The AI GTM Playbook Is Here: Announcing the Launch of "Ignite Your GTM with AI"

Let's start with a truth that everyone in GTM feels but nobody says on a conference stage: we're all living in the "Great AI Anxiety".

If you're a revenue leader, you're feeling it. It's the gnawing sense that while you're experimenting with email drafts and testing the latest AI SDR tool, others are playing a fundamentally different game. 

You're feeling the pressure from your board, who are asking increasingly pointed questions about your "AI strategy". You're seeing the headlines, the funding rounds, and the endless stream of "AI tips," and it all feels... chaotic.

You're not imagining it. And you're not alone.

The problem is, this anxiety is justified. The disconnect between AI's promise and today's operational reality is massive. While consulting firms report that 78% of companies have "adopted AI," our own analysis of over 1,000 GTM organizations tells a very different story: only 7.6% have operational AI, meaning AI that is deeply integrated into their actual workflows, processes, and systems .

The other 92% are stuck in pilot purgatory. They're buying more tools, creating more silos, and layering complex technology onto processes that were already broken. They're optimizing the un-optimizable. They're just finding new, more expensive ways to do "faster, louder bad things".

This is more of an architecture problem than a tool one.

That's why we're launching the book that delivers the blueprint to fix it. "Ignite Your GTM with AI" is officially here!

And you don't have to worry about this being another book of abstract theories. We've built it as the definitive playbook for a new, mandatory GTM discipline: Intelligence Architecture.

Stop Using AI. Start Architecting It.

The leaders who are winning with AI aren't asking "Which AI tool should I buy?" They're asking, "How do I architect intelligent systems that didn't exist before?".

That's the shift this book enables you to make.

Intelligence Architecture is the framework for designing intelligent systems that orchestrate data, AI agents, and human expertise to create entirely new forms of organizational advantage. It’s the blueprint for building an AI-native GTM engine from the ground up, an engine that learns, adapts, and compounds in value.

We—Santiago Suarez Ordoñez, Ashley Wilson, Moiz Virani, and Jonathan Kvarfordt —didn't write this alone. We brought together over 30 of the most brilliant operators and leaders who are actually building this future.

The entire book is built on the four pillars of this new architecture:

1. Context Engineering: How you give AI the structured information it needs to stop being a "probabilistic guesser" and start delivering deterministic business outcomes.
2. Human-AI Orchestration: How you design workflows where AI handles the cognitive "grunt work" (categorization, extraction, summarization) to amplify your team's human skills (strategy, empathy, trust).
3. Signals Mapping: How you move beyond "tracker theater" and simple keyword alerts to detect the subtle, predictive behavioral patterns hidden in your unstructured data.
4. Transparent Reasoning: How you build systems that can explain their 'why', earning the trust of your team and creating auditable, improvable workflows.

The Playbook: A Journey Through the New GTM

"Ignite Your GTM with AI" is structured to walk you through your entire GTM motion, chapter by chapter, showing you how to rebuild it around this new architecture.

Part 1: The Foundation (What, Why, and How to Start)

First, we lay the foundation. In Chapter 1, Jacco van der Kooij (Winning by Design) shatters the two biggest myths in AI: that hypergrowth is coming from consumption pricing or flashy chatbots. He reveals the real secrets of AI-Natives are deep architectural shifts: User-Decider Convergence and Real-Time Decision-Making.

In Chapter 2, our CTO Moiz Virani and GTM legend Mark Roberge (Stage 2 Capital) demystify the tech. They dismantle the myths of AI as a "mystical oracle" or an "employee-in-a-box" and provide the definitive primer on the Four Dimensions, showing how they solve the core architecture problem.

In Chapter 3, Kyle Norton (Owner) gives you your starting blocks. He proves that AI transformation cannot be delegated and provides the practical 90-Day Foundation Sprint to get "small wins in the bag" and build momentum.

In Chapter 4, Brendan Short (The Signal) and Elio Narciso (ScaleStack) end the paralyzing "Build vs. Buy" debate. They show why it's the wrong question, warning against the "Hidden Maintenance Trap" of building (where 95% of the work is after launch) and the "Vendor Dependency Trap" of buying. The solution is a hybrid "architectural assembly" model.

Part 2: Architecting the GTM Motion (From ICP to Deal Close)

With the foundation set, we rebuild the GTM engine.

