The false hope of GTM prototypes

When BetterUp gave their teams access to ChatGPT, Claude, and other AI tools last fall, the experimentation was immediate. Custom projects and GPTs proliferated across the organization. But the most common request quickly surfaced:

"I love Claude, but could it please ingest our internal data?"

Teams built promising prototypes, yet they couldn’t get beyond the demo phase. Leadership wanted book-level risk analysis. Account executives managing 25–50 accounts and 20+ renewals each needed full visibility into blind spots. Single-use workflows worked, but scaling them didn’t.

Without Salesforce data, they couldn't generate account briefs. Adding Slack and call transcripts was the next logical step, but the architecture wasn’t there. "People were just learning what an MCP server was, and most couldn't even talk about it. Meanwhile, everybody was fully allocated to other projects," notes Austin Johnsey, Director of GTM Systems.

“There's some naivete about combining all our data sources and thinking it'll be blissful. When people start assuming they can just combine data sources and get robust artifacts, they're probably going to be shocked at how poor the results are.”

— Austin Johnsey, Director of GTM Systems

Austin tried building his own version. "I got on Claude, started connecting a few things, querying about one person or one account. It worked decently well. Then I tried to look at someone's entire book of business and Claude just crashed." The pattern repeated across the organization. Teams would get something working for a single use case, then hit architectural limits when trying to scale.

Prototyping is easy. Production-grade infrastructure is hard. One of BetterUp’s engineering leaders estimated that moving from prototype to production would cost 2–4x more once you factor in implementation, maintenance, and opportunity cost.

What production infrastructure unlocks

For Pete Stratigakis, an Enterprise Account Executive who manages Fortune 500-level accounts, the value was instant. "Instead of analyzing endless history and context, I can ask Endgame for help: 'Highlight any historical interactions where things went wrong. Why did we not go forward? Help me build a hypothesis so I can re-engage.' I cut down a ton of time."

"What used to take an entire account team off what they were doing for hours, Endgame did in a few minutes. Within 20 minutes, we're handing something to an executive and they're making introductions.”

— Austin Johnsey, Director of GTM Systems

Organic momentum spread quickly. Account managers used Endgame to identify blind spots. Sales leadership used it for team inspection. Product marketing analyzed positioning. Kendall Steinmetz, from the Office of the CEO, used it to build briefs for board and executive introductions.

Pete captures the cross-account intelligence value: "With global search, it's not just my book of business but all of our book. I can get thematic analysis: Why do we typically win? Why do customers expand with us?"

Kendall summarized it best: "I am loving it. I'm telling literally everyone I know about it. This product is fantastic."

Building together beats building alone

What sealed the decision wasn't just the product, but the partnership. BetterUp relies heavily on Slack for workflow, and Endgame is working closely with them to build deeper integrations through its Slack bot and MCP server to meet teams where they actually work. "You typically don't get this level of collaboration with a vendor," Austin says. "The fact that we can work directly with Endgame constantly to tailor the product to our workflows has made all the difference."

That ongoing collaboration addresses what internal builds struggle with: continuous improvement and adaptation. "On its own, AI is neat, it's useful," Austin notes. "But when it actually does my work with me, then it's indispensable." That evolution from instructional to indispensable requires a partner invested in the long term, not a one-time build project.

BetterUp’s teams started with prototypes, but scaled into production with Endgame, which has unlocked myriad use cases across many teams. Today, they’re rolling out mass enablement with the confidence that comes from production-grade systems, not proof of concept prototyping.