Agentic AI in healthcare is graduating from answering prompts to autonomously executing complex marketing tasks—and life sciences companies are betting their commercial strategies on it.
According to a recent report cited by Capgemini Invent, AI agents could generate up to US$450 billion in economic value through revenue uplift and cost savings globally by 2028, with 69% of executives planning to deploy agents in marketing processes by year’s end.
The stakes are particularly high in pharmaceutical marketing, where sales representatives have increasingly limited face time with healthcare professionals (HCPs)—a trend accelerated by Covid-19. The challenge isn’t just access; it’s making those rare interactions count with intelligence that’s currently trapped in data silos.
The fragmented intelligence problem
Briggs Davidson, Senior Director of Digital, Data & Marketing Strategy for Life Sciences at Capgemini Invent, outlines a scenario that will sound familiar to anyone in pharma marketing: An HCP attends a conference where a competitor showcases promising drug results, publishes research, and shifts their prescriptions to a rival product—all within a single quarter.
“In most companies, legacy IT infrastructure and data silos keep this information in disparate systems across CRM, events databases and claims data,” Davidson writes. “Chances are, none of that information was accessible to sales reps before they met with the HCP.”
The solution, according to Davidson, isn’t just connecting these systems—it’s deploying agentic AI in healthcare marketing to autonomously query, synthesise, and act on that unified data. Unlike conversational AI that responds to queries, agentic 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 like: “Identify oncologists in the Northwest who have a 20% lower prescription volume but attended our last medical congress.”
From orchestration to autonomous execution
Davidson frames the shift as moving from an “omnichannel view”—coordinating experiences across channels—to true orchestration powered by agentic AI.
In practice, this means a sales representative could have an agent assist with call and visit planning by asking: “What messages has my HCP responded to most recently?” or “Can you create a detailed intelligence brief on my HCP?”
The agentic system would compile:
- Their most recent conversation with the HCP
- The HCP’s prescribing behaviour
- Thought leaders the HCP follows
- Relevant content to share
- The HCP’s preferred outreach channels (in-person visits, emails, webinars)
More significantly, the AI agent would then create a custom call plan for each HCP based on their unified profile and recommend follow-up steps based on engagement outcomes.
“Agentic AI systems are about driving action, graduating from ‘answer my prompt,’ to ‘autonomously execute my task,’” Davidson explains.
“That means evolving the sales representative mindset from asking questions to coordinating small teams of specialised agents that work together: one plans, another retrieves and checks content, a third schedules and measures, and a fourth enforces compliance guardrails—all under human oversight.”
The AI-ready data prerequisite
The operational promise hinges on what Davidson calls “AI-ready data”—standardised, accessible, complete, and trustworthy information that enables three capabilities:
Faster decision making: Predictive analytics that provide near real-time alerts on what’s about to happen, enabling sales representatives to act proactively.
Personalisation at scale: Delivering customised experiences to thousands of HCPs simultaneously with small human teams enabled by specialised agent networks.
True marketing ROI: Moving beyond monthly historical reports to understanding which marketing activities are actively driving prescriptions.
Davidson emphasises that successful deployment starts with marketing and IT alignment on initial use cases, with stakeholders identifying KPIs that demonstrate tangible outcomes—such as specific percentage increases in HCP engagement or sales representative productivity.
Critical implementation questions
The article notably frames agentic AI in healthcare as “not simply another technology-led capability; it’s a new operating layer for commercial teams.” But it acknowledges that “agentic AI’s full value only materialises with AI-ready data, trustworthy deployment and workflow redesign.”
What remains unaddressed: the regulatory and compliance complexity of autonomous systems querying claims databases containing prescriber behaviour, particularly under HIPAA’s minimum necessary standard. The piece also doesn’t detail actual client implementations or metrics beyond the aspirational US$450B economic value projection.
For global organisations, Davidson notes that use cases “can and should be tailored to fit each market’s maturity for maximum ROI,” suggesting that deployment will vary significantly across regulatory environments.
The fundamental value proposition, according to Davidson, centres on bidirectional benefit: “The HCP receives directly relevant content, and the marketing teams can drive increased HCP engagement and conversion.”
Whether that vision of autonomous marketing agents coordinating across 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 US$450 billion opportunity.
See also: China’s hyperscalers bet billions on agentic AI as commerce becomes the new battleground
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