AI-Powered GTM: 3 Founders → $30M ARR with Amos Bar-Joseph

What if one person could run your entire go-to-market motion — marketing, sales, and customer success — backed by a system of AI agents?

That's exactly what Amos Bar-Joseph is building at Swan AI — and in this episode, he pulls back the curtain on the AI-powered GTM model that generated over $1.5 million in pipeline in a single month. As CEO and sole go-to-market operator, Amos runs every stage of the funnel with a lean agentic system designed from the ground up for human-AI collaboration, not headcount growth.

This conversation will reframe how you're thinking about AI-powered GTM — what it actually takes to move from system engineering to context engineering, and why the real opportunity for marketing leaders isn't replacing your team, it's scaling them.

Guest Bio: Amos Bar-Joseph is the CEO and co-founder of Swan AI, a platform building what he calls the first AI go-to-market engineer — a coding agent designed specifically for GTM professionals. A three-time founder with two previous B2B startups under his belt, Amos is now testing a radical thesis: building a $30M ARR company with just three co-founders, powered by autonomous AI agents.

What we Cover: 

• Why 90% of AI implementations fail — and the mindset shift that changes everything
• System engineering vs. context engineering: the new operating model for CMOs
• The MQL-to-SQL handoff problem — and how AI agents can bridge the gap
• What "zone of genius" actually means for your marketing team — and how AI protects it

Chapters: 

• (00:00) Welcome and Episode Context
• (04:39) Building the Autonomous Business
• (13:03) The AI-Powered GTM System
• (28:05) Best AI Use Cases for CMOs
• (45:31) Advice for CMOs Bridging the Gap

Resources & Links:

• Swan AI: swanai.com
• Amos Bar-Joseph on LinkedIn: linkedin.com/in/amosbarjoseph
• The Autonomous Age Newsletter: swanai at Beehive (search 'Autonomous Age')

If this episode shifted how you're thinking about AI and your go-to-market motion, share it with a marketing leader in your network. And if you're not already following Growth Activated, now's a good time — a new episode drops every week.

Growth Activated is hosted by Mandy Hornaday. 

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Transcript

Mandy Hornaday:

Welcome to Growth Activated. I'm Mandy Hornaday, your host with 15 years of experience leading marketing teams ranging from small startups to large service organizations. I've built high-performing teams of all sizes and have seen firsthand how fast the landscape is evolving, making marketing leadership more complex than ever. Today, I help marketing leaders elevate their strategies, lead with confidence, and build careers they love. If you're ready to drive impact and unlock growth for yourself and your company, you're in the right place. Let's get started.

Mandy Hornaday:

What would it take to build a $30 million company with three founders and one person owning the entire go-to-market function? Hey everyone, welcome back to Growth Activated. I'm your host, Mandy Hornaday. About a year ago, I came across Amos Bar-Joseph on LinkedIn when he shared a goal that caught my attention — to build a $30 million ARR company with three co-founders. Around that same time, he also shared that his AI-powered go-to-market model had generated over $1.5 million in pipeline in a single month. So naturally, I had questions.

Mandy Hornaday:

Because not only is Amos the CEO of Swan AI, but he also owns the entire go-to-market function — marketing, sales, and customer success — supported by a system of AI agents running nearly every stage of the funnel. In this conversation, we unpack how they're building what he calls an autonomous business, including why he believes AI implementations fail, the difference between system engineering and context engineering, and why the real opportunity for CMOs isn't replacing employees, it's scaling them so they can spend more time in their zone of genius. If you're trying to figure out what the next era of go-to-market might actually look like in practice, this conversation will reframe how you're thinking about it. I know it did for me. Let's dive in.

Mandy Hornaday:

Hey, Amos, welcome to Growth Activated. We're so excited to have you here today.

Amos Bar-Joseph:

Thank you for having me, Mandy. I'm excited as well.

