A few years ago, I was pacing around late at night, refreshing my inbox every five minutes, waiting for a “big enterprise client” to reply. That morning, the prospect’s product team and two VPs were thrilled about our analytics solution—raving about how it could streamline their release workflow. By evening, radio silence. Something had clearly changed behind the scenes. Through our champion, I learned that one team lead thought our product was perfect, a VP believed it didn’t fit the current meta, and another stakeholder had no idea what it was all about. It felt like a secret government black box—decisions being made (or unmade) in Slack channels I couldn’t access.

Those cat-herding experiences—seeing entire deals vanish without warning—became unsettlingly normal in enterprise sales. Yet sometimes, the stars aligned: we navigated every handshake, secured the right champion, and scored our biggest wins. The quarter often swung on a single deal. What made the difference? Could we turn every “deal won” into a repeatable playbook? Late nights found me drawing elaborate mind maps, trying to decipher the hidden forces driving organizational buying decisions.

 

 

I dove into ideas like marketing memetics and selling in vectors, and a few pieces started falling into place. Sometimes it was a single sentence—one message—that sealed the deal. I’m not kidding: once, I showed a senior stakeholder how a million-dollar project wouldn’t fix the very problem they were trying to solve, and backed it up with an example of another company pivoting away from the tool they’d just pinned their hopes on. That revelation shattered his assumptions, the word spread, and months later we signed one of our largest contracts.

Still, in a high-stakes environment where a deal can be life-or-death for a quarter’s target, you can’t rely on chance. So when I read how Mike simulated an election to flip virtual voters, it clicked: why not do the same for sales engineers and account-based marketers? 

We could stage the ultimate “impossible buying committee”—loaded with clashing agendas, strict budget hawks, and a founder overshadowed by corporate demands. Then we’d methodically test different pitches, watch votes shift in real time, and crack the code on closing massive deals—no guesswork required.

This essay is the behind-the-scenes of that experiment—and how I got the impossible buying committee to say, “Take our money.”

Creating the “Impossible Buying Committee”

I once worked with an enterprise client whose sales process demanded buy-in from multiple department heads—each with its own tangled web of politics and maturity levels. That experience taught me just how chaotic these group decisions can be. To replicate that “herding cats” feeling, I cranked the difficulty to hard mode: the scenario unfolds mid-acquisition, with some stakeholders resentful about the merger and others scrambling to stay relevant. You don’t learn much in easy mode—real enterprise deals are never that tidy. So I took every challenging trait I’ve seen in negotiations and dialed them up to eleven.

Step 1: Cloning The Personas

To build our impossible buying committee, I began by prompting GPT to generate a set of hypothetical mandates, mindsets, and objections—capturing those instinctive, knee-jerk reactions common in enterprise negotiations. I then fed this initial scaffold into Rally, which cloned and expanded these roles with detailed backstories, defined responsibilities, and unique quirks. The result was a cast that radiates high drama, bureaucratic fireworks, and merger mayhem—all set in an environment where extreme cost cutting is non-negotiable.

 

 

I refined these cloned personas further by incorporating realistic details. Each persona now comes with an initial stance on our analytics proposal (bias); clear deal-makers and deal-breakers; and explicit notes on hidden agendas or conflicts. I even specified their voting dynamics, highlighted key interpersonal relationships and office politics, and added sample dialogue snippets that capture the unpredictable nature of enterprise decision-making. This process laid a solid foundation for our experiment, faithfully mirroring the chaotic, high-stakes world of real enterprise deals in a controlled, simulated setting. I used the persona edit feature in Rally to do this.

 

 

A committee that instantly agrees is not only unrealistic—it’s also dull. To replicate genuine enterprise friction, I seeded each persona with distinct, often opposing motivations. For example, the cost-conscious CFO is at odds with Marketing’s drive for more ad spend; the Product lead pushes for rapid system unification while the Founder worries about losing the startup’s unique “magic”; and the Operations team, which scrutinizes every expense, conflicts with the M&A leader who cares only about hitting long-term targets. These engineered tension points emulate the inherent guesswork in real deals, where you never quite know who’s really calling the shots.

 

To keep the scenario truly spicy, I introduced a twist: legacy analytics chaos at the smaller company. Everyone despises the outdated system—but no one’s willing to fund a new one. This discrepancy serves as our wedge: if you can prove that investing in analytics fixes enough headaches to justify the cost, you might finally win them over.

The Vote Mechanic and Synthetic Voting Process

For our experiment, I implemented a simple “Yes buy/No reject offer” vote after each pitch iteration. In practice, this meant sending a synthetic email to our simulated committee and having each persona cast their vote on whether to buy the analytics vendor. The email itself was intentionally ambiguous—mimicking the typical enterprise memo that leaves room for interpretation and internal debate.

 

 

In essence, this dynamic vote mechanism not only provided immediate feedback on each pitch iteration but also served as a powerful diagnostic tool. It allowed us to see, in near real time, how even slight shifts in messaging could alter stakeholder sentiment. The combination of the synthetic vote and cross-model analysis transformed our experiment into a living, breathing simulation of an enterprise buying committee—a high-stakes game show where every vote matters and each shift in sentiment reveals a critical part of the story.

To test if our models could accurately capture the nuanced dynamics of these high-stakes decisions, I ran this synthetic vote across four models, spanning both our fast and smart modes. 

 

 

All the models picked up on key narrative elements—such as poor Alex’s struggle to preserve his product’s identity after selling his small company to the larger entity. Interestingly, the OpenAI model running in fast mode yielded the highest number of “no deal” votes. 

Curious about this divergence, I compared our OpenAI fast vs smart mode. Initial findings revealed that the smart mode was more emotionally expressive and decisive—evaluating proposals more conditionally by layering in considerations of reputational, strategic, and emotional risk. I’ll run some deeper testing here for a follow up essay, but based on this initial finding, I decided to have some fun trying to convert our virtual buying committee in smart mode. 

I tested 5 different core narratives. (1) work reduction, (2) long term ROI, (3) minimal disruption, (4) innovation, and (5) customer centric. 

 

 

After running our synthetic votes across the five distinct narrative emails, I had the numbers—but more importantly, I had a story. Some pitches managed to sway a good chunk of the committee, revealing interesting insights into what resonated and what fell flat. Yet, one figure remained unswayed: CFO Catherine. Despite the varying messages, her response was a steadfast “No”—a brutal reminder of the unyielding nature of enterprise cost-cutting.

 

Determined to understand the root of these responses, I decided to dig deeper into the objections. I prompted a sort of Objection Decoder—designed to synthesize and map out nuances of exactly what each persona cared about. Without revealing the persona context, but just based on their responses in a single thread.

In no time, I had a clear, visual overview of our committee’s core concerns. For instance, while our Marketing and Product leads were easily nudged by proposals about enhanced lead generation and product unification, CFO Catherine’s objections centered around rigid cost controls and a deep-rooted fear of any non-immediate ROI. Poor Alex’s heartfelt pleas to preserve “startup magic” and the Operations lead’s vigilance over hidden fees were also clear on the map.

 

Armed with these insights, I fed the objection map into a separate prompt—along with an example one-page business case—to generate a refined pitch that addressed every objection head-on. The result was a meticulously crafted proposal that charted a clear pathway through the concerns, turning hesitant stances into near-universal enthusiasm across the committee.

 

 

Every persona flipped their vote—from “Reject offer” to an enthusiastic “Yes”—except for CFO Catherine, whose stubborn “No” persisted. Her focus was clearly on cost-cutting, paired with an ironclad fiscal discipline, she was hard to nudge over the line. Even with a compelling business case.

 

I wasn’t ready to accept defeat just yet. As I dug deeper into Catherine’s objections, I noticed a subtle, almost offhand remark: “If Eva backs this…, I might reconsider.”

 

Wait—who was Eva again?

 

 

That was when it clicked. I’d embedded a small nuance in Catherine’s persona context to capture her soft spot for Eva’s board-level insights. 

 

 

So I tested the pitch again with explicit endorsement from Eva, using her response from the last vote.

 

 

​​The result?

 

 

In the end, I managed to win over our entire simulated committee—even CFO Catherine eventually flipped her vote after the key endorsement from Eva. What started as a seemingly impenetrable wall of “No” transformed into a unanimous “take our money.” This wasn’t a win on paper—but it shows Rally isn’t just a tool for simulating stakeholder responses—it’s a powerful training ground for navigating the enterprise deals.

This experiment was far more than a fun simulation; it was a v1 test ground for a new breed of iterative process that provided immediate, actionable feedback on how our messaging could be refined to create consensus. The synthetic vote mechanism paired with role-playing agents sharing inner thoughts reveals exactly which elements resonated with each persona. Allowing me to beat the sim (this time) and discover something worth testing in the real world.

Key Takeaways:

• The Power of Iterative Messaging: Constantly refining your pitch based on real-time feedback is crucial. While one narrative might win hearts on one front, it can trigger a defensive “No” on another.
  
  

• Objection Mapping: Tools like the Objection Decoder are invaluable—they quickly expose the hidden drivers behind each stakeholder’s vote, empowering you to craft more targeted messages.
  
  

• Emotional Nuance Matters: Our analysis showed that models in smart mode, with their layered and emotionally expressive evaluations, often mirrored real-world stakes better than their fast-mode counterparts.
  
  

• There Will Always Be a Gatekeeper: CFO Catherine’s steadfast “No” is a stark reminder that some objections run so deep that no amount of refinement will change them. In enterprise sales, that final holdout is an ever-present challenge.