What we didn’t expect was that one use case, synthetic personas, would emerge as the most powerful accelerant for AI adoption across the team.

While they originated as buyer personas, the idea evolved. We first broadened them to include any role we wanted a conversation with and then gave them more depth, layered in tone, communication style, and behavioral insights to simulate specific individuals. That shift, from role to real, is changing how we communicate.

A Breakthrough Use Case: From Personas to Simulated People

One of our first success stories was the VP of Demand Gen agent. Originally created as a generic role augmentation assistant, we soon personalized using insights from real clients. When we added tone, decision-making quirks, and communication preferences, the chat began delivering feedback that felt eerily familiar (and extremely useful).

Consultants started using it to review campaign strategies, test sales pitches, and critique deliverables before ever sending a draft to a client. What started as a thought partner became a method for tailoring our work in a powerful new way.

How It Works: From Synthetic Persona to Simulated Person

Here’s how we transform a role-based persona into a highly specific simulated person:

Step 1: Behavioral Data Collection

We start by gathering transcripts, emails, and meeting notes that reveal how a stakeholder communicates. We use tools such as Google Notebook LM to extract patterns across large amounts of data.

Step 2: Layer Traits Onto Base Persona

We then use a foundational persona, the CMO, CRO, or VP of Demand Gen, and augment it with personalized attributes. These include DISC profiles, tone preferences, common objections, and leadership style.

Step 3: Deploy the Agent

The final step is loading the agent into ChatGPT Projects or Gemini Gems with customized instructions and background files. With the right prompts, the agent doesn’t just search knowledge, it gives pointed, contextual feedback as if from the real person's point of view.

The result is a simulation of a real stakeholder who helps you shape better strategies before the actual review ever happens.

Why This Took Off Internally

When our team realized these personas could mimic actual clients, adoption soared.

Now, they can test strategies with a simulated version of the stakeholder directly, whenever they wanted.

It wasn’t just faster. It was smarter.

My personal experience:

• I reviewed an email nurture framework for a client and made recommendations to expand reach and better highlight the client's value.
• The Gemini Gem that simulated the stakeholder gave me some critical insights:
• This stakeholder needed the highlights, level of effort, and expected outcomes up front before getting into the details - I was so focused on how to make improvements that I didn't include** why** the improvements were needed and how hard it would be to make them
• With these insights, I built a simple slide outlining the expected outcome for each change, and how much of a lift the change was.  The meeting went smoothly, and heads were nodding.

This work pre-check gives consultants and strategists the confidence to iterate faster and the ability to pitch stronger ideas. The associated confidence boost fueled even more usage, which helped us refine and scale the process.

Scaling: From Bottleneck to Teamwide Capability

Early on, only our AI Practice Lead could build these simulated personas. But with rising demand, we had to operationalize it.

So we're training the team.

We're teaching them how to:

• Collect meeting transcripts from Fathom and use them to infer communication style and decision-making criteria
• Create a DISC profile using AI tools
• Build a psychological profile using Google Notebook LM to assess all the available information
• Plug those traits into a base persona using ChatGPT or Gemini
• Test the results for realism and strategic relevance 

The result: We went from one creator to a distributed network of builders. Soon, nearly anyone at Inverta will be able to create a simulated stakeholder and use it to provide feedback on their work.

The Persona Network: AI Strategy in Surround Sound

Over time, we realized these personas didn’t have to work in isolation. We could build networks of simulated people, each representing a different stakeholder in the room.

Imagine you’re a CMO preparing a pitch. Before your real meeting, you test your materials with a simulated:

• CEO who demands bottom-line impact.
• VP of Demand Gen who nitpicks messaging and funnel fit.
• Head of Marketing Ops who raises red flags on budget and tech integration.
• CRO and Sales Leaders who need to know what areas of pipeline are impacted and what sales needs to do next
• Synthetic Buyer Persona who reacts to the actual messaging in the pitch.

It’s like running a boardroom or focus group simulation before the real thing.

We’ve used this setup to develop blog strategies, vet messaging, and rehearse go-to-market narratives. The results are sharper ideas, fewer surprises, and stronger alignment before anything gets presented.

Strategic Relevance + What We Learned

Simulated people aren’t just for ideation. They’re useful throughout the entire revenue cycle:

• Pre-launch validation – Let your simulated Head of Marketing Ops flag feasibility issues or budget misalignment.
• Messaging testing – Use synthetic buyers or 1:1 ABM targets to stress-test resonance and relevance.
• Pitch rehearsal – Sales teams can rehearse delivery with a simulated stakeholder and revise before the real call.
• Cross-functional alignment – Spot objections from internal stakeholders before they arise in meetings.

Here’s what we’ve learned:

• Adoption accelerates when AI feels useful and personal.
• Empowering the team to build tools drives scale.
• Simulated stakeholders reduce friction and increase confidence.

More than a co-pilot, AI has become a rehearsal space for leadership decisions.

What's Next: Expanding Conversations and Automating Flow

We’re already looking ahead to:

• Automated Simulated Review Cycles: using Zapier to automate review cycles from the point of view of multiple, real stakeholders at accounts
• Persona-to-persona conversations: building workflows (automated or manual) letting simulated roles “debate” strategy.
• Conflict detection: what happens when your simulated CEO and CRO disagree?
• Adaptive simulation: building processes for modifying personas as new inputs are added, to reflect changes in stakeholder behavior.

The goal is a flexible AI simulation layer that mirrors internal decision-making dynamics and external buyer behavior.

Surely There's a Catch?

These agents aren’t psychics, they’re mirrors.

They reflect what people have chosen to express: what they say in meetings, how they email, what they post publicly. They can’t tell you what someone’s hiding or hasn’t yet revealed.

If a buyer changes their mind often or if a stakeholder withholds concerns, the simulation may miss those nuances.

That’s why these tools are not decision-makers. They sharpen your ideas. They don’t make the final call.

Before you pitch your strategy to the real people, what if you could pitch it to a room full of near-perfect simulations first?

Let’s help you do exactly that.

Send us a current pitch or strategy doc, and we’ll show you what your simulated CMO, CRO, and VP of Demand Gen would say.

We’ll help you pressure-test messaging, uncover blind spots, and improve alignment before you walk into your next high-stakes meeting.