Marketers & Data Scientists—It’s Time to Talk

The biggest barrier to AI success in Pharma isn’t the technology—it’s the people.

Too often, commercial teams and data scientists operate in silos. Marketers focus on strategy and engagement; data scientists focus on models and metrics. But AI thrives on collaboration—and that means we need to talk.

Here’s how we build smarter, cross-functional partnerships:

Start with the Problem, Not the Tech

Instead of asking “How can we use AI?”, ask “What’s our biggest strategic challenge—and could AI help solve it?” Whether it’s identifying trial participants or personalizing HCP engagement, the business need should lead the way.

Learn to Speak Each Other’s Language

Marketers don’t need to become coders, but they do need to understand the basics of data science. Likewise, data teams need to grasp market realities, patient journeys, and regulatory constraints. Shared language builds trust.

Build Feedback Loops—Not Hand-Offs

The best AI solutions are iterative. Strategy informs model design, and real-world feedback refines it. This isn’t a relay race—it’s a co-creation process.

If we want AI to deliver real value, we need to break down the silos. What’s helped you bridge the gap between strategy and data? Let’s share what’s working.

Author: Agata KInga Kaczmarek - The Health Tech Advocate