Mandy Hornaday:

I've been looking forward to this one all week. I first came across your profile about a year ago when you shared your big audacious goal of building Swan AI to $30 million ARR with three co-founders. And I know my understanding is you own the entire go-to-market. So I could not be more excited for today's conversation. We'd love to give you an opening — tell us a little more about what you're building and why you believe this is possible.

Amos Bar-Joseph:

Swan AI is not my first company. I've actually built and scaled two startups before. Both were in B2B, based on the old unicorn growth-at-all-cost model — where you raise a ton of money before you even know who you're selling to, then you build a 30- to 40-person team before you get to your first million dollars in revenue. And then each round, you try to scale your total addressable market and your valuation, not really the core metrics of the business. Sooner or later, you realize you've built your entire company on a very sick foundation. It's hard to maneuver. There's so much bloat. And you're asking yourself, why did I do that?

Amos Bar-Joseph:

With Swan AI, I felt like something's got to change. With this notion of AI agents becoming a real thing in the business environment, I felt like this was the time to reinvent the startup playbook — how do we scale a business from zero to one, from one to ten, from ten to thirty? That's what we're passionate about at Swan AI. We're building the first autonomous business. It's a business designed from the ground up toward human-AI collaboration, not human-to-human coordination. And as you mentioned, Mandy, we're focused on revenue per employee as the North Star, not valuation. It's not about getting to a billion-dollar valuation. It's about getting to $10 million ARR per employee. It's a story of scaling employees, not scaling the valuation of the business.

Mandy Hornaday:

Oh, that's so fascinating. I love that perspective. Walk us through the three founding team members and what each person is responsible for.

Amos Bar-Joseph:

Me, Neve, and Ido — we go way back. We're super close friends. They were with me in my previous companies as co-founders, so we know how to work together. Neve is extremely technical — what you'd call a CTO in a traditional company. He's built products from zero to one, and then from one to a million users, and he knows how to build on a very solid foundation. Ido is an amazing product person who's super technical but also has a great eye for UI and aesthetics. And I'm in charge of growth. I'm good at capturing attention and turning that attention into pipeline, and then turning that pipeline into revenue.

Amos Bar-Joseph:

If you look at the core skill set required to build a company in the early days, this is really all you need. You need to build the product. You need to understand what the product should be. And you need to sell the product. So we told ourselves, why shouldn't we just try to build it ourselves? Can we actually make it work without hiring anyone else, and try to scale the output of each person so we can discover what it looks like to have Neve's version of the 10x engineer, Ido's version of the 10x product leader, and Amos's version of the 10x growth operator? 2025 was a pilot year. We grew from zero to over 200 customers across five continents — real businesses using Swan AI daily — and we're just getting started.

Mandy Hornaday:

Wow. I was telling my husband last night — I also have two really good friends, one who's a CTO and one who's a head of product, and we've been through multiple companies together. We've always joked that we should do our own thing. And you are a walking inspiration of that. Now I know who my call is to after this recording.

Amos Bar-Joseph:

You should, Mandy. It's never been a better time to build a company. Until the last two years, the incumbents had an advantage. They were big. They could out-engineer you, out-market you, out-sell you — out-everything you — because they had more people. But for the first time in history, that equation has flipped. Now their size is making them slower. It's making them hard to adapt to this new revolution, and everything — all the fundamentals — are changing beneath them. So if you're out there thinking about building your own company, there's never been a better time in history.

Mandy Hornaday:

Well, what I'm so curious to learn from you today is how you're owning the entire go-to-market function as a solo individual with this agentic team behind you. Marketing feels harder than ever right now. It feels really hard to break through the noise. Everyone is doing more — this "do more with less" era that everyone hates. But you're not only doing marketing. You're doing sales and customer success. You're the CEO. So what does a day in the life of Amos look like? Where do you spend your time?

Amos Bar-Joseph:

I'll take a step back first because I think it's important to explain our philosophy around AI implementations — that will explain why my day-to-day looks the way it does. When you look at companies today, 90% of AI implementations fail. The reason they fail is because people are looking at it from the wrong perspective. They're just trying to automate processes. They look at something and say, how can we use AI to solve all our problems? How can we satisfy our board by saying we're using AI agents? With Swan AI, we have a different perspective because it's a business designed to scale its employees, not replace them.

