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Great Strategies Die in the Reactions You Didn’t Simulate. AI Lets You Test Them First.

Liza Adams · September 6, 2025 ·

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.

The Practical AI in Go-to-Market newsletter is designed to share practical learnings and insights in using AI responsibly. Subscribe today and let’s learn together on this AI journey!

For those who prefer more interactive learning, explore our applied AI workshops, designed to inspire teams with real-life use cases tailored to specific go-to-market functions.

We also guide teams through their AI transformation journey. Check out this team transformation case study and step-by playbook of how we helped transform a lean GTM team into a human-AI powerhouse with human and AI teammates.

Or, if audio-visual content is your style, here are virtual and in-person speaking events where I’ve covered a variety of AI topics. I’ve also keynoted at many organization and corporate-wide events. Whether through the newsletter, multimedia content, or in-person events, I hope to connect with you soon.

AI Is Only as Sharp as Your Questions: How Critical Thinking Turns Any Work into Better Decisions

Liza Adams · August 21, 2025 ·

Hello go-to-market 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

Most people use AI to get tasks done faster and take the first response. They’re training themselves to accept answers without question. Every day, teams walk away from high value insights because they never learned to think systematically about what AI tells them.

This approach changes any AI interaction into deeper insight. Here’s what happens when you shift your approach:

Ask “why” behind every recommendation – You see how AI thinks, can check its work, and train yourself to think more systematically about your own decisions
Challenge assumptions in any context – Whether you’re questioning “educational content performs best” for social media or “Europe is our best market” for expansion, the same principles apply
Get multiple views and confidence levels – Turn any AI response into a thinking exercise that shows blind spots and options
Critical thinking works regardless of scope – The same line of question that improves email subject lines also makes sense for strategic planning
Work in a judgment-free space – AI doesn’t care about your ego or timeline, making it easier to question assumptions you’d defend in front of colleagues

The difference isn’t the technology or the complexity of your work. It’s how you think about thinking. When you approach AI as a thinking partner rather than a task doer, every interaction becomes an opportunity to make your decision-making better.

Prefer to listen to an AI-generated podcast?

To support different learning styles, this newsletter is also available as an AI podcast (13 mins) with two AI hosts. I used Google’s NotebookLM to create it and personally it for accuracy and responsible AI use. (Quick tip: After you click through, the player might take a moment to load after you press play.)

From Execution to Insights

AI can help with any type of work across your entire GTM org and beyond. Whether you’re writing email subject lines or planning market expansion, creating battle cards or setting sales territories, the same tech supports both daily tasks and big decisions.

But here’s what separates good AI use from transformational AI use: applying this approach to any of it, tactical or strategic work as shown in the table below.

Most teams automate what they already know how to do. Teams that understand AI’s real potential use it to uncover what they don’t yet know.

The Power of Asking “Why”

The key is asking AI to explain its reasoning.

Most people take AI’s first answer and run with it. But when you ask “Why do you recommend this?” or “What’s your reasoning behind this ranking?”, we learn something new.

You see how AI thinks. When you ask for reasons behind every response, you’re not just getting answers. You’re learning to think more clearly yourself.

You can check its work. This is how you catch AI confidently recommending terrible strategies that look brilliant at first glance. You catch gaps in logic. And you train yourself to think more clearly about your own decisions.

We’ve been rewarded our entire lives for having the right answers with good grades, promotions, recognition. But watch any great meeting: the most valuable person isn’t the one with all the answers. It’s the one asking the insightful questions that shift how everyone thinks about the problem. AI doesn’t change this dynamic, it amplifies it.

Getting better AI outputs is just the beginning. The real value is building better thinking habits that stick with you in every conversation and decision. Whether you’re planning enterprise strategy or choosing social media topics, asking “why” transforms any work into deeper analysis.

Mandy Dhaliwal, CMO at Nutanix, has experienced this firsthand.

“It’s important not to use AI like a Q&A machine. We guide its thinking, we brainstorm together, but we still make the final call.

How many breakthrough ideas get killed because someone had to wait for the next team meeting to bounce them around? We can test messaging ideas off hours or work through event plans during our morning walk.

That immediate access to a thinking partner completely changes how we make decisions.”

