12 AI terms every pharma marketer must know

Pharma marketers don’t need to be data scientists, but knowing the right AI terms helps you ask the right questions and spot the real value. To provide clarity, Manuel Mitola, CELforPharma faculty member of The AI for Pharma Marketing Course, put together this list of essential AI concepts explained in plain language, with examples of how they apply directly to pharma marketing.
 

1. Generative AI (GenAI)

AI that can create new content such as text, images, video, or even voice.

💡 In pharma: Creating for awareness or for promotion materials dedicated to HCPs, patient education materials, or compliant content variations at scale.
 

2. Large Language Model (LLM)

AI models trained on huge amounts of data to understand and generate natural text-based outputs.

💡 In pharma: Drafting summaries of clinical studies or MLR-compliant marketing copy.
 

3. Prompt Engineering

The art of writing clear instructions to get accurate results from AI.

💡 In pharma: Crafting prompts that produce compliance-friendly content effectively.
 

4. Hyper-Personalisation

Delivering the right message to the right person, also in real-time.

💡 In pharma: Going beyond traditional segmentation by tailoring engagement to individual HCP or patient behaviours.
 

5. Knowledge Base (KB)

The set of information that fuels AI tools.

💡 In pharma: Feeding AI with MLR-approved data to reduce compliance risks.
 

6. Next Best Action (NBA)

AI-driven recommendations on the optimal next engagement step.

💡 In pharma: Helping customer-facing teams focus on the most impactful actions or messages.
 

7. AI Agents

Semi-autonomous AI tools designed to perform specific tasks.

💡 In pharma: Automating market research analysis or running MLR pre-checks; Internal “AI copilots” for safe, on-brand content generation.
 

8. AI-Powered MLR Aiders (Pharma specific)

AI designed to pre-validate content before MLR submission to speed up approval cycles and reduce manual revisions.
 

9. Data Quality (GIGO)

“Garbage In, Garbage Out” – AI is only as good as the data you feed it.

💡 In pharma: Using clean, validated data to ensure accurate insights.
 

10. 4 Vs of Big Data

Volume, Velocity, Variety, and Veracity – the four pillars that define big data.

💡 In pharma: Managing the complexity of clinical, real-world, and engagement data.
 

11. Human-in-the-Loop (HITL)

Keeping human oversight in every AI-driven decision.

💡 In pharma: Marketer, MLR and compliance teams remaining the final gatekeepers.
 

12. AI-Powered NLP Chatbots

Conversational Natural Language Processing agents capable of speaking with human beings.

💡 In pharma: 24/7 support for patients or HCPs with accurate, compliant information; agents capable of replicating realistic sales scenarios for role-playing.

AI will not replace the fundamentals of good pharma marketing but it will change how those fundamentals are applied. Knowing the key terms is the first step to using AI where it really adds value.