The AI-First Revenue Engine
Why Your Next GTM Strategy Begins Before Buyers Meet You
TL;DR
What This Covers
AI has become the new front door to your revenue engine. Buyers are forming opinions, comparing vendors, and making recommendations before they ever visit your website — and they’re doing it through AI systems that interpret your brand with or without your involvement. This guide reveals why GTM leaders can no longer treat AI as a set of disconnected tools and instead must design a unified, systems-level strategy that shapes how AI understands their brand, orchestrates their workflows, and accelerates their teams.
Inside, you’ll learn the structural shifts defining modern GTM: how AI now mediates brand perception upstream, why positioning must be rebuilt for machine comprehension, how fragmented AI adoption undermines alignment, and how human + AI orchestration becomes the new foundation of predictable growth.
You’ll find the frameworks taught inside Pavilion’s AI in GTM School—from Liza Adams’ AI-driven brand architecture, to Josh Carter’s workflow design principles, to Ryan Staley’s AI teammate model, to the governance systems Andy Jolls and Jonathan Moss use to build defensible, board-ready AI strategies.
This is the operator-grade roadmap for CMOs, CROs, and GTM executives who need to re-architect their revenue engines for an AI-first world — where brand narratives are synthesized before a salesperson engages, workflows scale beyond human speed, digital twins pressure-test strategy, and leadership advantage comes from understanding how AI learns. It’s the systems-level approach to building a modern GTM engine that’s predictable, aligned, defensible, and built for the new rules of buyer behavior.
The AI mandate did not arrive gently. It landed on the desks of GTM leaders with the force of an unexpected board request — abrupt, non-negotiable, and strategically ambiguous. One quarter, AI was a curiosity reserved for early adopters and individual contributors experimenting on the margins. The next, CEOs were asking CMOs and CROs how quickly AI could cut CAC, accelerate pipeline velocity, and compress operating costs without compromising brand, experience, or governance.
And the truth is uncomfortable: most revenue organizations are nowhere close to answering that question.
Not because the potential isn’t there, but because AI has entered GTM the same way cloud entered IT — unevenly, reactively, and without a unifying strategy. Every function is now running its own informal experiment. Marketing uses AI to draft content. Sales plays with automation tools they don’t fully understand. Revenue Operations tests workflows in isolated pockets. CS cautiously dabbles in summarization and ticket routing. Everyone is “using AI,” yet no one can articulate how these experiments ladder up to revenue impact, operational leverage, or competitive differentiation.
It’s fragmentation masquerading as progress — the exact environment where inconsistent outputs, misaligned messaging, and avoidable inefficiencies thrive.
Meanwhile, expectations are rising faster than adoption maturity. Boards are asking for AI-driven productivity gains with measurable financial upside. CFOs want attribution they can defend. CEOs want to know which parts of their revenue engine can be rebuilt with automation and which cannot. What they’re really asking is whether their GTM leader can create strategic clarity in an environment defined by uncertainty.
Most can’t — not because they lack capability, but because they lack a roadmap grounded in real operational experience.
This is the gap AI in GTM School was built to close.
Instead of treating AI as a tactical accelerant, Pavilion’s instructors — operators who have deployed AI across public companies, PE-backed SaaS platforms, and high-growth environments — teach executives how to re-architect their revenue engines from the inside out. They’re the ones who have built human-plus-AI teams, orchestrated cross-functional workflows, analyzed brand visibility through the lens of AI systems, and automated the repetitive motions that quietly eat 15–20 hours of every GTM team’s week.
And what they teach is clear: AI is no longer a tool. It is now a structural force shaping customer perception, organizational design, and competitive advantage.
That is where our story begins.
When AI Becomes the First Touch: Your Brand is Being Judged Before You Arrive
If you want to understand how radically the buying journey has shifted, consider a simple reality Liza Adams emphasized in her AI brand course:
"Yesterday, perception is reality. Today, AI response is reality."
- Liza Adams, AI Marketing & GTM Advisor
In other words, your brand is no longer competing in the marketplace you think it’s in.
It’s competing in the marketplace AI systems construct on your behalf — and they’re constructing it whether you participate or not.
