Understanding the Knowledge Base in AI for pharma

By Manuel Mitola, expert-trainer of The AI for Pharma Marketing Course 
 

When exploring how AI can help you in your pharma marketing role, you will often come across the term Knowledge Base (KB). But what does it really mean in a pharma context, and why is it important?

A Knowledge Base is a structured system that supports AI-driven decision-making by bringing together structured and unstructured data. In the pharma industry, a strong KB allows for smarter stakeholder engagement, quicker responses to market changes, and more impactful commercial strategies.
 

Is a KB just a warehouse of a lot of data?

Let’s explore the three types of Knowledge Bases in pharma AI:

1. 📊 Explicit Knowledge Base (Structured Data)

This type of KB includes well-documented and codified information that can be stored and retrieved systematically. It forms the foundation of many AI tools used in pharma marketing.

Examples:

• Regulatory guidelines from authorities such as the FDA and EMA
• Drug specifications including indications, mechanisms of action, and contraindications
• Competitor analysis with market share and product comparisons
• Standardised medical FAQs and response guidelines for HCPs
   

2. 🔣 Tacit Knowledge Base (Unstructured Insights)

Tacit KB includes knowledge that comes from experience, human intuition, and expertise. It is not easily captured in documentation but is essential for shaping strategy and decision-making.

Examples:

• Learnings from sales reps based on their conversations with HCPs
• Reactions and feedback from HCPs on different engagement styles or product positioning
• Insights from KOLs gathered during conferences and medical meetings
• The emotional perception of a drug in the market, which contributes to brand positioning
   

3. ⏱️ Real-Time Knowledge Base (Dynamic Information)

Real-time KB is continuously updated to reflect the latest developments. This ensures that AI systems stay aligned with current scientific, regulatory, and market trends.

Examples:

• Updates from ongoing clinical trials
• New regulatory changes and communications
• Strategic actions and moves by competitors
• Shifts in HCP behaviour and engagement
   

Why This Matters for Pharma Marketers

To make use of the full potential of AI in pharma marketing, it is important to understand how to build and apply all three types of knowledge. Whether you are incorporating real-time updates to optimise communication or using field insights to personalise your strategy, your AI capabilities depend on the strength of your knowledge base.

That said, is it then important to have a higher volume of data than high-quality data? Still no.

Because: GIGO…. Garbage in, garbage out! 😊
 

Learn How to Put This into Practice

Join our AI for Pharma Marketing Course and discover how to effectively use structured, tacit, and real-time knowledge to drive better marketing outcomes, improve HCP engagement, and make more informed decisions.