Liza Adams
50 CMOs to Watch in 2024 | AI & Exec Advisor | Go-to-Market Strategist | Public Speaker | Fractional/Advisor of the Year Finalist
Hello go-to-market (GTM) leaders, strategists, and innovators! 👋 Thank you for dropping by to learn practical AI applications and gain strategic insights to help you grow your business and elevate your team’s strategic value.
Quick Take
We test subject lines, ad copy, and CTAs. But we don’t test as much how the people who influence our success will actually react to strategic moves.
Product marketers launch positioning without fully anticipating competitive responses. Brand teams may present to analysts while crossing fingers about tough questions. Revenue teams find themselves launching campaigns quickly based on assumptions about buyer priorities.
This guesswork costs millions in failed campaigns, competitive surprises, and deals that stall because we misread the buyer.
GTM teams no longer have to choose between moving fast and moving with strategic intelligence. AI simulation lets you pre-test strategies against key stakeholders in hours, not weeks, before spending budget or burning relationships. Real-world validation still happens, but you’ve already caught some of the major risks.
If you’ve built a digital twin of yourself, you already understand the power of AI simulation. (If you haven’t, see the newsletter on digital twins to learn more and how to build them). This takes that concept further. Instead of simulating yourself, you’re simulating the people who matter most to your business. The shift from “what would I do?” to “how would they respond?” changes strategic planning.
Most teams use AI personas to guide messaging like “Would this copy resonate with an IT director?” But that’s different from simulating how a real person — your CFO, your buyer, your fiercest competitor — will respond to strategic moves like market expansion, new tiered platform pricing, and channel partner approaches.
We’re going way beyond testing copy. We’re testing reactions to reduce the risk. That’s the difference between personalization and prediction.
Every strategy has a breaking point in the reactions. If you’re not simulating them, you’re flying blind.
Key takeaways:
- AI simulators let you test strategies against the people who influence your success before you engage them
- Three validation approaches: reactive (learn from failures), proactive (test before launch), predictive (anticipate moves)
- The People Simulator Priority Matrix shows which stakeholders matter most for each GTM function
- Most teams get the biggest impact by starting with research-based simulation for their top 2-3 critical stakeholders
Not all stakeholders carry equal weight. The People Simulator Priority Matrix helps teams prioritize whose feedback matter most and which functions need to test more deeply. Start with the people who can derail your launch, influence your strategy, or slow your momentum.
We’ll unpack this framework below to guide how your team applies simulation based on role.
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The Cost of Strategic Guesswork
Every quarter, GTM teams make million-dollar bets on assumptions.
Sales teams build pitches based on what they think buyers care about. Customer success teams launch retention campaigns without knowing what actually drives churn. Partnership teams negotiate deals while guessing at the partner’s real motivations.
When these assumptions are wrong, the costs add up fast. Campaigns that miss the mark. Analyst briefings that expose weak positioning. Competitive responses that catch teams off guard.
Teams face two bad choices: slow down with expensive research and surveys, or speed up and hope for the best. AI simulation offers a third path.
From Reactive to Predictive: Three Validation Approaches
Most teams validate reactively using traditional methods. They learn from expensive failures and adjust for next time.
How AI Simulation Works
Test your strategies against AI versions of the people who influence your success. Think of it as having permanent advisory access to your key stakeholders.
Before you start building simulations, check your company’s AI policy. Don’t input confidential, proprietary, or personally identifiable information. These tools are powerful, and using them responsibly builds trust and keeps your team protected.
Most teams follow a maturity progression through three implementation levels. Teams often start by testing messaging. But these tiers go beyond that. They help you test how real people will respond to the decisions that shape your strategy.
1. Basic Simulation
Simple role-play using AI’s general knowledge. Let’s say you’re testing new pricing strategy for your SaaS platform.
Example prompt: “You are a mid-market CFO evaluating our new pricing model. What concerns would you have about our 30% price increase? What would make you stick with us versus switching to a competitor? Please explain your rationale.”
Teams use this when they’re just getting started with AI validation, testing quick hypotheses, or need immediate directional feedback with no time for research or setup.
The benefits are zero setup time, immediate insights, works in any chat interface. But you get generic responses based on AI training data, not specific behavioral patterns.
2. Research-Based Simulation
These are AI advisors built with specific stakeholder data like their communication style, decision patterns, past positions, and known priorities. You can implement this through chat with uploaded research or custom GPTs (AI you can train to do a specific task) with knowledge bases.
Same pricing scenario, but now with research behind it.