The Psychology Behind Better Questions

When you shift from asking AI to execute tasks to asking it to challenge your thinking, the psychological barriers that normally keep us from questioning our own work disappear.

This works because:

AI doesn’t judge – No ego, politics, or timelines. This makes honest evaluation possible.
Private testing leads to public confidence – Challenge ideas with AI first, then show up to meetings prepared.
Silos vanish – A Harvard study with P&G professionals found teams working with AI “stop caring as much about the normal boundaries of your job.” AI focuses on problems, not politics.
Real example: strategic confidence builds fast – During a recent workshop, an ABM marketer used AI to challenge her key account marketing plan. Despite her excitement about the possibilities, she admitted, “It hurt because I’d worked so hard on this. This is my baby and AI was calling it ugly. But the questions were so good.” She realized she could strengthen her strategy by validating key assumptions.

This shift happens faster than you might expect. This week in Atlanta, I conducted function-specific AI workshops with 60+ marketers at Cox Automotive Inc., the company behind Kelley Blue Book and Autotrader that helps dealers and partners buy and sell millions of vehicles each year. Ramon L. Cortes, AVP of Marketing Operations, saw these mindset changes happening in real-time.

Ramon Cortes, AVP of Marketing Operations at Cox Automative Inc.

“I watched lightbulbs come on and mindset shifts happen right before my eyes. Once people started asking AI deeper questions and working as a group, the discussions became richer, more analytical, and focused on business outcomes.

These kinds of conversations typically don’t happen until later in our process – sometimes not until we’re already presenting to executives. Now it will happen sooner, which means we’ll identify gaps faster, use resources more efficiently, and increase our strategic value.”

The Critical Thinking Framework

This approach turns every AI conversation into critical thinking practice. I’ve been using this with teams for a couple years and my own thinking has gotten sharper because of it.

Here’s a three-level approach behind this thinking.

Level 1: Basic Evaluation

Offer some alternatives to this approach.
Give me the pros and cons of each option.
Rank these ideas based on [specific criteria].
Rate these from highest to lowest confidence.
Sort these options into must-have, preferred, and nice-to-have.
Break this down into smaller steps and show the timeline.

Level 2: Different Views

How might this be seen by [specific stakeholders]?
Put these options in a 2×2 chart using [X and Y criteria].
What would [competitor/customer/exec team] think about this approach?
Show me the decision tree for checking these choices.
What are the key factors and how does weighing them change the outcome?
Compare how this problem is solved in [different industry/company size/region].

Level 3: Assumption Challenging

What assumptions am I making that might not be true?
What would make this idea 10x stronger?
Where might this approach fail or backfire?
Run 3 what-if scenarios and show how each changes the outcome.
Challenge my assumption that [specific belief]. What situations would make our current approach actually work best?
Point to relevant data sources and provide reasons for your recommendations?

Yes, this takes more time upfront, but it saves you from spending weeks executing the wrong plan.

The important step is to always ask “What’s your reasoning?” or “Why do you recommend this?”

These techniques work whether you’re allocating marketing budgets or brainstorming content ideas. The scope changes, but the thinking discipline stays the same.

Critical Thinking in Action

Here are examples of what you can expect when you ask basic questions vs insightful ones. These assume some basic context was provided, but the real difference is how better questioning turns any conversation into deeper insights. With basic questions, we get basic answers. Insightful ones result in thoughtful responses that help us make better decisions.

The final judgment and answer are always on us, not AI.

Case 1: Social Media Topics (Level 1 Critical Thinking)

Instead of “Please give me social media topic ideas” that outputs this fairly generic response below:

Try: “Please suggest 3 social media topics for our demand gen audience. I’ve been doing mostly educational content but engagement feels flat. What different approaches should I try? Please rank them by likely impact vs effort and tell me why.”

Sample Response:

Case 2: Pricing Model Analysis (Level 2 Critical Thinking)

Instead of asking “Please compare these pricing options” which gives you this basic answer:

Try: “We’re considering three pricing models for our project management platform. Please put them in a 2×2 matrix using customer acquisition vs revenue predictability. Please show how sales, finance, and product would view each differently.”