In live Pavilion sessions, Liza demonstrated this dynamic with disarming clarity. When she asked Perplexity to compare Asana, Monday.com, Smartsheet, and Wrike, the system didn’t simply summarize product pages.
It:
- Pulled data from ten or more sources —Forbes, Reddit, Zapier, LinkedIn, YouTube.
- Generated structured scoring matrices.
- Identified ideal and non-ideal customer profiles.
- And ultimately concluded, “Monday.com stands out for your situation.”
No SDR. No nurture spiral. No competitive battlecard.
Just an AI engine making a recommendation with the authority of consensus.
And buyers trust these systems more than most GTM teams are willing to admit. The data speaks bluntly:
AI search visitors convert at 4.4x the rate of traditional organic traffic.
(Source: When AI Forms Your Brand Opinion First – Pavilion University)
By the time they land on your site, they’ve already formed a narrative — engineered not by you, but by the information ecosystems AI consumes.
This is why organizations now face a new category of brand risk:
AI invisibility, AI misinterpretation, and AI misalignment.
Executives talk about SEO declines, but the real threat is semantic displacement: if AI doesn’t understand your ICP, your differentiators, or your ideal use cases, you don’t just lose visibility — you lose strategic fit.
What does this look like in practice?
Liza highlights a scenario most leaders miss. If AI misunderstands your ICP and presents you to the wrong audience:
"It could tank our profitability… churn rises, negative sentiment enters the model, and now we have a difficult brand to deal with."
This is exactly how AI systems evolve — they learn from outcomes, from comments, from reviews, from public inconsistencies.
And if your digital footprint is incoherent, AI interprets it as truth.
This is why AI School doesn’t begin with tools. It begins with the most strategic — and most ignored — question:
What does AI think your brand is?
It’s a question with board-level implications because you can no longer control your buyer journey from the top of the funnel. The funnel now starts upstream — inside the logic of AI systems that act as filters, analysts, and advisors long before a human salesperson or marketer engages.
The New Buyer Journey (As Observed in Pavilion Sessions)
| Traditional Journey | AI-Driven Journey | Consequence |
|---|---|---|
| Buyer searches Google → scans listings |
Buyer asks AI → receives structured comparisons | Your differentiation is compressed or misunderstood |
| Buyer visits website → forms opinion | AI forms opinion → then buyer visits website | You inherit the perception AI created |
| Buyer requests demo after research | AI recommends product instantly | You may never enter the conversation |
This is why Liza’s warning matters: “AI judges brands before humans do.”
And why AI School positions brand adaptation as a strategic imperative, not a marketing exercise. Because the only thing worse than losing a competitive deal is never appearing in the consideration set at all.
Why This Matters for Executives
As a CMO, VP of Marketing, or CRO, you can’t delegate this.
Brand visibility, brand trust, and brand relevance are no longer human-mediated. They are AI-mediated — and the systems doing the mediation are only as good as the signals they ingest. If your brand is:
- inconsistent
- incomplete
- unstructured
- unclear about ICP fit
…AI fills the gaps. And it rarely fills them the way you’d want.
This is where AI School begins to become indispensable.
The program does not teach you how to “rank” in AI systems. It teaches you how to shape the information architecture that AI consumes, so that your brand is interpreted — not distorted.
Because in a world where AI produces recommendations before humans form opinions, the organizations that win aren’t just the ones with better messaging.
They’re the ones who understand how AI learns.
First, we exposed the uncomfortable truth — that AI now shapes your reputation before you do — now, we’ll confront the natural next question:
If AI is already interpreting your brand, how do you make sure it’s interpreting the right story?
This is where most executives underestimate the shift.
Traditional positioning has always been designed for humans: clean value props, crisp messaging, and ICP narratives crafted for cognitive ease. But AI doesn’t think like a human. It doesn’t absorb stories, emotion, or nuance. It ingests signals, cross-references them across thousands of sources, then synthesizes a conclusion using logic, pattern recognition, and relevance scoring.
Liza Adams makes this point repeatedly:
"Don’t chase the algorithms… focus on deeply understanding customers."
Because AI doesn’t care about your copy style, your tagline, or your brand promise. It cares about whether the information architecture surrounding your company is coherent.
This is where executives get caught flat-footed: AI doesn’t read what you publish — it reads everything.