In Chapter 5, Jordan Crawford (Blueprint) delivers a tactical masterclass. He argues your demographic-based ICP is garbage and that "you can't personalize your way out of a targeting problem" . The solution? Stop asking AI to write copy. Use its real power for data discovery. This chapter provides the templates for finding Pain Qualified Segments (PQS) and delivering Permissionless Value Propositions (PVP)—messages so valuable, "people would pay to receive them".

In Chapter 6, Elaine Zelby (Tofu), John-Henry Scherk (Growth Plays), and Micael Oliveira (Amplemarket) show you how to scale that precision. They warn that the "AI SDR" is a "slot machine for desperate sales leaders". The real path to scale is "Surround Sound SEO" (being omnipresent where buyers are actually researching) and moving from shallow personalization to deep, systemic contextualization.

In Chapter 7, a powerhouse team—Kyle Norton (Owner), Mark Turner (Demandbase), Brian Dietmeyer (Closestrong), Navin Persaud (1Password), Oran Akron (Monday), and Kevin Chu (ElevenLabs)—tackles the "chaotic art studio" of sales execution. Reps spend 70%+ of their time on admin, not selling. This chapter shows you how to build a "neutral spinal cord" that automates data capture, provides objective risk flags, and makes your CRM data finally tell the truth.

In Chapter 8, Julian Teixeira (1Password) and Andy Mowat (Whispered) solve the "signal poverty" problem. Your org is drowning in "scattered noise" , yet your teams only track 2-3 obvious signals. This chapter provides the architecture to continuously detect and correlate predictive patterns from all your systems, turning data into a true competitive advantage.

Part 3: Architecting the Organization (From Enablement to Exit)

Finally, we apply the architecture to your entire organization.

In Chapter 9, Toby Carrington (Seismic), Sheevaun Thatcher (Demandbase), Christina Brady (Luster), and Amanda Whiteside (Uber) declare traditional enablement—a "massive filing cabinet of stuff"—is dead. This chapter solves the "diagnostic crisis" with "Predictive Enablement"—an architecture that identifies skill gaps and delivers just-in-time coaching before a rep makes a revenue-impacting mistake.

In Chapter 10, Mandy Cole (Stage 2 Capital), Akash Bose (Innovious Capital), Tessa Whittaker (ZoomInfo), Megan Prince (Zeni), and Cris Mendes (Momentum) fix the "most dreaded ritual in revenue leadership": the forecast call. Black-box AI won't work because leaders can't trust it . The solution is triangulation—an architecture that orchestrates AI-driven analysis with human judgment to deliver a forecast you can both trust and act on.

In Chapter 11, Dione Hedgpeth (Momentum Advisor & ex-Sumo Logic) and Alphonso Calanoc (Momentum) tackle the "CSM Conundrum". We hire CSMs to be "trusted advisors" but trap them as "process quarterbacks" . This chapter provides the architecture to automate the quarterbacking, ensuring intelligence flows from sales to CS and freeing your team to focus on the human relationships that drive retention and expansion.

In Chapter 12, Seth Stover (Tiderise) reframes the future of talent. The old "cost arbitrage" model of outsourcing is dead. The new model is "Capacity Architecture". Learn how to build an orchestrated system of internal, external, and AI talent, starting by creating a robust Knowledge Base (from simple videos!) that prepares your processes for an automated future.

Finally, in Chapter 13, we bring it all home. We lay out the "Million-Dollar Decision"—the choice between buying more tools or investing in infrastructure. We give you the AEIOU Framework (Aggregation, Extraction, Inputs, Outputs, Under the Hood) as the final blueprint for building an intelligent GTM engine that creates compounding, strategic, and insurmountable competitive advantage.

Your Playbook Is Ready.

Rather than just being another book about AI, this is a new way of thinking.

Better yet, it's a new way of building.

To make this transformation practical, every chapter concludes with "The Intelligence Architect's Guide". This is your hands-on toolkit. It’s packed with checklists, templates, diagnostic tests, implementation frameworks, and warnings about common pitfalls. We give you the "what" and, more importantly, the "how."

The window for this transformation is closing. The next 24 months will separate the "tool adopters" from the "Intelligence Architects" .

The playbook is here. Stop experimenting. Start architecting.

"Ignite Your GTM with AI" is available now. Get your copy here.