How to 100x Your GTM Team with AI: A Masterclass in Agent-First Execution

If you’re still thinking about AI in terms of features, dashboards, and tools, you're missing the bigger story. The real transformation is operational. The focus has moved from headcount to architecture: designing systems that scale intelligence, not just effort.

In the latest episode of Momentum’s AI Advantage Series, Jonathan Kvarfordt (our Head of GTM Growth) and Jordan Nettles (Senior Sales Engineer at Momentum) sat down with Amos Bar Joseph, co-founder of Swan AI, to walk through what it actually looks like to build a go-to-market machine designed for scale without scaling headcount.

Amos and his two co-founders are building Swan to $30M in ARR with no sellers, no marketers, and no RevOps team. Just three humans and a full-stack army of AI agents. Swan is already doing what most teams haven’t figured out how to even prototype.

Let’s break down how they’re doing it and what your team needs to understand about real AI orchestration.

Why Swan Isn’t Hiring

Most startups scale through brute force. Fundraise, hire ahead of revenue, and sprint toward product-market fit while burning capital. Amos has been down that road twice. This time, he’s doing it differently.

“We're not building a headcount-heavy org,” Amos explained. “We're building a business where every problem is solved by layering intelligence, not people.”

At Swan, the constraint is the point. With just three founders, they’ve designed a system where AI agents absorb the work that would normally require dozens of full-time employees. But this isn’t about novelty. It’s about discipline.

Instead of adding SDRs or marketers, the team mapped their biggest bottlenecks and assigned intelligent agents to handle them. The result is a radically lean GTM system with full pipeline coverage (top to bottom) driven by autonomous workflows.

The First Principle: Build Around Strengths, Not Tools

One of the biggest missteps teams make when adopting AI is tool-chasing. They buy into the buzz, spin up a few integrations, and hope it sticks.

Amos’ take? Flip the lens. Don’t retrofit your workflows around tools. Build tools around what your humans already do best.

“We didn’t go out and install some generic AISDR,” Amos said. “We asked: What is each founder great at? Then we built agents to amplify that.”

For Amos, that meant content. He’s a gifted storyteller and thought leader. His LinkedIn presence drives millions of impressions every month. So the Swan team didn’t try to replace that: they built an AI assistant to help him scale it.

Meet Shakespeare, Amos’ custom content agent. It helps him brainstorm, draft, and refine high-performance LinkedIn posts while keeping his voice intact. Shakespeare doesn’t write for him. It co-creates, then accelerates.

Momentum’s Jonathan Kvarfordt summarized it perfectly: “You’re not injecting AI everywhere. You’re starting with the problem, identifying your unfair advantage, then scaling it through automation. That’s real orchestration.”

The AI Funnel: From Thought Leadership to Pipeline

Shakespeare is just the beginning. Swan’s go-to-market operation runs on an orchestrated funnel of AI agents that move prospects from social engagement to closed-won, all without traditional sales ops.

Here’s how it works:

1. Shakespeare: Content Amplification

Amos uses Shakespeare to co-write viral content on LinkedIn. The agent is trained on his style, tone, and messaging framework. It knows how to structure hooks, inject social proof, and build narrative arcs.

“Shakespeare helps me generate 1.5M impressions per month. It’s not replacing my voice, it’s letting me run faster with it.”

2. The Observer: Engagement Intelligence

Once a post goes live, another agent kicks in. The Observer scans LinkedIn engagement and surfaces the most promising prospects. It flags ICP-aligned users based on their roles, comments, and past activity.

For each signal, the agent generates a research card and delivers it straight to Slack. No scraping. No guesswork. Just usable buyer intelligence delivered in real time.

3. The Connector: Inbound Qualification

Amos gets over 300 inbound connection requests per day. The Connector scans every request, checks for ICP alignment, and flags the most valuable leads. It even surfaces talking points based on each contact’s digital footprint.

“Instead of digging through hundreds of profiles, I just open Slack and see who I should talk to, and why.”

4. The Hunter: Website Visitor Reveal

Next, they built an agent that identifies anonymous website visitors who didn’t convert. It matches traffic to known individuals, pulls technographics and buying signals, and suggests messaging.

It even auto-sends LinkedIn connection requests and DMs based on prior engagement with Swan content.

“I don’t need to hope they fill out a form,” Amos said. “If they’re interested, I can spot it and act.”

5. The Router: Smart Lead Distribution

For leads that do fill out a form, Swan uses another agent to score, enrich, and route them. If the lead is high value, it books directly with Amos. If not, it assigns them to a free trial flow or waitlist.

In one standout example, Swan’s CEO of a key partner company signed up for a free trial just hours after Amos gave a keynote at their event. The Router agent recognized the timing, flagged the context, and routed the lead to a high-priority track automatically.

6. Ezra: Calendar Intelligence and Call Research

Swan’s internal agent, Ezra, handles what every high-velocity seller dreads, preparing for nonstop meetings.

Ezra syncs with Amos’ calendar, email, CRM, and even the broader web to prepare custom briefings for every meeting. It pulls job titles, company info, social bios, and relevant news. It tracks activity history. It creates speaking points. It flags objections before they’re raised.

Slack Is the New Operating System

If Swan has a secret weapon, it’s not the agents, it’s where they live.

All of them operate through Slack.

From Shakespeare to Ezra, from Observer to Router, every agent communicates in a shared, conversational interface. They show up as Slack bots, post updates, respond to prompts, and adjust based on feedback.

