Revolutionizing Life Sciences Marketing: Unleashing $450 Billion in Value by 2028 with Agentic AI in Healthcare

Artificial intelligence (AI) in the healthcare sector is evolving rapidly, transitioning from simply responding to prompts to autonomously carrying out complex marketing tasks. Life sciences companies are placing their bets on this technology, shaping their commercial strategies around it.

A recent report cited by Capgemini Invent suggests that AI agents could potentially generate up to US$450 billion in economic value globally by 2028 through revenue uplift and cost savings. It is anticipated that 69% of executives will deploy AI agents in marketing processes by the end of the year.

The pharmaceutical marketing industry faces significant challenges, especially with sales representatives having limited face-to-face interactions with healthcare professionals (HCPs) – a situation exacerbated by the Covid-19 pandemic. The key is not just gaining access but ensuring that these interactions are meaningful with the help of intelligence that is currently locked within data silos.

The Issue of Fragmented Intelligence

In the context of pharmaceutical marketing, Briggs Davidson from Capgemini Invent highlights a common scenario where an HCP attends a conference, learns about a competitor’s new drug, and subsequently shifts their prescriptions – all within a short period. However, this valuable information is often spread across different systems such as CRM, events databases, and claims data, making it inaccessible to sales representatives before meeting with the HCP.

Davidson proposes that the solution lies in deploying agentic AI in healthcare marketing to autonomously gather, synthesize, and act on integrated data. Unlike conversational AI, which responds to queries, agentic systems can execute multi-step tasks independently.

Instead of relying on a data engineer to create a new pipeline, an AI agent could autonomously extract information from the CRM and claims database to address business questions like identifying oncologists in a specific region with lower prescription volumes who attended a recent medical congress.

Transitioning from Coordination to Autonomous Action

Davidson emphasizes the shift from an “omnichannel view” – coordinating experiences across channels – to true orchestration powered by agentic AI. In practical terms, a sales representative could seek assistance from an AI agent in planning calls and visits by asking for insights on the HCP’s recent responses or requesting a detailed intelligence brief.

The AI agent would compile various details, including the latest conversation with the HCP, prescribing patterns, thought leaders followed by the HCP, relevant content, and preferred communication channels. Subsequently, the agent would create a customized call plan for each HCP based on their profile and suggest follow-up actions based on engagement outcomes.

According to Davidson, agentic AI systems are designed to drive action, moving beyond merely answering prompts to autonomously executing tasks. This entails a shift in the sales representative mindset towards coordinating specialized agents that collaborate on different aspects of the marketing process under human supervision.

The Prerequisite of AI-Ready Data

Davidson underscores the importance of “AI-ready data” – standardized, accessible, complete, and reliable information that enables faster decision-making, personalized experiences at scale, and a deeper understanding of marketing ROI. Successful deployment begins with aligning marketing and IT teams on initial use cases and setting clear KPIs to demonstrate tangible outcomes.

Key Implementation Considerations

The article positions agentic AI in healthcare as a transformative technology that acts as a new operational layer for commercial teams. However, it acknowledges that realizing the full potential of agentic AI requires AI-ready data, reliable deployment, and workflow redesign.

While the piece does not delve into the regulatory and compliance challenges related to autonomous systems querying sensitive databases, it underscores the need for tailored use cases based on each market’s maturity. The core proposition revolves around mutual benefits for HCPs and marketing teams, with the aim of enhancing engagement and conversion rates.

Whether the vision of autonomous marketing agents coordinating across various systems becomes standard practice by 2028 or faces constraints due to data governance issues will determine the industry’s ability to tap into the projected US$450 billion opportunity.

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