Sample Response:

Case 3: Account-based Marketing Strategy (Level 3 Critical Thinking)

Instead of “Please create an ABM strategy for our cybersecurity platform targeting mid-market companies” that gives you this answer:

Try: “We’re considering ABM for our cybersecurity platform. What if our assumption that “bigger accounts mean better ROI” is wrong? Challenge this approach. What alternative targeting strategies might work better? What could make traditional ABM backfire for us?”

Sample Response:

In the AI era, the most dangerous decisions aren’t the ones you get wrong. They’re the ones you make quickly, confidently, and unquestioned because the answer sounded right.

Note: In any of the examples above and in your work, context and what we share with AI so that it can better help us are key. Reminder to share your goal, role you want it to play (e.g., competitive analyst, skeptical buyer, etc.), actions it should do or not do, context (e.g., current situation, relevant files, accurate data, etc.), and examples (i.e., what good looks like).

Where Do You Stand?

Want to see how you currently approach AI? Take this quick assessment to discover whether you’re using AI as Task Executor, Analytical Collaborator, Developing Critical Thinker, and Critical Thinking Partner. You’ll get personalized next steps based on your results.

The quiz takes 3 minutes and shows you specific ways to level up your AI thinking, regardless of whether you’re working on major strategic initiatives or daily tactical tasks.

The Breakthrough

Critics worry AI will make us intellectually lazy. The opposite is happening with teams that take this approach.

When you systematically challenge AI outputs and ask for reasons behind every recommendation, you develop stronger evaluation skills than most traditional education provides. You’re getting hands-on practice in logic, understanding perspectives, and systematic analysis.

These same analytical habits show up in your team meetings, strategic reviews, and decision-making conversations.

I’ve noticed this with my own thinking. The questions I ask now, of AI and in regular work conversations, are richer. I automatically look for alternatives, ask for confidence levels, and pressure-test assumptions in ways I didn’t before.

Your Critical AI Thinking Starter Kit

Want to start using AI as thinking partner? Try this prompt with your next project:

“I’m working on [PROJECT DESCRIPTION] with the goal of [SPECIFIC OUTCOME]. Here’s some context: [SITUATION/CONSTRAINTS]. You’re a [strategist/devil’s advocate/customer advocate].

Instead of solving this for me, suggest 5-7 strategic questions I should ask you that will help me think more critically, evaluate options thoroughly, consider different perspectives, and understand implications I might be missing. Focus on questions that challenge assumptions and identify blind spots.”

Then use those questions. Ask for rationale. Push back on the reasoning. Build on the ideas. That’s where the real value lives.

Sydney Sloan, CMO of G2, applies this thinking to customer feedback analysis.

“When I look at customer feedback, I don’t just ask AI to tell me what people are saying. I ask it to spot what we might be getting wrong. Like ‘Based on these reviews, what customer problems are we not solving that we think we are?’ or ‘What are customers actually using our product for that’s different from what we built it for?’ Those questions reveal gaps between what we assume and what’s really happening.”

The Bigger Picture

Whether you think you’ve mastered AI or you’re still struggling with it, you’re probably operating at 20% of what’s possible.

The biggest AI advantage comes from questioning what everyone else takes for granted, not from better tools or prompts.

When teammates say this is overthinking, show them the difference between your basic and thoughtful responses, the value becomes obvious quickly.

This also becomes essential as we build AI teammates. Teams that can’t think critically with these tools now won’t be able to build teammates that think critically later.

Pick one decision your team made in the last month. Ask AI to play devil’s advocate and identify risks you missed. Share those insights with your team. That’s how you demonstrate AI’s potential for critical thinking and get the most out of it.

AI makes it easier than ever to act fast. But it also makes it easier to be confidently wrong. Clear thinking still sets you apart.

Remember: you’re building critical thinking habits that improve every meeting and decision.

The Practical AI in Go-to-Market newsletter is designed to share practical learnings and insights in using AI responsibly. Subscribe today and let’s learn together on this AI journey!

For those who prefer more interactive learning, explore our applied AI workshops, designed to inspire teams with real-life use cases tailored to specific go-to-market functions.

The GTM Leadership Traits That Worked for Decades Are Now Holding Teams Back

Liza Adams · August 6, 2025 ·

Practical AI in Go-to-Market
Get practical insights in using AI for go-to-market strategy, initiatives, workflows, and roles.