This is strategic.

“We needed a single command center,” Amos explained. “Slack gives us one interface to orchestrate everything.”

When an agent misfires, Amos replies in-thread. The agent corrects itself, updates its memory, and adjusts its parameters.

This is collaboration between humans and software, where iteration happens inside the workflow instead of through tickets, delays, or dashboards.

Jordan emphasized the same point from our side:

“We don’t ask sellers to change how they work. We bring insights and actions directly into Slack. That’s where execution happens.”

At both Swan and Momentum, Slack isn’t just a chat tool. It’s where RevOps lives. It’s where insights are pushed, tasks are handled, context is surfaced, and systems are controlled. It’s the AI-native version of Salesforce, if Salesforce were actually usable.

Feedback Loops and Agent Evolution

One of the clearest takeaways from the webinar is that agents are never finished. They evolve through usage, feedback, and conversation.

Swan designed their agent framework to cover 70 to 80 percent of use cases upfront. The remaining 20 percent is handled in the field. Amos can tag an agent in Slack, leave a correction or clarification, and the agent retrains itself with that input.

In one example, an agent flagged a government organization as a qualified lead. Amos replied, “Exclude all public sector orgs from our B2B segment.” The agent acknowledged the correction, updated its targeting logic, and documented the new rules.

This is what most AI platforms miss.

They sell intelligence, but they ignore iteration.

Swan bakes learning into every agent interaction. Momentum does the same. If reps or managers give feedback on a Slack alert or data summary, our platform adjusts the workflow or updates routing behavior.

It’s the reliability, the ability to listen, adjust, and improve on the fly, that turns automation into something people actually want to use.

From Bottlenecks to Breakthroughs

Throughout the session, Amos kept returning to a core idea: AI doesn’t start with technology. It starts with constraints.

You don’t solve GTM complexity by throwing more people at it. You solve it by studying the friction. Where does execution break down? Where do tasks pile up? Where do humans get pulled away from what they’re best at?

Then, and only then, do you deploy automation.

“Think of AI like a great intern,” Amos said. “Start small. Find repeatable tasks with low error costs. Train them. Test them. Improve them. Over time, you’ll build an elite team of invisible teammates.”

In 60 days, Amos closed 71 deals with zero SDRs, zero AEs, and zero budget. His entire motion was powered by agents: surfacing leads, qualifying, enriching, personalizing outreach, prepping calls, and following up.

The bottlenecks didn’t disappear. They were absorbed, atomized, and delegated to software without sacrificing intent or impact.

Rethinking RevOps: From Architects to Orchestrators

Agentic AI is forcing a new conversation inside RevOps, one that questions not just how teams operate, but why those systems exist in the first place.

In a traditional setup, RevOps builds infrastructure. They map workflows, manage integrations, enforce process compliance, and try to keep CRM data alive. But in an AI-first GTM model, the role shifts from architect to orchestrator.

Instead of designing systems for humans to follow, RevOps now coordinates systems that support and adapt to human behavior.

Jonathan (aka Coach K) framed it clearly:

“Our job isn’t to create another tool for reps to log into. Our job is to embed insight, execution, and automation directly into their flow.”

That’s what Momentum does with its own AI agent stack. Whether it’s extracting next steps from Gong calls, pushing pipeline risk alerts into Slack, or auto-assigning follow-ups based on customer intent, the focus isn’t on tool visibility: it’s on business motion.

AI changes the equation. The question is no longer: “How do we make reps do more?” It becomes: “How do we make every rep operate at 10x capacity with 1x effort?”

That’s the unlock. And it’s exactly what Amos is proving with Swan.

What Swan and Momentum Have in Common

Though the webinar spotlight was on Amos and Swan, it became clear how closely their philosophy aligns with how Momentum builds.

We don’t believe in AI as a surface layer. We don’t build features for screenshots. We embed AI inside the systems that actually drive revenue (Slack, CRM, email) so that insights, actions, and automation happen exactly where work happens.

We help RevOps turn noisy GTM systems into intelligent orchestration layers. We help sales leaders unlock consistency and clarity across teams. We help managers coach without context-switching. We help sellers stay focused on the customer, not the clicks.

Like Swan, we believe in starting from friction. Fixing the broken motions. Automating with intent. And always putting the human at the center of the loop.

Teams stay at the center. AI just clears the path so their best work isn’t buried under busywork.

Final Takeaways: Building the 100x GTM Engine

If you’re still experimenting with AI tools without a plan for adoption, orchestration, or iteration… this is your wake-up call.

The future of GTM belongs to teams who:

• Map their bottlenecks before buying tools
• Build agent stacks around people, not platforms
• Orchestrate workflows in Slack, where real work happens
• Use feedback loops to evolve AI in motion
• Focus on outcomes, not interfaces
• Commit to a constraint-first mindset: more ARR, fewer people

Most teams are chasing the latest AI feature. The ones that win are reengineering their foundation while everyone else is still testing tools.

Amos said it best: “We’re not scaling headcount. We’re scaling intelligence.”

At Momentum, that’s our playbook too.

What’s Next?

Want to see how your team could scale GTM impact using AI-powered agents and orchestrated workflows?

• Book a demo with Momentum and see how our platform transforms your sales execution layer
• Watch past AI Advantage webinars on our Webinars page

Let’s stop treating AI like a feature set and start treating it like the operating system for modern GTM.

Because when orchestration is the core of your system (not an afterthought), scale stops being theoretical.