping, helping our internal teams become like take a lot of the redundant work off their plate and the low value inquiries and be able to focus on the more strategic work. So it helps, helps um, everything from triaging to the right CSM or SDR to like gathering some of that information and getting context from past conversations and figuring out like does this actually need a human to answer or can can me as this AI CSM answer for the customer. So tons of different use cases and using our product to solve that. And I would say the last thing like even our marketing team is using our ads engine product and um, our creative studio for making videos, making content. Like we, we use our products so holistically across our organization. So I think that's really critical to like the whole concept of dog fooding and using your product to see solve, to solve your problems.

Speaker A: Yeah, no, I love it. Obviously you know I'm a fan of, I'm a fan of just the voice technology and voice AI and that's why you know I'm, I'm an avid listener of podcast and doing podcasts. Yeah. I think the application's so wide even to areas where we never imagined before.

Speaker B: Mhm.

Speaker A: But before we wrap up, you know, fun question. What are some of your go to AI tools Right now, like, what do you use day to day?

Speaker B: Well, funny enough, I just got a notification on my computer for something that we haven't launched yet publicly, but it's our Scribe product, so I don't know if you've ever used Whisper Flow to like. Yeah, so very similar.

Speaker A: Every day.

Speaker B: Yeah. Yeah. So I would say that's probably where I spend mo, like use the most. It's talking to my laptop. I barely type anymore. It's pretty insane. I hate. I actually really hate typing now. It went. It was usually like my phone before where I would just talk and dictate, like do voice dictation. Now I talk to my laptop. I will never go back to typing. So that's, that's one big one. Um, and we're going to be launching that soon. And then I mean I use quad just personally day to day. Everything from like trip planning to like even my own personal health of like. I have a whole workout program that I've created for myself and I use. I've built like a Claude HTML page to track everything and give me like uh, to track my progressive overload and things like that. So I would. Those are, those are the two, two tools that I use most often in my day to day. I'm trying to think if there's anything else that I haven't mentioned. Um, but I would. Yeah, those are the two that I am using like religiously.

Speaker A: Yeah. No.

Speaker B: What about you? Any, any that, Any. Any that I should try that you're using day to day?

Speaker A: Day to day. Like you mentioned Claude. So I use Claude to basically run my business. It's essentially, essentially so the brain. Right. And, and it's so good. And you build agents on top of that. I also use Whisper Flow to your point, I think value, like you mentioned, obviously there's the time factor of like typing and just voicing and talking. But what I found, a huge, huge value from using Whisper Flow is just the context of like the questions or things that you try to talk about. You can speak more words than your typing, obviously, because typing I think is just a different mode of communication. Right. And I think that helps a lot on just the Asians and other things that we're kind of building and use quote, vanilla quite a bit, you know. Right. But what's really interesting, what I found though, we'll see where this goes and inevitably this is going to happen is Whisper Flow right now also has a recording feature. Right. For different calls, which is basically. Which is now you're starting to see sort of overlap and this is sort of geeking out, uh, on that technology. I used to use Fireflies before for recording, but I don't use it anymore because now you're starting to see that redundancy, the overlap of features and then you're trying to figure out, well, what's the most effective a cover all type of like product you can use. Yeah, that and maybe it's something fun, but it's still Claude. So I built a executive coach for myself using Claude. I found, I found it really useful. Solves a lot of problems. I have had coaches before, but as everybody knows, you're not going to be able to reach that human all the time, especially the time that you need it. That solves the problem.

Speaker B: Yeah, absolutely. That's amazing. I need to, to build one for myself.

Speaker A: We can trade notes then. I know we're, we're at time, so I really appreciate you taking the time to chat and also share your insights, your experiences and, and your knowledge. And for folks who want to connect with you further to learn more about eleven Labs or just your, your, you know, uh, your background, your experiences, where should they, where, where should they find you?

Speaker B: I'm Most active on LinkedIn, so definitely, yeah on LinkedIn and is the best way.

Speaker A: That's right. To everyone listening, if you found this valuable, subscribe to the podcast and share with other leaders. Navigating the intersection of AI and technology. Thanks for listening and we'll catch you on the next one. To everyone listening, if you found this valuable, subscribe to the podcast AI for business leaders on Spotify, Apple podcast, YouTube and leave a comment. Please also share with others. Navigating the future of AI. Thanks for listening and we'll catch you on the next one.