Amos Bar-Joseph:

What we're asking ourselves is: how can we ensure that each person at the company spends most of their time in their zone of genius? That intersection between their passion and skills that creates disproportionate value for the company — that's their zone of genius. We try to keep them within it as much as possible. AI automates what's outside of it — the mundane, the repetitive — and amplifies what's within. That's how you scale an employee: by allowing them to spend more and more time in their zone of genius and create more and more throughput there.

Amos Bar-Joseph:

When you look at my role at Swan AI, my zone of genius is storytelling. I love telling stories. And we realized that my superpower for telling stories on LinkedIn could generate a lot of attention, pipeline, and revenue for the company. But there's a lot of work required to do that. So how can we ensure I spend my time in that zone of genius?

Amos Bar-Joseph:

We started the process manually. I started posting on LinkedIn, getting attention, people started engaging. Then we identified the entire process and asked ourselves — for each step, what should I do, and what should the AI do? The first thing someone might think of is automating the posting itself. But no — that's where I'd lose my storytelling touch and leave my zone of genius. So to answer your question, Mandy, a day in the life of a single GTM operator starts with me writing the LinkedIn post. I use AI to accelerate the process and improve my thinking — not to write more posts, but to write a better post than I could without it.

Amos Bar-Joseph:

Then we have an AI that monitors post engagement. I generate over one million impressions each month on LinkedIn — over 15,000 people engaging with my content. That's too much for me to handle manually. So we have an agent that monitors that engagement, identifies hot ICP leads showing real intent, and surfaces them for me to review. I look at who we should focus on from that suggested list and send Swan AI to engage with them on LinkedIn on my behalf, with my instructions. Then for folks sending connection requests and interacting more directly, I have an agent that handles that inbound, routes it accordingly, and lets me focus only on the highest-priority leads.

Amos Bar-Joseph:

Then we have folks coming to the website after a connection request. We have an agent that de-anonymizes visitors and sends a personal outreach to contact them. At every step, I'm in the loop — understanding what's happening, guiding the agents on where to focus, how to engage, and sometimes writing my own personal messages for the highest-intent, highest-value leads. We also have an agent for the demo request itself — it researches, qualifies, routes, and then preps me for the demo and helps with follow-up tasks. And then helps with onboarding and delivery. So if you look at that process end to end — from a post all the way to capturing a lead and helping them through the onboarding funnel — an agent helps me at every single point, either automating or amplifying my work.

Mandy Hornaday:

Wow. Amos, I probably should have leveled set at the beginning — can you share a little about the go-to-market motion you use? That would help contextualize your average contract value and whether you're product-led or sales-led.

Amos Bar-Joseph:

First of all, we're creating the market, not going to market — which is a bit different and a big part of our philosophy. We're creating a movement around the autonomous business. Our target market is businesses that also want to become autonomous businesses — companies that want to scale with intelligence, not headcount. These are typically SMBs. And the way they learn about Swan AI is usually through this lens of building an autonomous business, not necessarily through our product specifically.

Amos Bar-Joseph:

Part of our go-to-market strategy is creating this movement around autonomous business — setting the playbook, the frameworks, and the way to think about it. Customers come to us when they feel they can trust us to help them through that transformation. It's not just about a product. It's about a transformation. And that allows us to attract a lot of inbound attention. When you're in that position, you're not asking yourself "is it PLG or SLG, enterprise or SMB?" What you're asking is: how do we optimize the trend line? We look at two axes — the number of touch points required to close an account, and the ACV — and we try to find the right balance for each segment.