Your SEO pages, competitors’ SEO pages, your reviews, customer comments, product documentation, LinkedIn chatter, YouTube content, comparisons you never wrote, and feedback you never saw.
AI forms its opinion of your company based on all of it — and then serves that opinion to your buyers as if it were fact.
Which means the central question facing
GTM leaders becomes:
Does AI understand who you are for — and equally, who you are not for?
This distinction matters more than most executives appreciate, because AI rewards precision and penalizes ambiguity. Humans tolerate broad messaging. AI does not.
In one Pavilion session, Liza showed how a company’s refusal to define who it was not built for created a cascade of issues inside AI systems:
“If we get served up for everything… it could tank our profitability. They leave, they say bad things, it gets into the model, and we now have a much harder brand to deal with.”
This is the dark side of AI-driven exposure:
misaligned recommendations lead to misaligned customers, which lead to negative signals, which feed back into AI models—creating a self-reinforcing loop of bad fit and bad sentiment.
No amount of sales enablement can fix a flywheel like that.
Why the Old Way of Positioning Fails in an AI World
Most positioning frameworks were built for a world where humans discovered brands through:
- websites
- events
- analyst reports
- peer recommendations
- sales conversations
AI collapses that sequence. It merges all those touchpoints into a single synthesized answer. And the mosaic it builds depends on how your content, reputation, and brand signals cluster together across the internet.
3 things AI cares about that traditional GTM ignores
- Semantic clarity
Does your messaging use consistent definitions that align with customer language? - Use-case specificity
Do you articulate situations where you win — and where you don’t? - Source diversity
Are your strengths reinforced across multiple sources, not just your own channels?
Executives often think the solution is to publish more content. But more content doesn’t solve a structure problem — it amplifies whatever inconsistency already exists.
This is why AI in GTM School puts disproportionate weight on what many leaders still treat as a secondary exercise: re-architecting your positioning for AI comprehension.
Because if AI is now your first salesperson, then positioning is no longer a messaging exercise. It’s a machine readability exercise.
How AI Interprets Your Brand
| AI evaluates... | By looking at... | Which means... |
|---|---|---|
| Relevance |
Use-case specificity, ICP clarity | You must define who you’re for and who you’re not for |
| Credibility | Case studies, reviews, 3rd-party sources | A single negative thread can outweigh 10 polished landing pages |
| Comparative Fit | Competitor patterns + buyer scenarios | You’re competing in a recommendation engine, not a search engine |
| Consistency | Overlap between website, PR, community, social | Inconsistency = confusion = degradation in ranking & recommendation |
This table underscores a core truth: AI constructs reality based on signals, not intentions.
The Pivot GTM Leaders Must Make Now
In this AI-first buying environment, the role of GTM leadership shifts from crafting messages to crafting meaning within AI ecosystems. It’s subtle, but profound.
In a pre-AI world, CMOs shaped perception by:
- Launching campaigns
- Refining messaging
- Training sales teams
- Managing brand narratives
In an AI world, CMOs shape perception by:
- Structuring data
- Codifying ICP clarity
- Tightening brand semantics
- Feeding consistent signals across channels
- Building frameworks AI systems can interpret cleanly
This requires GTM leaders to think less like ad strategists and more like information architects. It’s no longer enough to have a strong position. You must have a position that AI can recognize, categorize, and express with confidence.
This is the exact kind of thinking AI in GTM School was built to systematize. Because once executives understand how AI understands them, they can begin redesigning their revenue engines accordingly.
Design an AI System you can scale.
Most GTM teams believe they’re “doing AI” because every department is experimenting with it. Marketing drafts content in ChatGPT. Sales plays with automated outreach tools. RevOps tests agents. CS tries AI summaries. On paper, AI activity is high. In practice, AI impact is low.
This is the pattern Pavilion instructors see repeatedly: fragmented AI adoption masquerading as progress. Each team is acting independently, with no shared standards, no governance, and no meaningful connection to pipeline or efficiency outcomes. AI becomes noise instead of leverage.
Organizations making real progress with AI are not the ones running more experiments — they’re the ones building better systems.
Where AI Experiments Break Down (Across Every GTM Function)
Executives encounter the same failure modes across Pavilion’s courses:
- Different tools, no standards: Every team picks its own AI, creating chaos.
