Medical Affairs and the new AI: The Emergence of Agentic Intelligence

Artificial intelligence has transformed from experimental pilots to an indispensable assistant for Medical Affairs teams. The past five years have seen industry rocket forward from text-based generative AI that authors scientific responses to fully agentic AI systems that independently triage incoming questions, populate and maintain knowledgebases, and coordinate the convergent workflows across disparate systems. This comprehensive blog details evolution and explains why the transition is important for Medical Information (MI) operations. [1][2][3]

The AI Evolution in Medical Affairs

A Decade of Rapid Change [4]

• 2015-2019: Early machine-learning chatbots sort inquiries and auto-suggest from standard response letters.
• 2020-2023: Large language models (LLMs) provide generative AI (Gen AI) for scientific content drafts, literature summarization, and virtual medical advisors.
• 2024 and Beyond: Agentic AI which is a new class of autonomous “AI agents” that see context, plan multi-step tasks, act across systems, and don’t stop learning.

Why the Jump to Agentic AI Matters

Generative AI tackles discrete, text-heavy tasks, however, Medical Affairs requires end-to-end orchestration ranging from intake, classification, compliance, escalation, and insight generation. Agentic AI provides that autonomy by being policy-based with the ability to enact policies and to react in real-time to feedback. [5][6][7]

Generative AI in Medical Affairs (2019-2023)
Core Use Cases:

1. Scientific Content Drafting: Large Language Models (LLMs) auto-generate standard response documents in seconds which can reduce writing time by 60-80%. [8][9][10][11]
2. Rapid Literature Summaries: Generative AI does the reading for reviewers, summarizing 1,000s of papers into concise evidence tables, cutting review time 5-fold. [12][13]
3. Virtual Medical Advisors: Chatbots provide on-the-spot responses to HCPs, improving first-contact resolution by 25%. [14][15][16]

Value Delivered:

• Early adopter pharma companies reduced the average inquiry handling time by 35 percent.[17][18][19]
• MI groups globally deliver productivity improvements like gaining 1.4 Full Time Employees allocated to an additional 10 agents.[20][21]

Lingering Pain Points:

• Hallucinations & Compliance Risk: Large Language Models (LLMs) may invent fake references, where human verification still is required.
• Manual Handoffs: You are still making staff click through Case Selection, Classification, routing, and Updates.
• Static Learning: Generative AI gets better with more training, but with no real-time feedback from results.

Why Agentic AI?

Agentic AI is a set of independent software “agents” that work together to perceive data, reason, plan, act through APIs, and learn from experience, all with minimal human oversight.

Comparative Analysis of Generative AI and Agentic AI