Quick Take

GTM functions are converging. The silos between sales, marketing, and customer success are breaking down, especially in the AI era.

The leaders who rise will think about entire customer experiences and naturally bring people together across boundaries
Five GTM leadership traits that now matter more than scaling experience

Your buyers don’t care about your org chart. Neither does AI. But most GTM teams are still led like it’s 2015.

Prefer to listen to an AI-generated podcast or view an AI video explainer?

To support different learning styles, this newsletter is also available as an AI podcast (13 mins) with two AI hosts. I used Google’s NotebookLM to create it and personally it for accuracy and responsible AI use. (Quick tip: After you click through, the player might take a moment to load after you press play.)

The Convergence Is Already Happening

Future GTM leaders will think customer-first, collaborate across department lines, and run their teams like integrated businesses instead of separate functions. The Microsoft 2025 Work Trend Index Report noted that “teams form around goals, not functions, with AI helping employees do more and work faster.”

Ethan Mollick, Associate Professor and Co-Director of the Generative AI Lab at The Wharton School, shared insights from a Harvard study with P&G professionals. Cross-functional teams working with AI experienced an interesting finding:

“You stop caring as much about the normal boundaries of your job.”

There are distinct differences between what works before and what works now.

These changing expectations require fundamental shifts in how GTM leaders think and act.

Everything we’ve been measuring and rewarding in GTM leadership – the scaling credentials, the functional expertise, the proven playbooks – matter less than traits most companies aren’t even screening for yet.

The Five Traits That Define Future GTM Leaders

These five traits better position GTM leaders for success in the AI era.

1. Adapt to New Buyer Behaviors and Expectations

AI is changing how people find and choose vendors. Beyond searching, they’re asking AI tools like ChatGPT or Perplexity who’s best suited to solve their problem and who they should trust.

That change has real consequences. According to a recent Semrush Study, AI search visitors convert 4.4x better than regular organic traffic because they arrive educated and ready to buy. But AI forms opinions about your brand before humans do, crawling every touchpoint from your website to employee review sites.

Great GTM leaders think about both the human buyers and the AI influencing them, too. That means making sure your brand is easy to understand, credible, and recommended by both people and machines.

Today’s buyers expect more from every interaction. Leaders need to balance:

Automation with the human touch
Personalization with transparency
Convenience with privacy
Efficiency with empathy
Innovation with ethics

Making sure this balance happens is not trivial but the leaders who get this right will rise.

Where to Start: Want to see how AI recommends you vs. competitors? Use the Deep Research functionality inside tools like ChatGPT, Claude, Gemini, or Perplexity. This prompt works well to simulate how AI evaluates vendors and makes recommendations:

“I’m the [TITLE] at a [COMPANY SIZE/TYPE]. We’re dealing with [TOP 2-3 PAIN POINTS]. Please evaluate the top 3 solutions that can help. Evaluate them on criteria most important to [TARGET MARKET]. For each competitor, please provide 1-5 ratings for each criterion plus overall in a table and include your rationale. Give pros/cons, which fits best for our situation and why. Also output 3 questions I should ask each competitor in areas that are limitations or challenges for the vendor.”

Use this to see how your brand stacks up, what’s showing up as your perceived strengths and gaps, and how the AI is telling your story.

For more insights on how to become more relevant in AI search and help AI understand for situations you company and products are most suitable, check out:

2. Shift from AI Tools to AI Thinking Partners

Many teams use AI for emails, social posts, and basic content creation. More AI-forward teams do that AND use AI to pressure-test decisions, do scenario planning, identify and analyze gaps, and spot new opportunities. The gap widens daily between teams using AI as a strategic thought partner and those primarily using AI to write blogs.

Where to Start: Pick one decision your team made last month. Ask AI to play devil’s advocate, identify risks you missed, or suggest alternative approaches. Share the results with your team to show AI’s strategic potential.

Below are some examples of strategic AI use cases to get the ideas flowing.

Dive deeper into the topic here, Beyond Q&A: Using AI as a Thinking Partner.

3. Fix the Foundation Before Your Try to Scale

AI amplifies whatever you have. Poor product-market fit plus AI-powered campaigns equals faster path to failure. Plus it’s a waste of time, budget, and resources. But with good product-market fit, AI can speed up growth. Just like we can’t out-exercise a bad diet, we can’t out-campaign a bad product-market fit.