- Inconsistent outputs: Messaging, tone, strategy, and positioning drift.
- No governance: No version control, no approvals, no quality baseline.
- Silos deepen: Each function automates in isolation, making alignment harder.
- No measurable impact: Leaders can’t tie AI usage to pipeline, CAC efficiency, or productivity.
Individual experiments may increase velocity, but only connected systems create leverage. This is why orchestration—not tooling—is the foundation of AI School.
AI Maturity Model: How GTM Organizations Actually Transform
Liza’s four-stage GTM maturity model is the most accurate representation of how real companies evolve with AI. It’s drawn from live deployments—not theory—and it explains why so many companies stall at “playing with tools” instead of building advantage.
The Four Stages of an AI-Enabled Revenue Organization
| Stage | Definition | Organizational Symptoms |
|---|---|---|
| 1. AI as a tool |
Individuals use AI tactically | Fragmented usage; inconsistent outputs; no measurable value |
| 2. AI as a Teammate | Humans + AI share workloads in silos | Gains within functions; breakdowns between functions |
| 3. Cross-Functional Orchestration | AI bridges workflows across Marketing, Sales, CS, Ops | Signals flow smoothly; improved collaboration & efficiency |
| 4. Outcome-Aligned Expertise Pool | Teams restructured around shared KPIs | KPIs tied to revenue, time-to-value, customer outcomes |
Most orgs today sit between Stage 1 and Stage 2. AI in GTM School is designed to move leaders into Stage 3—and build the foundations of Stage 4.
A Real Transformation Example: From 20 AI Teammates to Over 100
Liza teaches a case study of a public company that demonstrates what Stage 3 and Stage 4 look like in practice:
- They began with 25 human teammates + 20 AI teammates.
- Within ~6 months, they scaled to over 100 AI teammates.
- They reached 100% AI adoption across workflows.
- They had 75 human “AI trailblazers” driving change.
- And 57 AI teammates were fully embedded in systematized workflows.
The biggest surprise wasn’t technical—it was human:
“The hardest part was not the AI. It was us — the human beings.”
The shift succeeded because they didn’t deploy tools. They redesigned workflows, roles, and KPIs to accommodate AI as a co-worker.
This is the inflection point where AI begins transforming revenue engines—not because the models get better, but because the organization learns how to work with them.
AI Only Works When the Workflow Works
If Liza defines what an AI-powered GTM organization becomes, Josh Carter explains how it operates.
In “AI for Modern Marketers,” Josh reframes AI as a workflow discipline. AI is powerful only when the data feeding it is structured, enriched, and consistent. His refrain is constant:
“The foundation for any good campaign is having really good data… It’s the most boring thing, but it is the foundation for everything.”
This is a subtle but critical distinction. Most leaders assume AI is a shortcut around process. In reality, AI magnifies whatever process it sits on. Josh uses Clay to illustrate this, not because Clay is the star, but because Clay makes the system visible:
- Enrichment becomes strategy — like identifying buyers with L&D budgets through job posting analysis.
- Documentation becomes orchestration — if a GTM motion can’t be mapped in Scribe, it can’t be automated.
- Predictability becomes scale — AI only performs reliably when the underlying motion is coherent.
AI School builds on this principle by teaching leaders how to architect workflows that AI can reinforce rather than destabilize.
Defining the AI Teammates Inside GTM
If Liza gives the architecture and Josh gives the plumbing, Ryan defines the workforce.
Ryan’s “AI-Augmented GTM Team” curriculum reframes AI not as a toolset but as a set of teammates with specific responsibilities. His insight, backed by data from 300+ OpenAI implementations and 4,000+ adoption surveys, is straightforward:
Organizations outperform when they assign AI clear roles.
Organizations underperform when they tell employees to “use AI more.”
The Four AI Teammates
- Copilot — supports drafting, coaching, and execution in real time.
- Delegate — handles repeatable, administrative, time-consuming tasks.
- Strategist — elevates research, synthesis, and planning.
- Revenue Teammate — maintains unified customer intelligence across functions.