Example prompt: “You’re a mid-market CFO based on this research data [upload persona profile, interview transcripts, survey data, past objection patterns]. I’m testing a 30% price increase. Instead of just reacting, help me think critically about this: What assumptions might I be making about CFO priorities that could be wrong? What questions should I ask myself to pressure-test this pricing strategy? What alternative scenarios should I consider – both best and worst case? Walk me through your reasoning for each concern you raise.”
Teams are ready for this when they’ve identified 2-3 critical stakeholders. They’re willing to invest research time upfront for ongoing value. They need consistent perspective across multiple team members. And they’re making repeated decisions involving the same stakeholders.
This works best for key accounts, major competitive threats, important industry analysts, and primary buyer personas.
3. Dynamic Simulation
With dynamic simulations, there’s real-time integration with CRM data, social listening, competitive intelligence, and other data sources. Updates happen automatically as stakeholder behavior changes.
Here’s the same pricing scenario, but with live market intelligence.
Example capability: Your CFO simulator automatically updates based on recent earnings calls, competitive pricing moves, and current economic conditions. When you ask about pricing strategy, it responds with current context: “Based on Q3 earnings calls, mid-market CFOs are focused on cash preservation due to rising interest rates. Your timing might be off. Three competitors dropped prices 15% in the last quarter. Here’s what CFOs are actually saying about budget priorities right now…”
Dynamic simulation makes sense for teams in fast-moving markets with access to real-time data. It requires technical setup, integration, and oversight, so it’s usually supported by specialists. Most GTM teams start with basic or research-based simulation for quick wins, then advance to dynamic once they have the right foundation in place.
Justin Parnell, my business partner who specializes in AI automation provides his perspective on the implementation reality.
“Dynamic simulation doesn’t mean every GTM professional is wiring up AI systems.
A select few specialists build and maintain the automated workflows, manage integrations, and handle governance in partnership with legal and IT. They create the infrastructure so the rest of the organization can use it safely and effectively.”
No simulation is perfect. The value is in creating a strong draft of stakeholder reactions you can validate and refine. It’s far easier to pressure-test and adjust a simulation than to start from scratch every time.
The People Simulator Priority Matrix
Using marketing as an example, I built an interactive framework to help you identify which stakeholders matter most for your function.
Great strategies often fail because one overlooked stakeholder derailed them. This matrix helps you identify who can make or break your move.
Key stakeholder types:
- Executives/C-Suite – Internal decision makers and budget holders
- Customers – Existing relationships and revenue base
- Buyer Personas – Target prospects you’re trying to reach
- Partners – Channel and alliance relationships
- Competitors – Market dynamics and positioning battles
- Media & Analysts – External validation and market perception
The matrix shows exactly which combinations create the biggest impact for your specific role. Product Marketing teams get most value from buyer persona, competitor, and analyst advisors. Brand teams need media, analyst, executive, and customer advisors.
Below is an example (Buyer Persona for Product Marketing) of the guidance for building a simulator once you click on one of the icons in the matrix.
Claire Darling, CMO at Clari, has put this approach into practice at scale:
“We’ve doubled our marketing team by creating 40 AI teammates in Q2. Our Persona Messaging Auditor has been transformational. Before launching any campaign, we audit messaging against our CRO, RevOps, and finance buyer personas. The auditor surfaces specific concerns each persona would have, like when our RevOps messaging focused on features instead of the workflow integration challenges they actually face.
This process gave us deeper insights about buyer decision patterns that become competitive intelligence. We’ve moved from assuming our messaging works to validating it works before we spend money.
That’s just the beginning. Messaging is where we started, but we can now explore how to simulate stakeholder reactions to strategic decisions — not just what we say, but what we do.”
The Impact
Some of the benefits of AI advisors are as follows:
- Risk Reduction – Catch problems before launch instead of learning from expensive mistakes. Test positioning with your analyst advisor before briefings. Check retention messaging with your customer advisor before campaigns.
- Strategic Preparation – Get perspective when you need it most. War-game competitive responses before product launches. Test partnership proposals before formal presentations.
- Competitive Advantage – Move faster with better intelligence than teams still using guesswork. While competitors learn from post-mortems, you prevent problems by testing first.
Your Next Steps
Start with one key stakeholder whose perspective would most improve your strategies and decision-making:
- Pick your first advisor based on your biggest strategic blind spot
- Research their behavior through public statements, past interactions, and communication patterns
- Build your advisor using the research as foundation (custom GPT works well)
- Test on a real decision and compare guidance to actual outcomes
- Expand your advisory team based on what you learn
You’ll still validate in the real world, but simulation gives you a massive head start. Instead of choosing between moving fast or getting insight, you get both.
Great strategies fail in the reactions. AI-forward teams won’t guess anymore, they’ll simulate first.
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