Where to Start: Complete the interactive Trust & PMF Assessment based on recent customer feedback. Know which quadrant you’re in to inform strategy and before you scale with AI.

Here’s another quick check: If your product hasn’t changed much in the last few years, it might be worth checking whether you’re still solving the right problems for your customers.

4. Measure What the Business Cares About

Kate Bullis, Global Marketing and Sales Practice Leader at ZRG, sees this in her executive search work.

“The most successful GTM leaders aren’t just functional experts, they’re business leaders who think like CEOs. Among other things, this means they think outside-in, so they optimize their departments by thinking first about optimizing the entire customer experience. They understand that their org chart is only as good as the market’s experience and results. This has always been the case but AI has made this increasingly important.”

Where to Start: Pick your biggest challenge in delivering customer success. Write what success looks like from their perspective. Then ask each department (marketing, sales, success) what metric they’d use to measure it.

You’re looking for misalignment. When different teams use different metrics to measure the same customer outcome. These gaps show where you’re thinking like separate functions instead of one business.

What you’ll typically find:

Marketing – “20% more qualified leads”
Sales – “45-day sales cycles”
Success: – “Under 5% churn”

What you want:

Everyone – “First value in 30 days with 90% satisfaction”

Those misaligned metrics are your roadmap for bringing teams together around shared customer outcomes. When everyone measures the same result, they naturally start working as one business instead of competing functions.

To learn more about the hidden cost of misalignment, check out AI Will Force Marketing and Sales Alignment: The Revenue Gap You Can’t Hide Anymore.

5. Develop People Alongside the Tech

The hardest part of AI transformation isn’t AI, it’s the human side. Leading through convergence requires understanding people’s fears, motivations, and passions. You’re asking teams to change how they’ve worked for years while navigating messy politics and ingrained culture.

Christine Heckart, CEO of Xapa, focuses on conscious leadership in the AI era.

“The most expensive AI deployment is one where people don’t know how to use it effectively. You have to meet people where they are and bring them along. The focus can’t just be on training machines. It has to be on investing in humans so people can guide AI responsibly and ethically.”

Where to Start: Several shifts to consider…

From: Driving AI adoption To: Developing AI-ready careers
From: Giving people tools To: Giving people space to learn
From: Mandating use To: Inspiring possibilities
From: Early wins at any cost To: Building confidence that lasts

Choose one shift above. This week, have one conversation with a team member about how AI could enhance their career.

We’re already seeing these traits used in practice especially in how teams are moving from physical handoffs to human–AI partnerships. I’ve shared how GTM leaders are breaking silos, connecting AI teammates, and building organizations that operate across functions instead of around them.

If you missed those, check out The End of Handoffs: How AI Teammates Work Together and Leader’s Playbook: How a Lean Team Transformed Into a Human–AI Powerhouse for real examples of what these leadership shifts look like in action.

The Human Side of Convergence

A few months ago, one of my 2025 predictions is that the GTM engine will begin to converge and that there are key leadership traits for those who will lead this unified org successfully. Today, it’s becoming more of a reality.

As expected, the human side matters more. People first, AI forward. That’s the formula that works.

This shift requires two things: GTM leaders developing these new capabilities, and organizations recognizing and hiring for them. The transformation happens faster when both sides evolve together.

The leaders who thrive will be those who remember that behind every metric, workflow, and optimization lives a human being trying to do their best work.

That’s worth celebrating.

The Practical AI in Go-to-Market newsletter is designed to share practical learnings and insights in using AI responsibly. Subscribe today and let’s learn together on this AI journey!

For those who prefer more interactive learning, explore our applied AI workshops, designed to inspire teams with real-life use cases tailored to specific go-to-market functions.

The End of Handoffs: How AI Teammates Work Together

Liza Adams · August 2, 2025 ·

Practical AI in Go-to-Market
Get practical insights in using AI for go-to-market strategy, initiatives, workflows, and roles.

Quick Take

Work is reorganizing around what customers need, not what our org charts say. Most people use AI teammates one at a time. But you can connect them so they work together like a real team.

