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Actionable
Takeaways
Listen to Your Customers:
Continuous feedback from users is crucial. Adapt your product to fit seamlessly into their workflows and address their specific needs.
Focus on High-Quality Data:
In AI, the quality of your data is paramount. Invest in robust data acquisition and processing to ensure reliable and actionable insights.
Prioritize Explainability:
Especially in complex fields like pharma, ensure your AI models provide clear, understandable insights that users can trust and act upon.
Be Selective with Investors:
Choose investors who align with your values and vision. They should offer more than just capital, bringing valuable expertise and support.
Plan for Long-Term Impact:
Beyond immediate growth, aim to create lasting value in your industry. Strive for broad recognition and tangible improvements in your field.
Conversation
Highlights
When Conservative Industries Reject Your Technology: How Intelligencia AI Cracked Pharma’s Go-to-Market Code
In a recent episode of Category Visionaries, Dimitrios Skaltsas, CEO and Co-Founder of Intelligencia AI, shared a story that most AI founders in conservative industries will recognize. Picture this: You’ve built groundbreaking technology. You’ve solved a massive problem. But when you walk into customer meetings, you’re met with skepticism, confusion, and polite rejection. Not because your solution doesn’t work—but because the industry isn’t ready to believe it can.
For Dimitrios and his team at Intelligencia AI, an AI drug development platform that’s raised $15.5 million, this wasn’t just an occasional hurdle. It was their daily reality for months.
The Moneyball Moment That Defined Their GTM Strategy
Dimitrios describes those early customer conversations with startling clarity. “I was often watching it in the early days, like, literally with my Co-Founder, data scientists, like, sitting and watching the movie,” he says, referring to Moneyball. 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.”
The pharmaceutical industry, despite its scientific sophistication, was stuck in what Dimitrios calls “suboptimal” decision-making processes. Companies were relying on benchmarks and expert opinions to assess drug development risk—a fundamentally outdated approach in an era where 85-90% of clinical trials fail. “You have this paradox where science is trade season making. Science is suboptimal, it’s not evolved enough,” he explains.
But here’s where most founders make a critical mistake: They assume conservative industries need to be convinced through more aggressive sales tactics, bigger marketing budgets, or flashier demos. Dimitrios took the opposite approach.
Building the Product Before Selling It
Unlike many startups that sell first and build later, Intelligencia AI started product-first. They launched in fall 2017 and spent months building their MVP before reaching out to potential customers. “We had the first results in sometime late spring 2018. And I was like, wow, we solved the problem, right?” Dimitrios recalls.
In pharma, where innovation propensity is “unfortunately a bit backwards” outside of drug creation itself, you can’t sell on promises. You need proof. They signed their first customer in early 2019—a major pharmaceutical company building an external innovation function. “They were building something that was forward looking and they want to embed. Innovate elements were the perfect match for them,” Dimitrios says.
The Lesson That Transformed Their Entire Approach
Landing that first customer was just the beginning. What happened next shaped everything about how Intelligencia AI approaches go-to-market today.
When asked about his most important GTM lesson, Dimitrios doesn’t talk about sales processes or marketing channels. Instead: “Listen to your customer again and again.”
He breaks it down: “A great solution is not enough. Ultimately, any company wants to have impact, right? You won’t have customers, you won’t have users. You want to move the needle in your space and obviously that’s only possible if people use you.”
The breakthrough came when they stopped thinking like technologists and started thinking like partners embedding themselves in actual workflows. “You build something great, that’s the core. Then you need to find how to embed it in the workflow of users, how to build the right functionalities around this, how to, you know, move yourself a bit from being a purist, if you will, to what the users exactly need.”
Navigating the AI Hype Cycle in a Risk-Averse Industry
When ChatGPT exploded onto the scene in late 2022, most AI companies celebrated. For Dimitrios, the reaction was more nuanced. “It turbocharged all these, it kind of accelerated something that already started being in motion during COVID,” he says. But acceleration isn’t always positive.
The real watershed moment for AI in pharma wasn’t ChatGPT—it was COVID-19. The pandemic proved that drug development could move faster than the traditional 10-year timeline. That’s when the industry’s attention shifted to AI and digital solutions.
But ChatGPT’s meteoric rise created a new problem: noise. “There are pharmaceutical companies actually are kind of shutting down or shutting out subject from their organizations because they’re like, okay, we cannot trust it yet, so don’t use it literally,” Dimitrios reveals. This skepticism bleeds into perceptions of all AI solutions.
So how does Intelligencia AI cut through the noise? By going deeper than the competition. “People who use both or multiple solutions, you know, they turn, tell us, okay, you’re not real competitors, because you do. Actually, I go deep where some of the cases, it’s a touch AI,” Dimitrios explains. The market is getting savvier, distinguishing between genuine AI that solves complex problems and “AI” that’s more marketing than substance.
The other critical differentiator? Explainability. “These are highly sophisticated users who want to understand and actually embed AI into their own pattern recognition, into their own decision making,” he notes. Black boxes don’t work in pharma. Trust requires transparency.
The Fundraising Drug and Getting to Cash Flow Positive
Dimitrios has a provocative take on venture funding: “Sometimes I joke funds is our track in a good way. It’s something you need survive, especially in the early days, especially if you build technology as we did… At the same time, I call it a drug because you can also become dependent on this.”
Intelligencia AI has raised $15.5 million and built patented technology that’s “pretty unique for an AI company in our space.” But Dimitrios’s proudest achievement isn’t the capital raised—it’s reaching cash flow positive while maintaining high growth. “Right now, to some extent, R and D is funded by our customers,” he says.
This is GTM discipline. Instead of throwing money at unproven channels, they’ve fine-tuned their approach until it works reliably. “I don’t feel like if we had a lot more money to throw into the commercial lens and would necessarily get better results. Sometimes it’s the opposite,” Dimitrios admits.
For founders in conservative markets: Build something undeniable. Listen obsessively to early customers. Embed yourself in their workflows. Cut through noise by going deeper than anyone else. Prove your model works before scaling. Sometimes the best go-to-market strategy isn’t about moving faster—it’s about moving differently.
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