AI Should Augment Sellers, Not Replace Them

Machine-generated transcript from the episode video. Speaker labels are not included and some names and product terms may be transcribed phonetically.

[0:00] I've learned that the best founders are always selling. Welcome to the GTM Vault podcast hosted by Rick Kleta. We uncover the strategies, the pivots, and the breakthroughs turning startups into giants. Let's crack open the vault and find out how. Welcome to the GTM Vault, where we uncover the grit, genius, and go to market strategies powering the tech world's most brilliant minds. Today's guest is a visionary redefining how businesses scale without sacrificing humanity straight from the heart of T Aviv. Alobar Joseph is the CEO of Swan AI, a startup using AI agents to identify high intent leads and supercharge human-led outreach. Here's what makes him unique. After selling two automation focused companies, he realized that scaling often means losing the human touch. Now he's on a mission to prove that AI and authenticity can coexist. From advising Fortune 500s at

[1:04] KPMG to building startups acquired by industry giants, Amos has cracked the code on blending tech and empathy. Amos, welcome. Thank you, Rick. I'm super excited to be here. Awesome. So, let's get right to it. After two acquisitions, why pivot to building AI for human-driven sales? What gap did you see in today's GTM playbooks? It's a that's a great question. So Swan's mission is, you know, to leverage AI to make sales human again. And we set out to achieve this mission because in the last 20 years we've seen that the amount of human interactions for the go to market team especially for sellers is just you know reducing consistently and system interactions is scaling and scaling and you know CRM cames with the pro promise of managing helping sellers managing relationships right but what they did is sellers spend more and more time working with their CRM and uh investing time in their systems, right? And then came

[2:07] sales automation. And sales automation also promised more interactions with humans and and buyers. But what happened is that sellers spent more time with systems. And today, um, each seller spent around 60% of their time just working out with their systems across 13 different tools. And now with AI, we're at an inflection point where we need to ask ourselves, are we going to use AI to take human interactions to zero and system interactions to 100%. Or are we going to leverage that technology to take everything and spin it around and increase our human to human time to 100%. So sellers could focus on their buyers and on their buyer needs and on their human relationships. And that's what Swan Eye is about.

[2:55] You said scaling sales destroys relationship. How does Swan AI's approach differ from traditional sales automation tool? Yeah. So I think that the first wave of AI tools was really about taking an existing role and try to replace it with AI basically. So we had uh customer support. So a customer support rep an AI agent that does that AISDR etc. And now we're witnessing kind of the second wave of this AI technology with Swan is part of that wave. And that second wave, what is different is that it tries to reimagine how human and AI could collaborate together to scale the amount of human to human interactions.

[3:41] And so we built Swan with the seller in the center of the solution. And we thought how AI could empower the seller so that it can focus on the most important conversations at every given point of time. And so Swan's entire solution and user experience and entire products is really an AI agent that is designed to help the seller, not replace it. Swan AI promises pipeline without the spam. How do you balance scale and personalization when leads are drowning in generic outreach? one we have a different approach to um you know sequencing and to outbound and to you know going to market by large we believe that at every given point of time only 5% of your market is actually in the market right and the rest the 95% aren't and no matter how much uh emails your SDR will send they will not be able to move this account from you know not in market to intent Basically and our

[4:45] approach is we enable the sales team to always reach out to an account when there is relevancy when there is something happening within that business that is relevant to you. And what we tell to our sales team is only try to create awareness and when the time comes within that business they will reach out and tell you yeah we are ready. So the way that Swan works basically it monitors all your accounts and it enables you to manage all these couple funnel relationships in parallel basically so that whenever a business event occurs within that account or within a specific prospect's daytoday you can reach out with one or two touch points be relevant showcase your solution how they could help and create that awareness and then the moment that it hits the perfect timing that prospect will raise their hand and say Yeah, we want a demo. But if the timing isn't correct, you seize an amazing opportunity to tell your story and to be relevant to that business and to what is happening within that business. And so

[5:47] what we're imagining basically what we call sequences 2.0. It's not about trying to find the best timing to reach out to an account and when that timing happens, just blast it with 30 touch points across 45 days. It's not about that. It's taking a list of maybe like 1,000 accounts and trying to manage the these top ofunnel relationships in parallel over the course of a quarter over a year maybe. And every time there's an opportunity with one or two touch points. And what we saw is when we moved sales teams from this spray and prey and hope for one signal to win that account to actually uh a more you know uh long-term relationship with that account trying to nurture them. We we've saw that um reply rates increased dramatically and conversion rates increase dramatically and resource utilization also increased dramatically because SDRs and sellers focus all the time on the best opportunities within

[6:51] their account list. I love that. I love that. Having managed STR teams in the past, I can tell you know wholeheartedly that it's all about identifying who's in market and being able to prioritize those accounts so that we're not wasting our time and where we know exactly who could be who is more likely to convert, right? Who's more likely to respond. So that makes a lot of sense. I ag I agree Rick because prioritization and lead scoring was like the byproduct of human capacity to manage a limited set of accounts at any any given point of time right and so we had to prioritize the accounts that we're working on but what we try to reimagine at Swan is what if we break that human capacity and today reps combined with AI agents can now manage an infinite number of topfunnel relationships So you don't need scoring, you don't need prioritization. What you need is just raise an account at any given point of time when there is a good opportunity