Each role maps directly to a friction point inside the traditional revenue engine:
| Friction point | AI Teammate that Solves it |
|---|---|
| Slow execution |
Copilot |
| Low productivity & wasted time | Delegate |
| Weak analysis or inconsistent strategy | Strategist |
| Fragmented customer understanding | Revenue Teammate |
This is how Ryan reframes GTM capacity: not as “AI replacing jobs,” but as AI removing friction so humans can operate at higher leverage.
And the results Pavilion members have documented—3x more leads contacted, 42% higher response rates, 65% SDR time savings, 28% lower cost per meeting—are direct consequences of defining AI’s job, not AI’s tool.
How These Three Perspectives Form the Foundation of AI School
When you integrate Liza’s organizational model, Josh’s workflow design, and Ryan’s AI teammate structure, a full picture emerges:
- Liza defines the structure of an AI-first revenue organization.
- Josh ensures the workflows are clean, enriched, and automatable.
- Ryan defines the roles humans and AI share inside the workflow.
This triad becomes the operating system of an AI-first GTM engine.
It’s also what most AI programs miss entirely. They teach tools, not systems. Prompts, not orchestration. Experiments, not transformation.
AI in GTM School was built to close this gap — to turn scattered efforts into disciplined, cross-functional, measurable systems that actually change how revenue teams operate. And everything that comes next—simulation, digital twins, risk modeling, strategic planning—sits on top of this foundation.
Once GTM leaders understand how to orchestrate human and AI workflows, they encounter a new possibility — one that fundamentally reshapes how strategic decisions are made. AI doesn’t just accelerate execution; it changes the way executives think by giving them the ability to simulate scenarios before making high-impact decisions.
This capability is what Liza Adams calls digital twins — AI-powered replicas of people, markets, competitors, or customers that allow leaders to battle-test ideas with extraordinary speed and depth.
And in Pavilion’s advanced AI sessions, this is where executives often have their breakthrough moment: AI is no longer a tool. It’s a rehearsal environment for thinking.
Why Simulation Changes the Executive Game
The concept is simple: A digital twin is a model of a persona — you, your CEO, your buyer, your competitor, your board — built with structured context and trained on information the real audience would use to make decisions.
The value is profound.
“The value of a digital twin isn’t training it to be like us. The value is helping us find our blind spots… elevate our thinking… and understand how others might perceive our outputs.”
— Liza Adams
Executives typically operate with three constraints:
- Limited time to think deeply
- Limited access to honest feedback
- Limited visibility into how their work is perceived
Digital twins remove those constraints. They provide simulations — safe, fast, repeatable environments where strategy is tested before it hits the real world.
Want to know how a CMO will react to your messaging? Simulate the CMO.
Want to know how a CFO will challenge your ROI assumptions? Simulate the CFO.
Want to know how your ICP compares you to a competitor? Simulate the ICP.
Executives aren’t guessing anymore. They’re modeling.
Inside Pavilion Sessions: How Digital Twins Are Used in Practice
Across Pavilion programs, four categories of digital twins consistently emerge:
1. Executive Twins
Simulate CEO, CFO, CRO, or board-level perspectives to:
- Stress-test strategic narratives
- Make decisions that withstand scrutiny
- Pressure-test ROI assumptions
- Anticipate executive objections
Executives use this to refine board decks, forecast presentations, and GTM strategies before presenting live.
2. Customer / ICP Twins
Simulate the behavior and reasoning patterns of buyers at different levels of sophistication.
These twins evaluate:
- Positioning clarity
- Feature-benefit resonance
- Sentiment
- Competitive comparisons
- Use-case fit
This is effectively a “customer wind tunnel” where ideas are tested before market exposure.
3. Competitor Twins
Organizations use competitive twins to:
- Assess strategic vulnerabilities
- Predict response patterns
- Evaluate how competitors frame strengths/weaknesses
- Understand comparative market narratives
The goal isn’t to copy competitors. It’s to understand how AI systems already compare you to them.
4. Persona Twins (Internal or External)
Simulate:
- Influencers
- Industry analysts
- Partners
- Internal stakeholders
This uncovers perception gaps and messaging blind spots.
