Monte Carlo's Co-founder & COO, Jordan Van Horn, started the year with a question that would reshape everything: "How would we run GTM if we started the company today and were AI native from the beginning? What would that unlock and how would it change the trajectory of the entire organization?"

By June, Monte Carlo's team was well on their way and trying to accelerate.

The vision and the gap

A few months after Jordan posed the question to his team, Monte Carlo had created a roadmap of AI agents and workflows and started prototyping. They went persona by persona, evaluated their GTM motion for potential productivity gains, and built agents one by one.

Monte Carlo had an advantage: they're a deeply technical company. Some agents made it to production, having proved out complete flows with real results. But then Jordan saw the problems: "You have two or three activators in your organization going nuts with AI. And then you have the vast majority of the rest of the team that won't adopt new technology." Further complicating the adoption issue was that Monte Carlo now had a big sprawl of technologies across different LLMs and tools.

When power users experiment with different tools, there's no path for the rest of the team to follow. So although the vision was right and the prototypes were working in isolation, transformation at the organizational level wasn't being achieved.

Solving for the entire team

Jordan needed a solution that would actually change behavior and yield real results across the entire team. He decided to look externally and evaluated eight vendors in two weeks on multiple criteria: partnership quality, iteration speed, and company viability (a priority underscored when two AI companies shut down during those two weeks).

Endgame hit the mark on all dimensions, but what truly separated Endgame was market perspective. "Endgame had a better and more well-thought-out perspective of where the GTM space is going with AI. It felt more nuanced and well thought out than mine, candidly,” including understanding that the adoption challenge was different from the technology challenge.

That insight shaped Monte Carlo's trial. They ran a two-week pilot focused on whether the broader team, not just power users, would actually use it. "Your product was wildly successful—including with people who weren't power users," Jordan reflects.

A few months later: 80% adoption across Monte Carlo's GTM team.

"Endgame has become the OS for our revenue team, with over 80% adoption. We've replaced consumer AI tools for everything from meeting prep to customer presentations to business analysis. We're on a mission to be an AI-native company and Endgame has been a huge accelerant."

Jordan Van Horn
Co-founder & COO at Monte Carlo

How did adoption work so well? Three main reasons:

Their own data made it real. "Getting them to see it with their own data was really eye-opening," Jordan explains. "For the first time, they saw with their own eyes that they were in the middle of a monumental shift." Generic AI demos don't change behavior. Seeing it work on your accounts, your calls, your deals does.

The data integration worked. "The actual pulling together of data sources and being able to make sense of it is your guys' superpower," Jordan notes. Most tools require constant manual curation or endless custom integrations. Because Endgame solved this—connecting Salesforce, Gong, Slack, and Google Drive seamlessly—the team actually used it instead of abandoning it.

It recovered real time. Jordan estimates teams spent 10-20% of their week "taking data and pulling it together and curating it into a presentation." When call prep that took hours took minutes, and account plans that got skipped actually got done, the value was immediate and tangible.

Tim Miller, Monte Carlo's CRO, describes what this looks like in practice: "I'm prepping for Delta Airlines and can ask what are the top 20 data use cases in airlines and get that in a very rapid fashion. It makes us more effective in the conversations we actually have."

From adoption to compounding capabilities

The 10-20% of their week reps are getting back can now be utilized for higher value activities. But more importantly, when the entire team works in the same system, you get both individual productivity and organizational intelligence. Training gaps surface automatically. Patterns emerge across the pipeline. The team is shifting from reactive to proactive.

Tim Miller describes the evolution: "There was the low hanging fruit like better call prep and more rapid discovery. Then it evolved into: how do we actually get more productive in where the gaps are in our sales process and pipeline generation?" The progression from individual efficiency to strategic insights shows what becomes possible when the entire team works in the same system.

"I can pull up strategic insights across all our wins in seconds—the top 20 initiatives we're tied to, the best use cases for a specific industry. That quick identification helps us make deals move faster and larger."

Tim Miller,
CRO at Monte Carlo

Six months in, Monte Carlo has 80% adoption across their GTM team. The sprawl of tools and isolated power users has been replaced by a unified system where insights compound across the entire organization. The vision they started with is becoming reality because they solved adoption first, then let capabilities build on that foundation.