[7:54] to reach out and the agent will give you all the context you need for that at specific touch point at that specific moment of time and you can reach out to them and then there's another opportunity. So you can cover maybe hundreds of opportunities at a given week and then maybe the next week you don't have a lot of opportunities so you will cover just you know 10 or 20. That's it basically. And so it's not about how many activities you pour in to the sequence. It's just about how much accounts are in market, how many accounts are actually having an interesting event that you can reach out. And so this notion of prioritization and scoring is actually substitute with an AI agent that surface the right opportunity at the right, right? And not to mention uh the the lashback you get when you're targeting accounts that aren't in market and aren't just not interested at that point, right? It's almost having a negative impact on your brand. Whereas with this type of a solution, um there there there's none of that.

[8:55] It's just targeting prospects that are more likely to respond. So you don't need nearly as much of a human human team and it's all based on intelligence and intent. And I love that approach. And when we when we think about your team's goal of making 10 million ARR per employee, this seems a lot more likely now when we look at the approach that you're taking. And how do you maintain this ruthless focus on efficiency without burning out? Yeah, it's uh it's the biggest challenge I would say when you're three founders trying to get to $30 million AR with no additional hires scaling becomes super challenging.

[9:41] What we found that works for us is that every process, every scaling process, we start with doing it completely manually. All of the founders are involved in that process. They try to um make the best work out of it and then once we figure how to do it in a repetitive way that actually works, we document it and then we try to teach an AI agent how to do at least 80%. Not 100% but like at least 80%. Um and an amazing example is customer support. So at the beginning the founders took every customer support request uh and Slack jumped on it and basically started to document the resolutions and then quickly we added an AI agent between the user and the founders. So it would just be able to transfer the messages, transfer the support requests and take our resolutions, make them more um intelligently written and documented in a knowledge base. And then over time

[10:45] what we had is a knowledge base of all the resolutions that we wrote to the customer. And now the AI agent what it does basically it takes the user request it looks at the knowledge base try to understand if it has the answer to that user request. If it does, it will respond immediately. If it doesn't, it will escalate again to the founders. Founders will write the resolution to the agent. The agent will pass it to the user. We'll document it in the database again and then for the next time it will be able to actually resolve that specific query. And so what we've developed is kind of like a self-learning support system that every time the founders gives their input, the system learns and can resolve that issue on their on it. Got it. and everyone claims to track high intent leads. How does Swan AI cut through the noise to identify real buyer readiness?

[11:37] Yeah. So our approach as I've mentioned earlier in the conversation is not about having kind like the perfect timing to reach out to an account but we realized that you know intense data has been around for like a decade at least and it's the you know it's the golden pot at the end of the rainbow but basically it's super hard to actually provide consistent intent data at scale and what happens with all of intent data is that you know sales teams lose trust and they feel like it works like a Russian roulette. Sometimes it works, sometimes it doesn't. So our approach to intent is it's not about if the account is in market to buy your solution right now.

[12:22] It's more like this is a great opportunity to showcase your solution to that account. Maybe they are more up the funnel. Maybe they are still in the awareness phase. But if you'll reach out right now, then you'll be able to tell your story in a relevant way that would actually provide value to the account. So once they are ready, they will know you and they will always take they would already take you into consideration. And so our our solution is have more intent signals. Try to have more touch points. Try to manage multiple accounts in parallel. not multiple but hundreds or even thousands of accounts in parallel instead of betting your entire sales team resources on a single intent account that is popping in a you know a single dashboard basically. So it's not about that. It's about seizing great opportunities and in parallel simultaneously and trying to move accounts down the funnel. Yeah, this this is all moving really fast. Where do you see AI driven sales in three years?

[13:21] Will reps become AI whisperers or will tech replace them entirely? Yeah. So, I feel like that's the hot debate in my uh LinkedIn feed these days. But I would say that people are thinking about it completely wrong. It's not about replacing sellers with AI or sellers controlling an AI and becoming kind like an a GTM engineer. Actually, the solution is in the middle. Basically what we're imagining in Swan is a world where human and AI collaborate together. Every team consists of sellers that work together and every seller has different AI agents that helps them you know take the tedious work replace their system interactions but these AI agents will never replace their human interaction.

