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Welcome to the first post in our new blog series where we take you chapter-by-chapter through our new book, "Ignite Your GTM With AI", starting with the one that sets the stage for everything: Chapter 1, "The Lay of the AI Land in 2025."
And we will start by being honest with you: the state of AI is noisy.
Every day, there's a new breakthrough in generative AI. You read about OpenAI, Google’s Gemini, or Anthropic’s Claude. You see Microsoft embedding Copilot into every application. You're constantly told that automation is the future, that LLMs will change everything, and that you need an AI strategy right now.
In this frenzy, most leaders are scrambling. They're asking, "Which AI tools should we buy?" or "How do we build chatbots for our website?"
But what if those are the wrong questions?
In Chapter 1, we open with a jarring insight from Jacco Van Der Kooij, Founder of Winning by Design. He asks a room of leaders a simple question: "How many companies do you think have grown from $1 to $100 million in the past 18 to 24 months?"
The answer isn't one or two. It's a couple of dozen.
While most of the business world is struggling, a small cohort of "AI-Natives" is achieving impossible hypergrowth. This isn't an exception; it's a new pattern.
But the reason for their success is not what you think. And most companies are already being left behind, optimizing for a game that’s already over.
The Two Great Myths of AI Success
Chapter 1 immediately dismantles the two most common assumptions about how these AI-Natives are winning.
Myth 1: It’s All About Consumption-Based Pricing
The prevailing wisdom is that the future is "pay-as-you-go." Everyone assumes these new companies are riding the wave of consumption models.
The reality? The majority of these hypergrowth AI-Natives run on subscription models or pre-committed credits.
Think about ChatGPT Plus—it's a monthly subscription. Or API commitments, which are essentially pre-paid usage. Pure consumption is the exception, not the rule.
So, if it’s not the business model, what is it?
Myth 2: It’s About a Superior AI-Powered User Experience
The second assumption is that these companies must be winning because their websites are magical. They must have amazing, human-like AI agents greeting you, offering radically better use cases and experiences than anyone else.
Again, the data shatters this myth.
Jacco points out that "hardly any" of these breakout companies actually give you an "AI-like experience on their website".
So, they aren't winning with revolutionary pricing or a flashy AI-powered front-end.
This means countless companies are "optimizing for the wrong variables". They're chasing surface-level implementations while completely "missing the architectural changes that create exponential advantage".
The Real Secrets: Convergence and Speed
If the myths are wrong, what's the truth? Chapter 1 reveals two "secrets" that redefine how AI-Natives operate.
Secret 1: The User-Decider Convergence
This is a fundamental shift in how AI adoption happens.
In traditional SaaS-Native companies, the people using the software are not the people deciding to buy it. Think back 20 years: an executive team decided "You're going to use Microsoft Excel... learn to live with it". The user had no choice.
In the AI-Native world, this model is inverted.
The users are the deciders and champions. Because they are the ones using the product, "they know what they want, they know what they like, and they can force the decision". This creates adoption that spreads because people genuinely want the solution, not because procurement mandated it. The AI applications they choose are adopted from the bottom up, with incredible speed.
Secret 2: Real-Time Decision Making
This second secret explains their mind-boggling growth rates.
In a typical SaaS-Native company, if a sales win rate jumps from 22% to 27%, it might take four weeks for that signal to "bubble up" to leadership, and three months before any action is taken.
In an AI-Native company, how long does it take? "Hours, at most a day".
This is the core of the new technology. Intelligence is "woven into the decision-making fabric itself, rather than bolted onto legacy workflows". AI-Natives are built on a real-time data infrastructure.
The real differentiator isn't a flashy chatbot. It's "real-time data and closed-loop systems".
The Four Data Problems Blocking Your AI Future
While AI-Natives build their operations around these intelligent data flows, most traditional companies are stuck. Chapter 1 outlines four fundamental problems that prevent them from ever competing.
As you read these, ask yourself if they sound familiar.
1. Unstructured Data Your company is drowning in valuable insights it can't use. 95% of conversations remain unformatted and unused. Think about all the intelligence locked away in Slack messages, sales call transcripts, customer emails, and product logs. It's all "free-form conversations" that an AI system cannot process consistently. Without a structured format (a "common language") your machine learning models are guessing.
2. Incompatible Data Even if your data is structured, your sources don't talk to each other. Your sales calls are in Gong, your CRM data is in Salesforce, your support tickets are in Zendesk, and your product metrics are in Amplitude. Each function uses its own format, so patterns can't be compared.
A customer might complain about a missing feature on a support call, sales might note "interest in advanced capabilities" in the CRM, and product data shows they're hitting usage limits. These three signals together scream "expansion opportunity". But because your AI systems can't connect these dots, the opportunity is missed entirely.
3. Disconnected Data This is the most damaging problem: your feedback loops are broken. Insights "don't flow back into the system".
Your product team discovers that users who adopt Feature X in week one have 80% higher retention. That's gold! But that intelligence stays with the product team. Sales never finds out, so they keep selling generic benefits. Marketing doesn't know, so they keep targeting the wrong audience.
AI-Natives, by contrast, are built on "continuous feedback loops". Insights from customer behavior automatically inform acquisition strategies. This creates "compound growth"—the holy grail of venture-backed business.
4. Incomplete Data Finally, your data is full of gaps. A prospect visits your pricing page, but you don't know their company size. A customer churns, but you never captured why. These gaps "cause AI to mislead, not just misfire".
AI-Natives use AI agents to proactively fill these gaps. They enrich records, identify buying committees, and infer preferences automatically. They move from static segmentation (like "Enterprise") to dynamic, real-time segmentation (like "user who just became a power user and is advocating on LinkedIn").
Why Is This Happening Now? The Three Black Swans
This entire shift was triggered by an unprecedented convergence of three "Black Swan" events.
- COVID: Suddenly, over a billion people became dependent on SaaS (Zoom, DocuSign, etc.), proving the model at a global scale.
- The End of Zero-Interest-Rate Policies (ZIRP): In 2022, the macro-shift "pulled back" the era of cheap money.
- The Emergence of AI: An event "totally independent of the first two".
These three forces collided, compounding each other and exposing the "systemic failures" that SaaS companies had ignored for years. The rise of artificial intelligence is simply "exposing what SaaS-Natives should have addressed long ago".
It exposed that boardrooms are still obsessed with acquisition over retention. It exposed that companies are trying to scale without the right structure. As Jacco notes, we have "billion-dollar companies that are functionally no more mature than $20 million companies".
The Path Forward: Architecture, Not Tools
The conclusion of Chapter 1 is clear: the next two years will separate the winners from the losers, "not by the adoption of tools, but by the adoption of architecture".
The path forward requires three new imperatives:
- Focus on Compound Growth: It’s "always about growth, growth, growth". This requires compound, exponential growth, which can only be achieved with "growth loops". AI's role is to "identify and accelerate" these loops.
- Build a Customer Success Architecture: You cannot get to a billion dollars without successful customers. "Recurring revenue only comes from recurring impact".
- Adopt a Systems-First Approach: For years, companies over-indexed on people because they were "cheap" (funded by ZIRP). Today, the funding has shifted to systems—to AI, large-scale infrastructure, the compute, the GPUs, the NVIDIA chips. We are moving "toward systems".
The Momentum Architecture: Your Systems-First Solution
This brings us to the central challenge for every GTM leader reading this.
You don't have a tool problem. You have an architecture problem.
You're facing the four horsemen of data chaos: your data is Unstructured, Incompatible, Disconnected, and Incomplete.
You can buy all the AI tools in the world. You can get licenses for ChatGPT for your whole team. You can experiment with open-source models, try fine-tuning your own LLM, or plug into any API from OpenAI, Anthropic, or AWS.
None of it will matter.
Your shiny new AI models will be fed garbage data from disconnected silos. They will "mislead, not just misfire". You'll have an AI strategy on paper, but zero real-world impact. You will not achieve the "real-time decision making" of your AI-Native competitors.
This is the problem Momentum was built to solve.
Momentum is the Intelligence Architecture that Chapter 1 proves you need. We are the "systems-first approach" made real.
We are the layer that connects your entire GTM ecosystem. We sit between all your disparate systems (Salesforce, Gong, Amplitude, Zendesk, Slack, email) and turn them into a single, cohesive, real-time infrastructure.
- We Solve "Incompatible Data": Momentum’s no-code workflows connect all your providers and tools, allowing signals to be passed between them instantly.
- We Solve "Unstructured Data": Our automation can capture and structure data in real-time. You can build frameworks like SPICED directly into your workflows, enforcing a "common language" across all your teams.
- We Solve "Disconnected Data": Momentum is the engine for your "closed-loop systems". When product data shows a "retention" signal, Momentum can instantly trigger a workflow that alerts the sales team, adds the user to a marketing campaign, and creates a task for CS—all in real-time.
- We Solve "Incomplete Data": You can use Momentum to orchestrate AI agents that "proactively fill gaps". When a new lead comes in, Momentum can trigger AI development workflows that enrich the record with firmographic data, identify the user's LinkedIn profile, and score their intent before it ever lands in a rep's queue.
This is how you move from "three months" to "three hours".
Your Next Step
Stop optimizing for chatbots on your website. The real winners of the AI race are using GenAI and AI systems to build an internal architecture that makes their entire organization faster and smarter.
They are building the "systems-first" organization that Chapter 1 demands.
You don't need another AI tool. You need an architecture.
The insights in this post are just the beginning. To get the full picture and the complete framework for building your own intelligence architecture, you need to read the book.