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How Parable Achieved a 100% POC Win Rate in Enterprise AI Sales

Ninety-five percent of CFOs report seeing no ROI from their AI investments. When that study dropped, the AI optimist crowd immediately pushed back—claiming the methodology was flawed, the sample size questionable. But Adam Schwartz, Co-Founder and CEO of Parable, had a different take: “It was according to CFOs. And I think that’s a relevant arbiter of truth.”

The statistic exposes something more fundamental than whether AI “works.” Enterprises are pouring hundreds of millions into improving operational efficiency without first defining what operational efficiency actually means. You can’t optimize an undefined metric.

In a recent episode of BUILDERS, Adam shared how Parable built an intelligence platform that quantifies collective time allocation across organizations—and more importantly, how they engineered a go-to-market strategy that led to never losing a proof of concept.

The False Signal Problem

The AI transformation market in 2024 created unprecedented demand-side curiosity. For most enterprise software founders, getting executive attention is the hard part. Adam faced the opposite problem.

“You’re going to get a lot of curiosity in your go to market,” Adam explained. “They are not going to shut the door in your face.”

Prospects would engage enthusiastically. They’d attend discovery calls, request follow-up meetings, loop in additional stakeholders. All the signals that typically indicate buying intent. Except they weren’t buying—they were learning.

“They want to keep talking because they want to learn and they want to know what’s going on and they are genuinely interested,” Adam said. “I don’t think they’re doing this like to be annoying.”

This dynamic destroys enterprise sales economics. Long cycles that feel like progress but never convert. Pipeline that looks healthy but doesn’t close. Adam needed frameworks that forced prospects to disqualify themselves early.

The Organizational Structure Filter

Adam’s qualification mechanism cuts through stated intent to reveal actual commitment: organizational design.

His question to every prospect: “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.”

Not a steering committee. Not a cross-functional working group. A named individual with three specific characteristics:

Direct C-suite reporting line. If the AI transformation owner reports to a VP who reports to the CIO, they lack the authority to drive change across business units. Real transformation requires executive sponsorship that can break through functional silos.

Dedicated budget. Not “we’ll find money if we see ROI.” Allocated budget for this fiscal year with committed headcount. Budget reveals priority in a way that verbal enthusiasm never does.

Empowered team. One person can’t transform an enterprise. They need product managers, program managers, and technical resources. The team size and composition signals how seriously leadership takes the initiative.

Adam found that buying Gemini or Glean means almost nothing. “To me, like potentially that’s non serious,” he explained. Many enterprises expect vendors to carry them through transformation—Microsoft handles productivity, Atlassian handles collaboration, and AI becomes something they consume passively rather than strategically deploy.

Serious buyers have attempted five-year work-backs. They’ve mapped how AI will reshape their industry, business model, and pricing model. They’ve identified cascading effects. Most haven’t achieved this fully—Parable essentially productizes that strategic process—but they’re actively working toward it rather than waiting for solutions to present themselves.

Buyer Journey Timing

Adam initially hypothesized Parable could serve two distinct buyer types: companies just beginning AI exploration who needed diagnostic audits, or companies with programs in flight who needed measurement.

Sales data invalidated the first segment entirely.

“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,” Adam said. “Those are the people that we really want to be talking to.”

Early-stage explorers aren’t buyers. They’re learning. They attend webinars, read whitepapers, take meetings. But they lack the organizational infrastructure to implement solutions. More critically, they don’t understand the problem deeply enough to evaluate solutions effectively.

Companies with programs in flight operate differently. They have dedicated teams doing actual product work—talking to business stakeholders, generating use cases, building product briefs, managing roadmaps.

“You ask them what they do all day, they do product work,” Adam explained. “They go talk to business stakeholders, they ask them what they do all day, they come up with use cases, they build a product brief against that.”

These buyers have encountered the measurement problem firsthand. They’ve deployed tools, claimed success based on adoption metrics, and gotten pushback from finance teams asking about actual business impact. They understand viscerally why Parable exists.

Parallel ICP Experimentation

Rather than select a target segment based on intuition, Adam ran controlled experiments across multiple ICPs simultaneously.