The Digital Twin Ladder of Value
| Twin type | What it simulates | Executive value |
|---|---|---|
| Self Twin | How you think, decide, and bias | Identify blind spots; improve clarity |
| Executive Twin | CEO, CFO, CRO perspectives | Build board-ready strategy and messaging |
| Customer / ICP Twin | Buyer evaluation logic | Strengthen positioning; optimize GTM |
| Competitor Twin | Competitive framing | Pressure-test differentiation; anticipate market moves |
This ladder represents cognitive leverage. Each rung moves leaders from intuition toward evidence-based reasoning. This is the backbone of Foundations of Marketing Leadership: alignment not as a philosophy, but as an operating system.
Why Digital Twins Matter for AI in GTM School
The logic is simple: If AI can evaluate your brand before your buyer sees you, then AI should also evaluate your strategy before your CEO, board, or market sees it. Simulation becomes the ultimate competitive edge.
Pavilion teaches leaders to build these systems for themselves — not to outsource thinking to AI, but to elevate it
And when paired with the orchestration principles in Section Three, simulation becomes the doorway to measurable performance gains. That’s where the next section will take us: the actual, quantifiable outcomes AI-augmented GTM teams are achieving.
As GTM leaders absorb the strategic implications of AI-enabled orchestration, the next question becomes unavoidable: does any of this materially improve revenue performance? Executives don’t invest in concepts; they invest in outcomes. And in Pavilion’s AI programs, those outcomes are no longer theoretical. They are documented, repeatable, and increasingly difficult to ignore.
Ryan Staley, who teaches the “AI-Augmented GTM Team” modules and draws on data from 300+ OpenAI enterprise implementations and 4,000 adoption surveys, puts it simply in his sessions:
“The biggest mistake leaders make is treating AI like a tool. AI is a teammate. And when you give a teammate a real job, you get real results.”
Across Pavilion member companies, those results show up most visibly in outbound efficiency, content performance, and leadership productivity — categories that traditionally require trade-offs between speed and quality. AI collapses that trade-off. The organizations that adopt AI with structure, intent, and workflow discipline see both accelerate simultaneously.
Pipeline & Outbound: From Labor-Intensive to Leverage-Driven
In Ryan’s modules, leaders see firsthand how AI reshapes the economics of outbound. Research, enrichment, and personalization — tasks that once consumed hours of human labor — become orchestrated workflows supported by Copilot and Delegate models. The purpose is not to replace SDRs; it’s to amplify them.
Ryan explains it this way:
“If your reps are spending most of their time researching and prepping instead of selling, AI hasn’t automated anything. It’s just moved the bottleneck somewhere else.”
Once AI handles the heavy lifting — account research, surface-level personalization, segmentation signals — reps spend more time in conversation and less time in preparation. And because the insights are deeper and more consistent, conversations improve instead of degrade.
The effect is unambiguous: outbound volume rises, response rates improve, and cost per meeting drops. The efficiency isn’t cosmetic; it’s structural.
Marketing Performance: Scaling Content Without Sacrificing Brand
In content and SEO workflows, performance gains appear even faster. Nathan Thompson, who spent years building AI-enabled content systems at Copy.ai, challenges one of the most persistent misconceptions in marketing:
“If you feel like AI outputs are ‘generic,’ it’s not the AI. It’s the context you’re giving it. Slow down to speed up.”
This principle sits at the center of the “Brand Brain” system Nathan teaches in Pavilion’s content automation classes: AI will only “sound like you” if it actually knows who you are. When teams codify their mission, voice, audience segments, and product positioning into structured context, AI doesn’t flatten their identity — it reinforces it.
And when coupled with orchestrated SEO and content workflows, the results follow a familiar pattern: more content, higher quality, lower cost, stronger organic performance.
Nathan summarizes this dynamic bluntly in one session:
“Most teams don’t have a content problem. They have a context problem.”
AI solves the latter, and in doing so, unlocks the former.
Leadership Efficiency: Clarity at the Speed of Thought
Some of the most transformative impact arrives not in frontline GTM roles, but in the workflows of leaders themselves. Meeting prep, weekly readouts, risk assessment, pipeline summaries, customer insights — executive bandwidth is often consumed by the administrative overhead of understanding what is happening across teams.
Ryan emphasizes this repeatedly:
“Leaders spend too much time trying to get to the starting line. AI gets you to the starting line instantly so you can actually lead.”
