Agentic AI for Intelligent Pharmaceutical Market Access Analytics Platforms

Abstract

Pharmaceutical market access decision making is becoming more complex as pricing models evolve, payer requirements diversify, and reimbursement policies shift more rapidly. These pressures expose fundamental limitations in many traditional analytics environments—fragmented data foundations, static models, and workflows that struggle to adapt to new evidence and market signals. 
This paper explores the application of agentic artificial intelligence (AI) within pharmaceutical market access analytics and proposes a decision intelligence framework that integrates autonomous, goal directed AI agents with large scale real world data (RWD) and healthcare claims data. The proposed approach enables continuous evidence discovery, contextual reasoning, and adaptive decision support across market access activities such as payer strategy development, formulary positioning, and reimbursement negotiation planning. 
Using a conceptual and literature driven synthesis of research across AI systems, healthcare analytics, and market access strategy, the study introduces a multi layer platform architecture that connects data integration, agent based intelligence, decision orchestration, and stakeholder ready interfaces. The analysis suggests that agentic AI can improve the agility and usability of market access analytics by automating complex interpretation workflows, generating timely insights, and supporting continuous learning through feedback loops—shifting market access analytics from static reporting toward adaptive, enterprise ready decision intelligence.