In September 2024, he wrote a memo to his team: “We’re going to go after lots of different ICPs. 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. And it’s going to be maybe a little painful, but I would rather do that than sort of put my finger in the air and just guess.”

This approach only works if you have conviction in your product’s core value. Adam had that conviction because of one critical metric: Parable’s POC win rate.

“Once we get our platform into the hands of a company and the leaders… have this like, quantitative understanding of where they’re starting from,” Adam said. “It’s very powerful and it works and we’ve never lost a POC.”

One hundred percent POC win rate means the bottleneck isn’t product quality—it’s identifying which prospects will actually reach the POC stage. That insight justified the parallel experimentation strategy. Rather than optimize for product-market fit first, Adam used go-to-market motion to validate sales-market fit and problem-market fit as precursors.

The experimentation revealed something unexpected: Fortune 500 companies universally take AI transformation seriously. Fortune 5000 companies show far more variance. Company size alone doesn’t predict buyer quality—organizational maturity around AI does.

The Technical Foundation

Parable’s technical approach explains why POCs convert so reliably. The platform ingests activity and log data through a thousand data connectors, building what Adam describes as a proprietary knowledge graph or ontology.

The insight layer is what differentiates them: “We measure and understand how an organization is spending its collective time, how hundreds of people collectively aggregated together are spending their time.”

Not individual productivity tracking. Collective time allocation. This distinction matters enormously for AI ROI measurement. Individual tools might save a specific person 30 minutes daily. But does that translate to organizational efficiency? Or does that person just fill those 30 minutes with lower-value work?

Parable quantifies collective time spent on specific processes, then models how AI interventions would affect that allocation. “If this is how we spend our time today, how might AI affect that?” Adam explained. “That’s how we build rationality into use cases and into budget cases for AI investment from a quantitatively backed graph.”

The platform sets a quantitative baseline, validates AI investment cases before deployment, then measures actual impact post-implementation. This closed loop is what CFOs need to see ROI.

The Measurement Category Imperative

Adam’s thesis extends beyond Parable’s specific product. He believes the entire AI vendor ecosystem needs to shift from adoption metrics to quantified business outcomes.

“There is too much hand waving around soft efficiency gains of your product,” Adam warned. “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 previous decade over-indexed on revenue attribution because venture investors demanded it. But that created dishonest narratives. Not every B2B tool directly drives revenue. Some save time. Some reduce operational costs. Some enable reallocation of human resources to higher-value work.

The dishonesty isn’t claiming these benefits—it’s failing to quantify them. Adam used Calendly as his example: “I would actually argue as it relates to Calendly is that it didn’t drive you revenue, it saved you time. But they just don’t know how much. And I’m sort of saying well but I do.”

Parable partners with AI companies to validate their value propositions quantitatively. An agentic platform claims to reduce incident response time? Parable measures baseline time allocation, tracks changes post-deployment, and quantifies the delta. This creates a symbiotic ecosystem where AI vendors can prove their products work and enterprises can justify continued investment.

First Principles at Scale

Adam believes AI transformation will fundamentally reshape enterprise operations, but the path requires questioning assumptions embedded in organizational structures for a century.

“It’s going to be a circuitous route to that place and it’s going to require a lot of first principles thinking which calls into question really a century of management structure and operational structure,” Adam said. “And that’s not going to, it’s not, that’s just not a straight path.”

The shift from treating AI as CapEx experimentation to OpEx investment with measured returns means enterprises need new operational frameworks. They need to define efficiency quantitatively before investing to improve it. They need measurement infrastructure that captures baseline state, tracks interventions, and attributes outcomes.

Parable’s go-to-market journey offers a blueprint for founders building in emerging categories where buyer education and buyer readiness create false pipeline signals. Engineer disqualification mechanisms that force prospects to reveal organizational commitment. Use sales motion as an ICP discovery tool when TAM is massive but acute pain is concentrated. Target buyers living the problem daily rather than those exploring it theoretically. Build measurement precision into your category narrative from day one—soft claims won’t survive renewal conversations.

The enterprises that survive the next decade will be those that master operational measurement. The AI vendors that thrive will be those that prove their value in dollars, not adoption percentages.