How Parable achieved a 100% POC win rate in enterprise AI sales | Adam Schwartz

Parable is building an end-to-end intelligence platform that quantifies how organizations spend their collective time—the foundation for measuring real AI impact. With a thousand data connectors ingesting activity and log data across the enterprise software stack, Parable constructs proprietary knowledge graphs that size opportunities and measure outcomes in hard dollars, not adoption metrics. In this episode of BUILDERS, I sat down with Adam Schwartz, Co-Founder & CEO of Parable, to explore why 95% of CFOs see no AI ROI, how his decade running profitable businesses under resource constraints shaped his focus on inputs over outcomes, and why 2026 requires moving AI from CapEx experimentation to measured OpEx.

Topics Discussed:

• Why the 95% CFO stat on AI ROI matters as an arbiter of truth, despite backlash
• Building knowledge graphs from activity data to quantify collective time allocation across hundreds of people
• The fundamental problem: enterprises lack quantitative frameworks for operational efficiency pre-AI
• Running parallel ICP experiments to achieve sales-market fit before product-market fit
• Why Parable has never lost a POC once leaders see quantitative baselines
• Market dynamics creating false signals—unprecedented curiosity without buying intent
• The demarcation between companies treating AI as product work versus those waiting for vendor solutions
• Why AI transformation demands century-old management structures to be questioned

GTM Lessons For B2B Founders:

Engineer disqualification in momentum markets: Market-wide AI enthusiasm creates pipeline illusion. Prospects will engage indefinitely for education without purchase intent. Adam's framework: "How do we get people to say no to us and not drag us along... They want to keep talking because they want to learn and they want to know what's going on and they are genuinely interested." In enterprise sales during category shifts, build explicit qualification gates that force prospects to reveal resource commitment or disqualify. Extended evaluation cycles feel like traction but destroy unit economics.

Use go-to-market as ICP discovery mechanism: Adam intentionally pursued multiple customer segments simultaneously—different company sizes and AI maturity stages—to let data reveal fit rather than rely on hypothesis. His memo to the team: "We're going to go after these three, you know, many different sizes of companies in order for us to decide like, who we like best." The key insight: get to problem-market fit and sales-market fit validation before optimizing product-market fit. This inverts conventional wisdom but works when TAM is massive and the bottleneck is identifying who feels pain acutely enough to buy now.

Qualify on organizational structure, not verbal commitment: Every enterprise claims AI is strategic. Adam's hard filter: "Who in the organization is responsible for AI transformation? And if you don't have a one person answer to that question, you're not serious." Serious buyers have a named owner reporting to C-suite with dedicated budget and team. Buying Gemini, Glean, or other point solutions isn't a seriousness KPI—it's often passive consumption of AI as a byproduct of existing software relationships. Look for companies doing five-year work-backs on industry transformation and cascading effects on their operating model.

Target post-experimentation, pre-scale buyers: Adam discovered the sweet spot isn't companies beginning their AI journey—it's those who've deployed initial programs and now need to prove value. "The market of people that have started to build AI into their operating model or into their strategy in like a coherent way, there's a team, there's an owner, there's budget... those are the people that we really want to be talking to." These buyers understand the problem viscerally because they're living it. They do product work daily—talking to stakeholders, generating use cases, building briefs, triaging roadmaps. They need your solution to professionalize what they're already attempting manually.

Build measurement into your category narrative: The AI tooling market has over-indexed on soft efficiency claims that won't survive renewal cycles. Adam's warning: "There is too much hand waving around soft efficiency gains... you're going to have to renew and you need NRR and I don't think it's going to be that usage of the tool internally by employees and adoption is going to be enough." The last decade over-rotated to "everything drives revenue" due to VC pressure. This decade requires precision: does your product save time, reduce headcount needs, or accelerate revenue? Quantify it. Partner with measurement platforms if needed. Adam's insight on Calendly is instructive—it clearly saves time, but most buyers can't quantify how much, which weakens renewal economics.

// Sponsors:

Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io

The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co

//

Don't Miss: New Podcast Series — How I Hire

Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role.

Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM