Agentic AI Revolutionizes Healthcare Marketing Strategy
Life sciences companies are leveraging artificial intelligence (AI) to enhance their commercial strategies, marking a shift from answering prompts to autonomously executing complex marketing tasks. This transformation can be largely attributed to the emergence of agentic AI in healthcare.
A recent report cited by Capgemini Invent predicts that AI agents could generate up to $450 billion in economic value globally by 2028 through revenue uplift and cost savings. Furthermore, approximately 69% executives are set to deploy these agents in their marketing processes by the end of this year. The source of this information can be found here.
The Power of Agentic AI in Pharmaceutical Marketing
In the realm of pharmaceutical marketing, face-to-face interactions between sales representatives and healthcare professionals (HCPs) have become increasingly rare, particularly due to the Covid-19 pandemic. This makes it essential to optimise these limited interactions by intelligently leveraging data currently trapped in data silos.
The Issue of Fragmented Intelligence
Briggs Davidson, Senior Director of Digital, Data & Marketing Strategy for Life Sciences at Capgemini Invent, highlights a common scenario in pharma marketing. A HCP might attend a conference where a competitor presents promising drug results and publishes research, leading to a shift in their prescriptions to a rival product within a single quarter.
“Legacy IT infrastructure and data silos often store this information in disparate systems in CRM, events databases and claims data. This means the information was probably inaccessible to sales reps before meeting with the HCP,” Davidson explains.
According to Davidson, the solution lies in deploying agentic AI in healthcare marketing to autonomously query, synthesize, and act on unified data, rather than simply connecting these systems. Agentic AI systems can independently execute multi-step tasks, unlike conversational AI that merely responds to queries.
Transitioning from Orchestration to Autonomous Execution
Davidson describes the shift from an “omnichannel view” (coordinating experiences in channels) to true orchestration powered by agentic AI. For instance, a sales representative could have an AI agent assist with call and visit planning by asking questions like “What messages has my HCP responded to most recently?” or “Can you create a detailed intelligence brief on my HCP?”
The agentic AI system could compile recent conversations with the HCP, the HCP’s prescribing behavior, thought-leaders the HCP follows, relevant content to share, and the HCP’s preferred outreach channels. This system could then create a custom call plan for each HCP based on their unified profile and recommend follow-up steps based on engagement outcomes.
AI-Ready Data: A Prerequisite for Success
The success of deploying agentic AI relies on what Davidson refers to as “AI-ready data”, which is standardized, accessible, complete, and trustworthy. This information allows for faster decision-making through predictive analytics, personalization at scale, and accurate measurement of marketing ROI.
Davidson emphasizes that successful deployment begins with aligning marketing and IT on initial use cases and identifying KPIs that demonstrate tangible outcomes, such as specific percentage increases in HCP engagement or sales representative productivity.
Considerations for Implementing Agentic AI in Healthcare
Agentic AI in healthcare is not merely another technological capability, but a new operating layer for commercial teams. However, its full potential can only be realized with AI-ready data, trustworthy deployment, and workflow redesign.
Unaddressed issues include the regulatory and compliance complexities of autonomous systems querying claims databases containing prescriber behavior, particularly under HIPAA’s minimum necessary standard. Further, the article does not provide details on actual client implementations or metrics beyond the projected $450 billion economic value.
Davidson suggests that use cases should be tailored to fit each market’s maturity for maximum ROI, indicating that deployment will vary in different regulatory environments. The fundamental value proposition, according to Davidson, revolves around bidirectional benefit: “The HCP receives directly relevant content, and the marketing teams can drive increased HCP engagement and conversion.”
Whether this vision of autonomous marketing agents coordinating in CRM, events, and claims systems becomes standard practice by 2028, or remains constrained by data governance realities, will likely determine if life sciences achieves anything close to that $450 billion opportunity.
Global Adoption of Agentic AI
It’s not just healthcare industries that are betting on agentic AI. China’s hyperscalers are also investing billions in agentic AI as commerce becomes the new battleground.
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