Amos Bar-Joseph:

We'll never have a 100-touch-point deal for a million dollars — that's not our autonomous business model. But that doesn't mean we don't work with enterprises. We have companies like Palo Alto Networks — one of the biggest cybersecurity companies in the world — working with us. They came to us through an acquisition, and that deal had very few touch points despite being a significant deal. We also have a free-trial, self-serve funnel for folks paying around $3,000 a year. What we're designing is a motion that attracts a lot of attention, so pipeline generation isn't the problem. It's about how you allocate resources between machine intelligence and human touch based on ACV.

Mandy Hornaday:

I love it. And I love what you said — creating the market, not going to market. Would you view it as category creation?

Amos Bar-Joseph:

Definitely. But we're doing it a little differently. We have two narratives. One is a company or culture narrative: what type of business are we? That's the autonomous business — a super category. We're saying: if you want to win in this AI revolution, you must become an autonomous business. That changes your mindset about your products, your tooling, and your human-AI collaboration model. That thinking leads to our second narrative, which is the category we're actually creating in go-to-market specifically.

Amos Bar-Joseph:

We're building a cloud code for GTM. Basically, an AI go-to-market engineer — something between a developer and a RevOps person that works with sales and marketing to turn any go-to-market process into an agentic workflow in seconds, from prompt to pipeline. The only truly successful AI implementation we've seen humanity produce so far is coding agents. We've never seen a successful AI implementation that isn't in the shape of a coding agent. At Swan AI, we built the first coding agent designed for GTM professionals, not for developers. That gives GTM professionals and revenue teams the same superpowers that developers have today. The category creation here is that GTM engineering doesn't need to be a hiring decision anymore. You let AI take on that burden so you can focus on executing your ideas at the speed of thought.

Mandy Hornaday:

I love that. And are you using Swan AI for a lot of the agents we were talking about, or are you also relying on other tools?

Amos Bar-Joseph:

I'm a heavy user of Swan AI and we've built our entire flow around it. The only way for a small team to build and maintain complex agentic workflows is if they have a coding agent that can help them with all these GTM flows. Swan AI just abstracts away all the technical complexity. I can tell it, "I want to change our inbound motion because of this ICP — I just want to route them to a free trial. I don't want to see them." And I just tell Swan AI that, and it adapts the workflow. After about 20 seconds, it's already live. You need that speed of iteration when you're moving fast. Swan AI is the main agent I use, but we're also using Claude Code and Cursor. And I use Claude regularly for a lot of my work. It's not like I spend my entire day in Swan AI — I have other tools helping me — but specifically in GTM, because Swan AI is a coding agent, it can do almost everything.

Mandy Hornaday:

One of the things that's so challenging — and I'm still trying to figure out the balance on as a CMO — is the time it takes to build agents, manage and optimize them, do the human elements of my job, and also train and upskill in agentic AI. What does your day actually look like in terms of time allocation?

Amos Bar-Joseph:

Part of the transformation that go-to-market teams go through when they work with Swan AI is moving from system engineering — the old work — to context engineering. Let me explain both, and why context engineering is actually very similar to what you do as a manager today.

Amos Bar-Joseph:

System engineering is what you get when you're promised a solution that could generate pipeline if you focus on intent signals — but then you buy it, and you need to invest so much time, engineering, and configuration to make that promise real. That's the past. Even the hottest go-to-market platforms today still require enormous time and money to make their promises come true. What happens when you have a coding agent that's native to your GTM environment, where a single sentence can execute on all these ideas? When you collapse that cost of production to zero, all that configuration cost disappears — and the transformation goes to context engineering.

Amos Bar-Joseph:

The only thing that matters in context engineering is: what's the strategy? What are we trying to achieve? Who should lead this? Who should get this? Why are we missing this? Do we have a pipeline drift? Do we have revenue leakage? These are the questions you should be focused on. And instead of a chain of command of ten people to make it happen, if you're a CMO or CRO, you can just tell Swan AI to execute it, and it makes sure all the systems and people are aligned. The only thing that changes is that now you're not only managing humans — you're also managing AI agents. You need to provide them with the right context, just like you do with your employees. The fundamental work hasn't changed. We're just done with system engineering.