[14:10] So what will happen over time is that AI will take more of the mundane tasks and more of the you know analytics and data oriented tasks and sellers will move towards more strategic work, creative work and relationship management work and basically we will see kind of like this new type of collaboration, new type of teams working together where sellers are in the center of that team, agents are around them but they are collaborating together and what's your advice for founders aiming to build capital efficient high impact teams in the AI era? Yeah. So I would say that before AI founders most of their challenges came from you know scale basically these are good challenges right I'm not talking about bad challenges like lack of product market fit or inability to create demand I'm talking about scaling challenges which comes from getting more and more demand and achieving better market fit and these challenges traditionally in the old pay playbook they were solved by throwing more bodies at the problem so

[15:14] you had more support tickets, you hire more customer uh support agents, right? You try, you need more pipeline, you hire more SDR, you need more top of funnel, you pay more to ads and and you hire demand basically. So in this new playbook that we're trying to reimagine at Swan AI, we called it the autonomous business OS. Basically, we try to think how can a business scale without adding more bodies but with adding more intelligence. So what we try to do at Swan is we look at a process and instead of thinking how can more humans solve the challenges that we're experiencing, we're thinking how can humans and AI collaborate together to solve significant parts of that process. And so what we found out is that most of these processes were built from the ground up with human and AI together could actually replace this notion of hiring more and more folks just for the sake of solving these scaling challenges. How do you ensure AI

[16:17] agents augument sales teams instead of making them feel replaceable? Yeah, so our core focus is on what we call the difference between system interactions and human interactions. We have like a north star at our product roadmap to decrease the amount of system interactions that sellers perform and increase the amount of human interactions. And whenever we use AI in our solution, we use it to replace the complexity that sellers have in their go to market and not increase it just for the sake of automation. And so when you use Swan, the main interface is actually within Slack. It's a Slack app and our AI agent is kind of like your RevOps agent. It's your accountbased agent.

[17:05] It's your support agent and it's your CS agent. It does everything basically on Slack. So if you want to research an account, you just message Swarm. If you want to change your configuration or change your ICP um definition or change your workflow in the platform, you just message Swamp. You reduce what would take 30 minutes of your time to 10 seconds of a message. And if you have a support question, you just message Swan. If you want to consult on how to do something, you message Swan. And what happens quickly is that feedback loops become super fast. The platform improves itself very fast because changing something is just a message away. And what happens also is that the amount of human interactions grows and grows and grows because sellers have more time to spend focusing on their buyers instead of managing their system. I love that. I love that approach. The only issue I have there is I've noticed that as companies scale the the different revenue departments are generally

[18:08] siloed. You know, marketing has their own northstar, sales has their own northstar, customer success has their own northstar. How do you unify these departments and get them all to work in tandem with each other under a singular kind of north star and the where they're all using Swan AI to move faster? Yeah, that's a that's a great question and a very tough one. I don't think that anyone has cracked the code on that yet. I would say that in my perspective, you know, with my previous two companies, what worked well for us is aligning two things. one incentives incentive alignment. So basically it's critical that you know CS is incentivized by revenue and you want sellers to be incentivized by retention and you want marketing to be incentivized by you know pipeline success and if you align incentives in a way that you know marketing thinks about sales, sales thinks about CS, CS thinks about sales, basically you can create

[19:12] more coherent strategy, a coherent organization that works in a very synchronized way. The second thing is attribution. So the first thing is incentives. The second thing is attribution. You need to align attribution. Last touch point. Attribution is the most terrible thing you can do to your go to market organization because then people would just fight over you know who who talked with the with the prospect the last, right? But it's not about that because converting an account from all the way from not in awareness to awareness to intent to a closed one to actually retention requires so many touch points, right? So many touch points and everyone is in charge of that. So attribution should really be rethought here in the context and it should be aligned with the incentives as well. So if you nail incentives and attribution, you can create an amazing aligned go to market organization. And just to follow up on that note, how do you see the organizational structure of companies changing as we need less humans? We need and as efficiency increases.

[20:19] is like having a CRO and having marketing sales success all report to you one leader more effective or do you see a world where these revenue departments still have you know their their seale leaders and and they're all reporting to the CEO. Yeah. So I I don't think that AI would change any of that. I think that the way that these organizations evolve over time in startups are actually driven by the DNA of the founders or the initial leadership team. What happened is that when you start a company and it's very product oriented for example so you have a product organization that controls a lot of budget and resources and maybe marketing could happen you know beneath product they call it growth and just a PLG so it will you know be sit under the CTO when you have a like very hardcore salesled organization because the founder was an amazing salesled or they decided to hire an amazing CRO early on then the entire organization evolved

[21:23] into being very salesdriven and so marketing reports to sales and success reports to sales and everyone goes like that. Um so it really depends on the DNA of the company that is driven mainly by the you know initial leadership team that is built in the organization and AI wouldn't change any of that and what AI would do basically is it would rethink how existing roles are performed you know CS would maybe focus on different types of processes sales would be able to maybe focus less on top of the funnel less on you know um creating demand more on capturing demand and uh marketing would be able to take more of like the SDR roles on their own basically and try to create these opportunities. So AI will reshape how we know roles today but the DNA of the organization will always be the number one factor will determine how these um organization structures are determined.

[22:23] Amos this has been a masterclass in scaling with purpose. Thanks for challenging the status quo and proving that AI can be a force for human connection. To our listeners, dive deeper into Swan AI's mission at getswan.com and follow Amos on LinkedIn for his unfiltered takes on GTM. If you love this episode, hit subscribe. We've got more boundary pushing founders from Tel Aviv to Tokyo lined up. Until next time, automate the grind but never the soul. Love it. Thank you so much, Rick. Tech founders and VCs careers lessons GTM