The gap between “who knows what” and “what needs to be done” disappears when AI doesn’t care about your org chart
Expertise flows across team boundaries, organizing work around outcomes instead of departments
More people can focus on strategic, big-picture work when AI handles the tactical execution and handoffs
Connected AI teammates show you what this new way of working looks like in practice

What you’ll discover in this edition: How to start your first chain, what this means for teamwork, and real examples of teams reimagining work around customer outcomes.

LIVE DEMO:

I’ll show a real GPT chain in action — no slides, just AI teammates working together in real-time. You’ll see how each one builds on the last, and how the GPT Navigator suggests which teammates to pull in for the job. This is where the lightbulb goes off.

Prefer to Listen? Try the AI-Generated Podcast

For those who prefer to consume information through audio, I’ve used Google’s NotebookLM to transform this newsletter into a short podcast episode, featuring a natural conversation between two AI hosts. You can listen to the 16-min podcast here while driving, walking the dog, or doing chores. Once you hit play, give it just a few seconds then it will start.

How Work Should Flow

Right now, work follows org charts. Marketing creates content, hands it to Sales. Sales qualifies leads, hands them to Customer Success. Product builds more features, hands specs to Marketing.

Each handoff loses context, slows momentum, and creates friction for customers.

But when you connect AI teammates, expertise starts flowing where it’s needed, when it’s needed. Department lines become less important than getting the job done.

You’re seeing early infrastructure for work organizing around customer outcomes instead of functional silos.

From Tools to Systems: Where Most Teams Get Stuck

AI teammates are specialized AI tools you build and train with your team’s knowledge. These could be Custom GPTs, Claude Projects, Gemini Gems, or other specialized AI tools designed to handle specific tasks with your best practices built in. Each one is built, trained, maintained and managed by humans with their unique expertise.

Most teams move through three phases with AI:

Phase 1: Using AI as Tools – You ask questions, get answers. Faster individual tasks.

Phase 2: Guiding AI as Teammates – You build specialized AI that knows your processes. Better results through ongoing collaboration.

Phase 3: Orchestrating AI Systems – You connect multiple AI teammates so they work together. Different types of expertise combine in new ways.

Most teams are stuck in Phase 1. Some are building Phase 2. Phase 3 is where competitive advantage lives because most teams will stay stuck coordinating individual AI tools while you’re orchestrating AI systems.

Angie Hill, Sr. Vice President of Growth and Integrated Marketing at Procore Technologies, is already thinking about this shift.

“The companies that will make the biggest leaps are those that can reimagine how work flows when expertise can move freely across different internal teams and functions. More of our people need to think and work strategically and collectively at the outcome level. Connecting AI teammates begins to show us what that future looks like.”

How Chaining Works

Chaining means connecting AI teammates so they work together in sequence. Each one sees the full conversation and builds on what came before. Instead of briefing each AI teammate separately, you create workflows where expertise flows from one specialist to the next.

The image below shows how you “call” each GPT with the @mention function in the message box. It’s similar to how we tag people on LinkedIn. You can call GPTs one-by-one, as you need them, into the same conversation.

It’s a simple yet powerful feature. Probably one of the most underrated and underused features of ChatGPT despite being available since early 2024! Why has it flown under the radar? You need multiple Custom GPTs to make chaining valuable, and most people haven’t built them.

Here’s the difference in practice.

Without chaining (using each AI teammate individually) – You work with Content GPT to create an article → You take that output and start fresh with Webinar GPT → You summarize the webinar strategy for Email GPT → You give Social GPT a brief overview.

Each AI teammate starts from scratch. You spend time re-explaining context (e.g., target persona, key pain points, value props, stage in the buying journey). You upload or cut and paste outputs from one GPT into another. Details get lost.

With chained AI teammates – Content Drafter GPT (built by Sasha) creates messaging → @Webinar Buddy GPT (built by Shiloh) sees the full conversation and builds event strategy using that exact messaging → @Email Writer GPT (built by Remi) sees everything and creates sequences that support the webinar → @Social Creator GPT (built by Yuki) adapts it all for social platforms.

Every AI teammate sees the complete conversation. Nothing gets lost. Each specialist’s knowledge builds on the others. You get campaigns that combine everyone’s expertise from day one. This only works when each AI teammate is well-trained. Weak links in the chain amplify problems instead of solving them.