Mandy Hornaday:

That sounds wonderful. For those who are interested in learning more — what do you typically need to do to set up and start taking advantage of a product like Swan AI?

Amos Bar-Joseph:

What we've realized is that Swan AI's job is to get the context out of your head. We don't think of it as your job to tell the agent what to do. The agent has a goal, and it tries to understand that goal from you and then get all the context it needs. When you onboard to Swan AI, it will ask you: what's the goal here? Are we trying to generate pipeline? Improve speed to lead? Move deals from negotiation to closed-won? There's a goal. Then Swan AI starts bouncing ideas with you — all in the service of understanding what's inside your head and how you think about it. You finish the onboarding in about 20 to 30 minutes, having shared a lot about your ICPs, your biggest bottlenecks, and how you're thinking about solving them. By the end, Swan AI has already built you an agentic workflow that can presumably solve most of those pain points.

Amos Bar-Joseph:

And it does it in a human-centric way — meaning it will tell you, "The next step for you is to review the output of this process. I'm not going to change anything in the systems yet. I'm not going to send anything to prospects. Your next step is to review the output." So you didn't spend time building anything. What you did was share context. Then everything becomes human-AI feedback loops that are constant. Your GTM motion should evolve constantly, and Swan AI just creates a very good practice of iterating on it.

Mandy Hornaday:

I'd love to hear — what are some of the agents that CMOs are most excited about that they're building through Swan AI? What are the best use cases from a marketing perspective?

Amos Bar-Joseph:

There's the hottest use cases, and then there's the ones with the most impact — and they're not the same. Before I answer, the most important thing people don't understand is that the best use case for AI is not found in the AI world. The answer is in your GTM organization. You have a bottleneck, and the best use case for you is your biggest bottleneck — not the best AI solution. That's what people get wrong.

Amos Bar-Joseph:

With that said, the biggest bottleneck we're seeing consistently across most of our customers is the handoff between MQL and SQL — that transition. That's the hardest part. Marketing does a lot of work. There's a marketing-qualified lead. Now sales needs to act on it. Something falls in that handoff. When you have an agent that can listen to both sides and make sure everyone gets what they need — marketing can tell Swan AI how to score and prioritize leads, and sales can inform it why they don't want to reach out to a particular person — Swan AI can do the work of explaining the rationale, qualifying, routing, and creating feedback loops between the teams. All the research, scoring, and system admin work goes to the AI, while marketing still owns the strategy for bringing and qualifying leads, and sales still owns the conversion thinking. Swan AI takes care of the handoff.

Mandy Hornaday:

So just so I understand correctly — is Swan AI constantly capturing feedback from sales on every MQL delivered, and then using that to go back to marketing and say, "Hey, I think we should update our qualification process based on this overwhelming sales feedback"?

Amos Bar-Joseph:

That's exactly how it works. You build what we call skills with Swan AI — skills are essentially the agent equivalent of SOPs, standard operating procedures. They're codified as docs that everyone can read — think of them like a Notion doc, except Swan AI has read and write permissions to it. Every time there's feedback, it either automatically updates the doc or escalates to the right owner. Marketing might decide that sales can't update the assignment doc directly — Swan AI would escalate to marketing instead. So there's ownership. The AI is the intermediary, but people own the processes. You can do that for assignment, qualification, routing, deal creation, research, outreach — everything can now be codified into what we call the context model. And what people don't understand is that all these terms felt super technical before because coding agents were designed for developers. When you wrap it for GTM folks, it just feels like a knowledge base, policies, and "how we do things at the company." It gets completely demystified.

Mandy Hornaday:

I love it. What was the other example you were going to move into? I think it was the most popular use case.

Amos Bar-Joseph:

CMOs love generating MQLs — that's a big part of what they do. The lowest-hanging fruit right now for generating MQLs is reaching out to folks with intent. You're already doing marketing work. If you're not day one — if you already have resources deployed, awareness initiatives, touchpoints all over the place — then you have intent out there. The next best thing to generate an MQL is to double down on the highest intent.

