The Moneyball Strategy: How Intelligencia AI Broke Into Pharma’s Boys’ Club

There’s a scene in Moneyball every founder selling to skeptical industries should memorize. Billy Beane walks into a room full of old-school baseball scouts and pitches data-driven player evaluation. They look at him like he’s speaking a foreign language.

In a recent episode of Category Visionaries, Dimitrios Skaltsas, CEO and Co-Founder of Intelligencia AI, revealed that he and his team watched that movie on repeat during their early days.

“I was often watching it in the early days, like, literally with my Co-Founder, data scientists, like, sitting and watching the movie,” Dimitrios says. The parallel was uncanny: “You’re this, in many ways, you’re this toppy young guy who, you know, speaks a different language, and people are not sure they get it right.”

Here’s what Dimitrios learned about breaking into industries where sophisticated people use suboptimal methods.

The Paradox: Smart People, Outdated Methods

The pharmaceutical industry is filled with incredibly sophisticated scientists—PhD-level researchers, experienced executives. Yet when it comes to deciding which drugs to develop, they rely on approaches that would make a data scientist cringe.

“The way the industry has been historically, traditionally approaching that risk is quite suboptimal, which is shocking because it’s an industry where science is very sophisticated and people are very sophisticated,” Dimitrios explains.

They look at benchmarks, call experts, collate opinions. “It’s a tunnel vision,” Dimitrios says. The result? “You have this paradox where science is trade season making. Science is suboptimal, it’s not evolved enough.”

This is the Moneyball moment. Just like baseball scouts trusted their eyes over statistics, pharma executives trusted experience over AI-driven risk assessment. Not because they’re unsophisticated—because that’s how it’s always been done.

Why You Can’t Win by Making Them Feel Stupid

Most disruptive tech founders make a fatal mistake: They try to win by making the current approach look stupid.

Dimitrios took the opposite approach. Instead of positioning Intelligencia AI as the smart upstart showing pharma how backward they are, he positioned the company as partners who could enhance what was already there.

This matters because in industries with low risk tolerance, people don’t respond well to being told their entire framework is broken. “It’s highly regulated for all the right reasons. And yeah, people are highly sophisticated and very careful on how they do things for all the good reasons,” Dimitrios notes.

The industry’s conservatism isn’t a bug—it’s a feature. Understanding this changes everything about how you sell.

The Language Barrier Problem

When Dimitrios talks about speaking “a different language,” he’s not being metaphorical. Early-stage AI in pharma wasn’t just new technology—it was a fundamentally different way of thinking about risk.

Pharma executives spoke the language of clinical trials and regulatory pathways. Dimitrios’s team spoke machine learning and probability models. “Early days of AI and the pharma is not as risk prone as our industries or the innovation propensity. You know, it’s very specific to creating tracks, but when it comes to technology, it’s unfortunately a bit backwards,” he explains.

This created a chicken-and-egg problem: They needed pharma to trust AI, but pharma wouldn’t trust AI without proof it worked in their specific context. The Moneyball solution? Build proof that speaks their language natively.

Building Credibility Through Results, Not Rhetoric

Billy Beane didn’t win by giving better presentations about statistics. He won by showing results—winning games with undervalued players.

Intelligencia AI followed the same playbook. “We started product first, and for many years, actually, we have retained that money, but we built something,” Dimitrios says.

They spent nine months developing their MVP before reaching out to anyone. When they finally did approach potential customers, they weren’t selling a vision—they were showing outcomes.

This flips the dynamic. Instead of trying to convince skeptical executives that unproven technology might work, they were looking at results and asking how they could be part of making it better.

Finding Your Internal Champion

In Moneyball, Billy Beane had authority to implement his vision. Most startup founders don’t. You’re the outsider trying to change how insiders think. So you need an internal champion.

For Intelligencia AI, that came through forward-thinking teams inside major pharma companies. “They were building this external innovation function where it’s a function where they look for new drugs from smaller companies, from biotech, and by default, they were building something that was forward looking and they want to embed. Innovate elements were the perfect match,” Dimitrios explains.

These teams have permission to innovate, enterprise budgets, and internal influence. They’re your bridge between startup agility and enterprise adoption.

The Explainability Requirement

In Moneyball, the scouts eventually came around because they could understand why the approach worked. The statistics weren’t a black box.

Dimitrios recognized this early: “These are highly sophisticated users who want to understand and actually embed AI into their own pattern recognition, into their own decision making.”

Pharma executives don’t want AI that spits out answers they have to trust blindly. “People appreciate it. They dislike black boxes,” Dimitrios says. In conservative industries with sophisticated buyers, mystery is a liability, not an asset.

The Timing Factor: When Markets Are Ready

Billy Beane’s strategy worked partly because baseball was ready for it. For Intelligencia AI, the readiness moment came during COVID.

“The water said moment in our space in pharma was Covid,” Dimitrios explains. The pandemic proved drug development could move faster. “During COVID it was shown that whoa, can be done much faster. You can accelerate many parts of the process. So that’s where the whole industry moved actually its attention to AI and digital and start investing in the space.”

You can have the right approach and proof, but if the market isn’t ready to change, you’ll struggle. Sometimes you need an external catalyst.

The Transferable Framework

The Moneyball strategy isn’t about baseball or pharma. It’s about selling disruptive approaches to sophisticated buyers in conservative industries.

The framework: Understand why they’re conservative (usually for good reasons). Don’t fight the culture—work within it. Build proof in their language. Find internal champions with permission to innovate. Prioritize explainability over mystery. Wait for or create readiness moments.

Dimitrios didn’t invent these principles by watching Moneyball. But the movie gave him a mental model for what he was experiencing—and that model helped him stay patient during months of rejection.

Sometimes the best strategy isn’t moving faster. It’s understanding the game you’re actually playing.