Agentic AI in healthcare is moving from responding to prompts to autonomously executing complex marketing tasks – and life sciences companies are banking on this in their business strategies.
According to a recent report quoted According to Capgemini Invent, AI agents could generate up to $450 billion in economic value through increased revenue and cost savings globally by 2028, with 69% of executives planning to deploy agents in marketing processes by the end of the year.
The stakes are particularly high in pharmaceutical marketing, where salespeople have increasingly limited contact with healthcare professionals – a trend accelerated by Covid-19. The challenge is not just access; it’s about making those rare interactions count through the intelligence that is currently trapped in data silos.
The problem of fragmented intelligence
Briggs Davidson, senior director of digital strategy, data and marketing for life sciences at Capgemini Invent, main lines a scenario that will sound familiar to all pharmaceutical marketing professionals: a healthcare professional attends a conference where a competitor presents promising results on a drug, publishes research, and changes their prescriptions to a competing product, all in a single quarter.
“In most companies, existing IT infrastructure and data silos maintain this information in disparate CRM, event database, and claims data systems,” Davidson writes. “Chances are none of this information was available to the sales reps prior to their meeting with the healthcare professional.”
The solution, according to Davidson, is not to connect these systems, but to deploy agentic AI in healthcare marketing to autonomously query, synthesize and act on unified data. Unlike conversational AI that responds to queries, agent systems can independently execute multi-step tasks.
Instead of a data engineer building a new pipeline, an AI agent could autonomously query the CRM and claims database to answer business questions such as: “Identify oncologists in the Northwest who have 20% lower prescription volume but who attended our last medical conference. »
From orchestration to autonomous execution
Davidson sees the change as moving from “omnichannel vision” – coordinating experiences across channels – to true orchestration powered by agentic AI.
In practice, this means that a sales representative can ask an agent to help schedule calls and visits by asking, “What messages has my healthcare professional responded to most recently?” » or “Can you create a detailed information file for my healthcare professional?”
The agent system would compile:
- Their most recent conversation with the HCP,
- The prescribing behavior of the health professional,
- The opinion leaders that the HCP follows,
- Relevant content to share,
- The awareness channels favored by the healthcare professional (in-person visits, emails, webinars).
More importantly, the AI agent would then create a personalized call plan for each healthcare professional based on their unified profile and recommend follow-up steps based on the engagement results. “Agentic AI systems aim to drive action, moving from ‘respond to my prompt’ to ‘autonomously execute my task,’” says Davidson.
“This means shifting the mindset of sales reps from asking questions to coordinating small teams of specialist agents who work together: one plans, another retrieves and verifies content, a third plans and measures, and a fourth enforces compliance guardrails – all under human oversight.
The prerequisite for AI-ready data
The operational promise is based on what Davidson calls “AI-ready data”: standardized, accessible, complete and reliable information that enables three capabilities:
Faster decision making: Predictive analytics that provide near real-time alerts on what’s about to happen, enabling sales reps to act proactively.
Large-scale customization: Deliver personalized experiences to thousands of healthcare professionals simultaneously with small human teams enabled by networks of specialized agents.
Real return on marketing investment: Go beyond monthly historical reporting to understand which marketing activities are actively driving prescriptions.
Davidson emphasizes that a successful deployment begins with aligning marketing and IT on the initial use cases, with stakeholders identifying KPIs that demonstrate tangible results, such as specific percentage increases in healthcare professional engagement or sales rep productivity.
Critical Implementation Questions
The article presents agentic AI in healthcare as “not just another technological capability; it’s a new operational layer for sales teams. » But he recognizes that “the full value of agentic AI only materializes with AI-ready data, reliable deployment, and workflow redesign.”
What remains unresolved is the regulatory and compliance complexity of autonomous systems querying claims databases containing prescriber behavior, particularly to HIPAA’s minimum necessary standard. The article also does not detail actual customer implementations or metrics beyond the ambitious $450 billion economic value projection.
For global organizations, Davidson says use cases “can and should be tailored to the maturity of each market for maximum ROI,” suggesting deployment will vary depending on regulatory environments. The core value proposition, according to Davidson, focuses on two-way benefits: “The HCP receives directly relevant content, and marketing teams can drive increased engagement and conversion from the HCP.
Whether this vision of autonomous marketers coordinating CRM, events and claims systems becomes common practice by 2028 – or remains constrained by the realities of data governance – will likely determine whether life sciences achieves anything close to this $450 billion opportunity.
See also: Chinese hyperscalers bet billions on agentic AI as commerce becomes the new battleground
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