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Why We Killed Our Entire GTM Motion (And What Happened Next)

When scaling meant choosing between hiring fast or dying slow, we chose option C.

Author

Amos Bar Joseph
April 07, 2025

"Why would you kill your entire GTM motion after hitting product-market fit?"

That's what a fellow founder asked me last week when I shared that Swan AI was moving from sales-led to product-led growth.

The answer? Because when you're building a $30M ARR company with just 3 founders, you learn to move at a different speed.

Here's what happened when we completely rebuilt our go-to-market in 7 days, and the surprising insights we discovered about scaling in 2025.

When "Good" Isn't Good Enough

First, some context: Our sales-led motion was working. We were closing MM/enterprise deals, running 18+ demos daily, and building a solid pipeline. Many would say "don't fix what isn't broken."

But we saw something others missed: Every closed deal required multiple calls, endless email threads, and constant founder involvement. As demand grew, we faced a choice:

  • Hire a traditional sales team

  • Or fundamentally rethink how we scale

Most startups would choose option 1. After all, that's the playbook - hire fast, scale later.

But here's what most miss about that path:

  • 50 employees = $7M+ annual burn

  • More employees = more approvals

  • More approvals = slower decisions

  • Slower decisions + high burn = disaster in 2025

A 7-Day Transformation

So instead of scaling our team, we decided to scale our operating system.

The transformation wasn't just about adding a "Sign Up" button. We had to reimagine every touchpoint:

OLD OS → NEW OS:

  • Marketing: Lead scoring → Real-time qualification engine

  • Sales: Demo calls → Self-service onboarding

  • Product: Gated features → Instant activation

  • Engineering: Manual setup → Zero-touch deployment

A transformation that typically takes companies 6+ months of planning, we executed in 7 days.

How? When your operating system runs on AI agents instead of approvals, everything moves differently:

  • Every operator becomes their own department

  • Every decision becomes immediate

  • Every bottleneck becomes an API call

No weekly sync meetings. No approval chains. No cross-functional committees.

Just 3 founders, 30+ AI agents, and a singular focus on speed of execution.

Early Results & What’s Next

The early results surprised even us: In our first week of self-serve, we saw logos like Guru and Yotpo complete their entire onboarding without a single sales call. One enterprise customer went from signup to annual contract without any founder involvement - a process that used to take us weeks of demos and negotiations.

But let's be real - it's still far from perfect: Our funnel has major dropoff points we need to fix. The onboarding flow is clunky. And we're seeing patterns in the data that we need to understand better.

I'll share more detailed insights once we have more data. But here's what we know for sure:

The old playbook of adding headcount to solve scaling challenges is dead. The future belongs to small teams that can move fast and adapt faster.

FRAMEWORK OF THE WEEK
Minimum Viable Intelligence (MVI)

Our first AI agent crashed and burned after we spent weeks perfecting its rules.

The breakthrough? When we stripped away 90% of the constraints and built a simple feedback system instead, performance didn't just improve—it skyrocketed.

This contradicted everything we thought we knew about AI implementation. But we soon realized a powerful truth: AI agents don't need perfect instructions—they need perfect feedback systems. The ability to learn quickly matters more than starting with comprehensive knowledge.

Build, Learn, Evolve. The MVI Blueprint

This insight became the foundation of our Minimum Viable Intelligence Framework. Instead of spending weeks documenting every possible scenario, we now focus on building simple feedback loops that allow our AI agents to evolve rapidly. The approach has transformed how we automate everything from customer support to sales qualification.

The MVI approach centers on three critical capabilities:

Know the Basics: Start with only the most common scenarios. We focused on the top 20% that cover 80% of interactions.

Know Your Limits: Create clear signals for when agents should escalate and ask for help. Either create clear boundaries, or use Confidence Level.

Know by Learning: Each escalation creates a learning opportunity. The founders don't just resolve the issue, they help the agent understand how to handle similar situations in the future.

MVI in Action

Here's MVI in action with our support agent:

BEFORE: We tried providing 50+ support scenarios and detailed troubleshooting trees. Agent still failed on 40% of tickets. Customers frustrated, founders overwhelmed with fixes.

AFTER: We equipped the agent with just 10 core solutions. Built a simple escalation system through Slack. Added a learning loop where each escalation becomes new knowledge.

Result: 70% resolution rate in week one. 90% by week four.

*Critical touch points like pricing and billing remain out of scope.

Getting Started

Getting started with MVI isn't about building the perfect system—it's about taking the first small step.

Pick one repetitive task, equip an AI agent with minimal knowledge, and create a simple way to capture when it needs help. The magic isn't in your first version—it's in what your system learns after a week of real-world feedback.

The Movement Grows

You're not just reading about a revolution in business. You're watching the death of bureaucracy in real-time.

Years from now, you'll remember when you first realized: The age of the autonomous business had begun.

And you weren't just there to witness it - you were there to shape it.

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