Amos Bar-Joseph:

The easiest way is your first-party intent — people who landed on your website but didn't convert. If you look at your funnel, you already have MQLs. But five inches above them are people who almost became MQLs and didn't. Swan AI can help you create an agentic motion for all these folks showing intent on your website. If it's low intent — nurture. If it's high intent from a high-value account — push to sales and get human attention. If it's high intent but lower value — maybe automate the first touch points or push them into a retargeting campaign. You have endless possibilities for a single motion. The old world required stitching HubSpot, Salesforce, Marketo, Sixth Sense, and Apollo together and manually building workflows everywhere. Now you just tell Swan AI, "I want to tighten up our high-intent definition — only folks who visited multiple pages from a company with high ICP fit," and Swan AI updates the definition across all the workflows instantly.

Mandy Hornaday:

I have one for you. One of our big pain points — age-old pain points — is understanding the buyer's journey that buyers are actually taking. If you want to throw out the word attribution, go for it. I'm curious — can Swan AI help CMOs look across all those different systems and understand what's actually happening so they can tie the story together?

Amos Bar-Joseph:

Yes, and it does that in two ways. First, Swan AI has the ability to tag accounts, buyers, and prospects in natural language. So you could say, "If they came from a Facebook campaign, tag them. If they're a high-ICP with high intent from Google and visited three or more pages, tag them." Swan AI monitors all these touchpoints and, the moment it sees an account or buyer, determines which tags are relevant. That's one side. The second is that Swan AI has introspection capabilities — it can look internally at all the jobs that have already run. It can look at everything happening within its own ecosystem. So you can start asking questions like, "Show me all the hot leads that came from Facebook and closed in the last 48 hours." You can also use natural language search across all of it — ask about what happened with a specific account, or tell Swan AI to send you a weekly Slack message with the five accounts that did a specific set of things.

Amos Bar-Joseph:

You're not giving away your brain to the AI. You need to come up with the hypotheses and the questions. You need to understand which tags to create. There are still difficult tasks to be done — they're just in the context engineering realm, not the system engineering realm. You need to be strategic, creative, and have a good understanding of your processes and your data. And the better people are at those skills, the more they can leverage AI. It's not that AI is going to take all your work. It's more that people who were good in the previous era will be even better in this new one. But people who only knew how to manipulate systems and configure workflows? That knowledge is going away.

Mandy Hornaday:

Well, if you can solve that, there are a lot of clients who would benefit. So I'm curious, Amos — where does this go? Where does it go wrong? Where has your own hypothesis been tested over the last year? What are some learnings we should all be aware of?

Amos Bar-Joseph:

The word autonomy plays a big role here. We're building an autonomous business, and we're working with AI agents that have autonomy. This isn't new. If you build a business and give a lot of autonomy to your employees, you're doing something right — but things can also go wrong. In go-to-market, you see two types of organizations. Some are top-down, where SDRs and AEs operate like robots with no creative freedom. Others cultivate creativity and talent, enabling people to work in their zone of genius. But sometimes those people do something that might hurt the brand. The same applies to AI agents. The more autonomy you give, the more risk you take — but also the more creativity and unexpected solutions you get.

Amos Bar-Joseph:

Our biggest incident from the past year involved Swan AI giving an unauthorized discount to a customer. Swan AI has a deep knowledge base about how to operate in GTM and within its own system. A customer asked Swan AI about our pricing and the effects of upgrading. This happened a week after we had changed our pricing and increased costs. Swan AI still had the previous pricing in its knowledge base. The customer said, "Wait, these numbers are higher than I thought." And Swan AI told them, "You're right, and you're such a good customer — we can give you the old pricing back." The customer agreed, Swan AI didn't have billing privileges to actually execute it — there's always a human in the loop — but I saw the message and, well, grabbed my head.