This isn’t a productivity hack; it’s a strategic unlock. When leaders start their week with cleaner summaries, better intelligence, and clearer priorities, they make better decisions faster — and the organization feels it. Cross-functional conversations become more focused. Action items become more precise. Alignment strengthens because everyone is responding to the same set of interpreted signals.
Josh Carter, teaching alongside Ryan in AI for Modern Marketers, reinforces the operational side of this shift:
“AI isn’t magic. It’s plumbing. And when your plumbing is clean, everything flows.”
The “flow” he refers to is organizational clarity: AI removes the friction of manually gathering, synthesizing, and interpreting information across systems — giving teams more capacity and leaders more certainty.
How Pavilion Instructors Describe AI’s GTM Impact
| Instructor | What They Teach | Quote |
|---|---|---|
| Ryan Staley | AI-augmented GTM teams, Copilot/Delegate/Strategist/Revenue Teammate roles | “AI is a teammate. Give it a real job.” |
| Josh Carter | Data foundations, workflow design, practical automation | “AI is plumbing — and when the plumbing is clean, everything flows.” |
| Nathan Thompson | Brand Brain, content automation, scaling without losing voice | “You don’t have a content problem. You have a context problem.” |
This table reflects a shared philosophy: AI improves performance not because it writes faster, but because it removes friction, organizes intelligence, and amplifies expertise.
The 60-Day Proof Window
One of the most compelling patterns Pavilion instructors observe is what happens during a 60-day AI pilot. When teams deploy structured prompts, improved workflows, enriched data, and defined AI role responsibilities, the gains show up fast: improved output quality, increased quantity, reduced time spent, and enthusiastic adoption.
This phenomenon matters because it rebuts the “AI transformation takes years” narrative. It doesn’t. Not when the foundations are set correctly.
And that is where AI School steps in: helping leaders design the governance, processes, and adoption frameworks that make these results repeatable, defensible, and scalable — not just in isolated experiments, but across the entire revenue organization.
Design an AI System you can scale.
By now, one thing should be unmistakably clear: AI transformation isn’t blocked by technology. It’s blocked by alignment. Every Pavilion cohort reveals the same pattern—teams don’t fail at AI because the models aren’t capable, or because the tools aren’t good enough. They fail because their organizations lack structure, ownership, sequencing, and accountability.
This is why Pavilion’s instructors push governance to the forefront. It is not the “boring” part of AI transformation. It is the transformation.
Andy Jolls, who teaches growth strategy inside Pavilion’s Practical AI Workshop and Co-Dean of AI in GTM School, frames the stakes with executive bluntness:
“AI has to produce numbers you can defend. If you wouldn’t put it in front of your CEO or board, it’s not a strategy.”
Executives often come into AI conversations searching for the right tools. They leave understanding that tools are the easy part. The hard part is building the operating system around those tools so AI creates momentum instead of chaos.
Why Governance, Not Tools, Determines Success
When Pavilion members talk about why AI initiatives stall, the causes are rarely technical. They’re structural. Leaders either diffuse responsibility across too many teams, allow every function to create its own AI conventions, or try to roll out sweeping automation before proving anything works.
Jonathan Moss, EVP of Growth & Ops at Experity and Co-Dean of AI in GTM School, distills the core error this way:
“Transformation fails when you start with scale instead of starting with proof.”
This is reinforced by the data Pavilion uses in its AI programs. Large, organization-wide AI rollouts succeed only 24% of the time. Phased, disciplined rollouts succeed 82% of the time. The lesson is simple: AI should start narrow and expand with evidence, not enthusiasm.
Governance is what turns early wins into durable systems. It creates a shared language around AI usage, sets expectations for quality and guardrails, and prevents the brand drift and internal confusion that emerge when teams automate independently. Without governance, AI becomes the new Martech sprawl—expensive, inconsistent, and underutilized.
The Human Architecture Behind an AI-Enabled GTM Team
While Liza Adams teaches the organizational stages of AI maturity, and Josh Carter shows how workflows become automatable, Ryan Staley adds the final layer: the human roles that make AI sustainable. His insights come from hundreds of enterprise AI implementations, and the pattern is unmistakable: companies with defined AI leadership roles outperform those without them.