This is like assembling your dream team. Each GPT carries the knowledge of it’s human builder’s expertise in that area: Sasha’s content experience, Shiloh’s event strategy, Remi’s email best practices, Yuki’s social insights. Each builder is like a personal trainer who knows their athlete intimately and has trained them to peak performance. The team then works together with shared context toward the same goal.

When you connect the GPTs, you’re the coach. You set the strategy, guide the plays, and ensure responsible execution while your dream team delivers top-notch results.

Below is a sample marketing org chart showing the AI teammates and how humans can orchestrate jobs to be done by chaining the AI teammates together.

Connecting and Orchestrating AI Teammates to Work Together

View the short demo video to see chained AI teammates in action for a campaign: pitch deck creator GPT + webinar planner GPT + email buddy GPT

Today, only ChatGPT supports teammate chaining in one conversation. But the concept of orchestrated AI teammates is platform-agnostic. The future is about designing systems where expertise flows.

The Research Behind the Shift

When you experience chained AI teammates, you start seeing how work wants to flow across knowledge areas, not department lines.

This aligns with what researchers are discovering about AI and collaboration. Ethan Mollick, Associate Professor at the Wharton School, shared insights from a Harvard study with P&G professionals. Cross-functional teams working with AI experienced something remarkable:

“You stop caring as much about the normal boundaries of your job.”

When specialists from different functions used AI, the lines between expertise areas nearly disappeared. Traditional silos broke down as AI helped people think beyond their specialized training. When specialists could access each other’s expertise through AI, project timelines shortened and quality improved because context never got lost in translation.

This is happening now. AI teammates accelerate this shift because expertise flows freely across them. When your positioning expert’s AI teammate works smoothly with your content expert’s AI teammate, you see how knowledge wants to move – not through department channels, but directly to where it’s needed.

People start organizing around outcomes rather than job descriptions.

As Maggie Miller, Senior Director of Corporate Marketing at HackerOne, puts it:

“What excites me most about chained GPTs is how they let us combine different perspectives smoothly. Instead of sequential handoffs where context gets lost, we get true collaboration where each expert builds on the others’ work. This is changing how we think about teamwork itself.”

If you viewed the demo above, Maggie’s quote will resonate. It changes how information flows, work gets done, and what becomes possible.

Start Your First Chain

Pick one workflow that typically involves multiple people and expertise areas:

Product launch campaign
Lead nurture sequence
Customer onboarding process
Event promotion workflow

Step 1: Map the expertise needed What different knowledge areas are required? Positioning, content, design, email, social, sales enablement?

Step 2: Check your current AI teammates Which ones do you already have? Which ones do you need to build? Each should capture one person’s expertise and best practices.

Step 3: Design the flow How should expertise move through the chain? What should each AI teammate see from the previous steps? Use @mentions to connect them in order.

Step 4: Test with human oversight Run your first chain with someone reviewing each handoff. You’re orchestrating expertise, not replacing judgment.

The goal is workflows where your team’s best practices combine every time, delivering results that no single person could create alone.

The GPT Navigator – Your Team’s AI Guide

As you build more AI teammates, keeping track of them becomes a challenge. Which GPT should you use for competitive analysis? How do you chain them for a product launch?

This is where the GPT Navigator comes in. Think of it as a Custom GPT that knows about all your team’s AI teammates and suggests which ones to use or how to chain them for any given project.

The Navigator asks a few questions about your project, then recommends the right AI teammate or suggests a chain sequence. It’s like having a smart assistant who knows everyone on your team and exactly what they’re good at. It helps teams reduce confusion and scale more easily.

Here’s a demo of how a GPT Navigator works:

Use this app to see examples of other workflows enabled by specific GPTs connected together.

Also check out a real-life GPT Navigator that the Dice marketing team uses. They call it AI Concierge.

Focusing on Jobs to Be Done

AI doesn’t care about org charts. It flows knowledge where it’s needed, when it’s needed. When your positioning expert’s AI teammate works smoothly with your content expert’s AI teammate, you see how expertise wants to flow across knowledge areas, not department lines.