Amos Bar-Joseph:

We decided to honor the discount because we're not optimizing for quick wins — we're trying to build lasting relationships with customers. But the conclusion wasn't to limit Swan AI or remove it from those conversations. The right question to ask wasn't "is it capable of handling this?" The right question was: where do we want humans to be involved? And we realized that commercial conversations with customers are conversations we want to be part of — not because AI can't handle them, but because we want to be in those conversations. I think I'll handle them better. I can learn from them. There's a kind of moral obligation to be present. So our conclusion was: whenever there's a commercial discussion, Swan AI should escalate to the team. We're building AI not to build walls between us and our customers, but to build bridges.

Mandy Hornaday:

What a great example. Amos, I know we've got a few minutes left, and I've really enjoyed this conversation. For the CMOs out there listening — this all sounds great, but it feels really aspirational. They already have huge teams struggling to adopt AI meaningfully. When you think about 2026 and where go-to-market is headed, what advice would you give to CMOs who see the vision but feel like it's so far away? How can they start to bridge that gap?

Amos Bar-Joseph:

Our mission with building this autonomous business is not to turn the entire world into three-person teams. We understand that's not a feasible reality. What we're trying to show is the North Star. This is not a zero-one game. The North Star is: how can I use AI to scale my employees? That's the right mindset, and I think it's what most people aren't getting yet. CMOs are looking for quick wins — that's the DNA of go-to-market. There's pressure to deliver. And people are being told to use more AI to get more with less. But "more with less" could be reframed as: how do I scale my employees?

Amos Bar-Joseph:

If you change your mindset and look at your talent stack rather than your tech stack, ask yourself: what is one thing I could do today to unblock my employees and make sure they're spending more time in their zone of genius? And if you don't know what their zone of genius is — start figuring it out. Because if you don't, AI will eventually replace your entire department, because people will be spending time on the wrong tasks. Your only way to protect your team is to ensure your talent is working on what they should be working on. It might not be this glorified zone of genius where passion and skills perfectly intersect, but it needs to be something unique to your company, your department, your people — something AI can't just mimic. Move one inch at a time. That's the path to success.

Mandy Hornaday:

Yeah. And frankly, even as you're talking — that's always been part of the job. It's always been our role as marketing leaders to figure out how to reduce bottlenecks and make sure team members are set up to contribute in the areas where they can. So it's a great reminder that that part hasn't changed. The solution for it probably has — being able to tap AI versus building more systems and processes.

Amos Bar-Joseph:

Exactly. The right frame here is that the fundamentals are still fundamentals — they're still the most important part of the job. People are still the most important part. The only thing that's changed is that we're becoming native to a digital environment within business organizations. AI allows us to become native within that digital environment. That's the category creation play, Mandy. We don't think AI is replacing your talent. It's enabling them to become native within the digital realm. How do you unleash your talent in this new universe that you can now enter into? There's a lot of exploration here. But it puts the human at the center and asks how you can scale them.

Mandy Hornaday:

Awesome. Well, Amos, if people want to learn more, they can follow you on LinkedIn — I know you're always sharing great perspectives and knowledge there. And drop your website for people interested in learning about Swan AI.

Amos Bar-Joseph:

I also have a newsletter. It's called the Autonomous Age. If you want a front-row seat to how we're building our autonomous business, subscribe. It's on Beehive — search "Autonomous Age" or find it as swanai at Beehive.

Mandy Hornaday:

I love that. Thank you so much, Amos. I've really enjoyed this conversation and hope to have you back soon.

Amos Bar-Joseph:

Thank you, Mandy, for having me.

Mandy Hornaday:

Thanks so much for tuning in to this episode of Growth Activated. I hope this conversation sparked new ideas, challenged your thinking, and gave you practical tools to help elevate your impact as a marketing leader. If it did, I would love for you to pass it along to a friend or colleague in B2B marketing. The more we grow together, the more we raise the bar for what marketing leadership can look like. And as always, in the meantime, keep activating growth for yourself and your company. See you next time.