Instead of treating AI as “everyone’s job,” high-performing organizations assign it structure. An AI Owner sets vision and ensures that efforts tie back to GTM objectives. AI Champions inside each function drive experimentation, track outcomes, and reinforce standards. AI Enablers operationalize the workflows and maintain quality, so AI becomes a dependable component of the revenue engine rather than a novelty.
Ryan often warns executives about what happens when these roles don’t exist: “When nobody owns AI, AI goes nowhere.”
These roles aren’t administrative—they are the scaffolding that allows workflows, automation, and AI teammates to integrate into daily GTM motion. Without them, adoption flatlines and experimentation devolves into noise.
A Strategy One Can Defend: The 90-Day AI Plan
One of the most valuable outputs of Pavilion’s AI School is a board-ready 90-day AI strategy. It is the antidote to vague AI roadmaps filled with ambition but lacking structure. And it is designed to withstand the scrutiny of CEOs, CFOs, and boards who increasingly want proof, not aspiration.
The plan borrows from the rigor Andy and Jonathan use in their Practical AI Workshop: tying every initiative to a metric, mapping workflows that reduce drag across GTM functions, aligning projects with existing KPI frameworks, and sequencing the work so adoption builds gradually rather than explosively. Leaders learn how to size their initiatives, identify dependencies, forecast impact, and articulate ROI in a way that an executive team can trust.
Andy summarizes the philosophy neatly during a workshop session:
“A strategy is only real when it influences resource allocation.”
AI in GTM School teaches leaders to build plans that clear that bar.
The 90-day timeframe is deliberate. A year is too long for a rapidly evolving technology. Thirty days is too short to prove value. Ninety days strikes the perfect balance between velocity and credibility. It is enough time to deploy workflows, collect real data, refine processes, and show measurable impact—yet short enough that the organization doesn’t lose momentum or clarity.
A strong 90-day AI plan accomplishes three things simultaneously: it creates early wins, builds internal confidence, and establishes the governance structure required to scale safely.
Where This Leaves the Executive
By the end of AI in GTM School, executives don’t just understand AI—they understand how to lead with it. They’ve learned how to stabilize adoption, how to structure their organizations for cross-functional leverage, how to assign ownership, how to build workflows that scale, and how to test strategic ideas before putting them in front of their teams or customers.
Most importantly, they leave with something GTM leaders rarely have in AI conversations: a strategy they can defend and a roadmap they can execute.
GTM is entering a new era — one where buyers form opinions before you ever speak to them, where workflows move faster than human coordination can follow, where content multiplies without headcount, and where decisions become sharper because insight is available instantly. The story threading through every section of this article is simple: AI is no longer an advantage at the margins. It is now embedded in the infrastructure of how companies grow.
Liza Adams showed us that AI is already shaping buyer perception upstream, reshaping how brands must position themselves in a world where algorithms decide relevance before humans do. Josh Carter demonstrated that AI only becomes effective when the workflows underneath it are clean, orchestrated, and data-rich. Ryan Staley revealed how AI teammates replace friction, not people, and why GTM organizations accelerate only when they assign AI real responsibilities. Andy Jolls and Jonathan Moss reminded us that strategy doesn’t matter unless it can withstand board-level scrutiny — and that governance is the difference between AI chaos and AI capability.
What these instructors collectively prove is that AI transformation is not a technology project. It’s a leadership project.
The organizations that thrive over the next decade will be those who learn how to harmonize human expertise with AI-powered leverage — where teams don’t compete with AI, but collaborate with it. Where work becomes faster and more precise. Where GTM motion becomes an orchestrated system, not a set of disconnected efforts. And where executives lead with clarity because they finally have the information architecture they need to make confident decisions.
This is the world AI School prepares you for.
AI School is not another AI “overview.” It is a blueprint for building an AI-first revenue engine — one that changes how your buyers discover you, how your teams work, how your workflows scale, and how your strategies are built. It gives you the intellectual foundation, the operational systems, the governance model, and the 90-day plan your CEO expects you to produce.
Most importantly, it gives you something the market has made increasingly rare: control. The next decade won’t belong to the leaders who adopt the most tools. It will belong to the leaders who understand how to orchestrate them.
That’s the promise of AI School. And it’s why the leaders enrolling now won’t just adapt to the future — they’ll define it.