What I’m observing with the GTM teams I work with aligns with broader trends. Microsoft’s 2025 Work Trend Index confirms that teams are forming around goals, not functions.

As I explored in my “AI is Breaking Department Silos: Moving from Org Charts to Work Charts” newsletter, chained GPTs give you early infrastructure for this shift toward outcome-driven work.

Once you experience this, you start asking different questions:

What if teams formed around specific workflows instead of departments?
What if expertise could flow to where it’s needed most, when it’s needed?
What if we organized around customer outcomes instead of functional silos?

Your marketing chains are just the beginning. Imagine when this connects to sales, customer success, product. When customer workflows run with fewer handoffs and stops. When the gap between “who knows what” and “what needs to be done” shrinks.

AI doesn’t care about our silos and neither do our customers.

Your Next Steps

Pick one workflow that typically involves multiple people. Map the expertise needed. Identify which AI teammates you have and which you need to build. Then design your first chain.

The infrastructure is here. So, will you keep briefing your AI teammates one by one or orchestrate a team that builds together?

New to AI teammates? Start with these two newsletters:

A Leader’s Human-AI Org Transformation Playbook – real case study of a team that grew from 20 to 100+ AI teammates today.
Build Your AI Inner Circle – how to think about and create your first AI teammates

Ready to connect your AI teammates so they work together? You’re in the right place.

The Practical AI in Go-to-Market newsletter is designed to share practical learnings and insights in using AI responsibly. Subscribe today and let’s learn together on this AI journey!

For those who prefer more interactive learning, explore our applied AI workshops, designed to inspire teams with real-life use cases tailored to specific go-to-market functions.

Also check out this team transformation case study and step-by playbook of how we helped transform a lean GTM team into a human-AI powerhouse with human and AI teammates.

The End of Handoffs: How AI Teammates Work Together

Liza Adams · July 23, 2025 ·

Practical AI in Go-to-Market
Get practical insights in using AI for go-to-market strategy, initiatives, workflows, and roles.

Quick Take

Work is reorganizing around what customers need, not what our org charts say. Most people use AI teammates one at a time. But you can connect them so they work together like a real team.

The gap between “who knows what” and “what needs to be done” disappears when AI doesn’t care about your org chart
Expertise flows across team boundaries, organizing work around outcomes instead of departments
More people can focus on strategic, big-picture work when AI handles the tactical execution and handoffs
Connected AI teammates show you what this new way of working looks like in practice

What you’ll discover in this edition: How to start your first chain, what this means for teamwork, and real examples of teams reimagining work around customer outcomes.

LIVE DEMO:

Prefer to Listen? Try the AI-Generated Podcast

How Work Should Flow

Right now, work follows org charts. Marketing creates content, hands it to Sales. Sales qualifies leads, hands them to Customer Success. Product builds more features, hands specs to Marketing.

Each handoff loses context, slows momentum, and creates friction for customers.

But when you connect AI teammates, expertise starts flowing where it’s needed, when it’s needed. Department lines become less important than getting the job done.

You’re seeing early infrastructure for work organizing around customer outcomes instead of functional silos.

From Tools to Systems: Where Most Teams Get Stuck

Most teams move through three phases with AI:

Phase 1: Using AI as Tools – You ask questions, get answers. Faster individual tasks.

Phase 2: Guiding AI as Teammates – You build specialized AI that knows your processes. Better results through ongoing collaboration.

Phase 3: Orchestrating AI Systems – You connect multiple AI teammates so they work together. Different types of expertise combine in new ways.

Angie Hill, Sr. Vice President of Growth and Integrated Marketing at Procore Technologies, is already thinking about this shift.

“The companies that will make the biggest leaps are those that can reimagine how work flows when expertise can move freely across different internal teams and functions. More of our people need to think and work strategically and collectively at the outcome level. Connecting AI teammates begins to show us what that future looks like.”

How Chaining Works

Here’s the difference in practice.

When you connect the GPTs, you’re the coach. You set the strategy, guide the plays, and ensure responsible execution while your dream team delivers top-notch results.

Below is a sample marketing org chart showing the AI teammates and how humans can orchestrate jobs to be done by chaining the AI teammates together.

View the short demo video to see chained AI teammates in action for a campaign: pitch deck creator GPT + webinar planner GPT + email buddy GPT