Best Use Cases for Scaling Agentic AI in Commercial Life Sciences
The Rise of Generative AI in Life Sciences
Generative AI (GenAI) has captivated industries worldwide, revealing its potential for transforming operations—from document summarization to interactive chatbots. However, as many organizations have discovered, the journey to scaling these AI solutions across their enterprises is fraught with challenges. As outlined in industry research, over 70% of GenAI experiments fail to scale, leaving untapped potential on the table.
So, how can you ensure your AI initiatives become success stories? In the life sciences sector, the key lies in pinpointing the most impactful use cases that harness the extraordinary capabilities of agentic AI.
What Is Agentic AI—and Why Is It Relevant Now?
While GenAI focuses on producing human-like outputs, agentic AI takes it a step further. Agentic systems can autonomously generate content, make informed decisions, and take targeted actions toward specific objectives. These agents function optimally when rooted in domain-specific knowledge and guided by reliable data, seamlessly integrating into existing human workflows.
In today’s data-rich life sciences environment, the demand for such innovation is more pressing than ever. Traditional analytics struggle to keep up with the sheer volume of unstructured data—spanning regulatory documents, market studies, and clinical notes. The need for effective decision-making in this landscape has never been greater, and this is where agentic AI shines.
Unraveling Potential Use Cases for GenAI and Agentic AI in Life Sciences
While initial excitement around GenAI set high expectations across various business domains, life sciences organizations have learned that not every task suits LLMs or autonomous agents. The most impactful successes typically emerge from use cases where:
- Large volumes of structured or semi-structured data are involved.
- The cost of manual processes is significant.
- Speed and consistency are critical alongside creativity.
- Existing regulatory frameworks can accommodate AI-driven outputs.
Notably, GenAI and agentic AI have already transformed specific areas within life sciences, particularly in pharmacovigilance (PV) and safety operations. They’ve also shown effectiveness in:
- Commercial field enablement.
- Qualitative market research analysis.
- Syndicated data exploration and reporting.
- Post-call CRM analysis for optimizing messaging strategies.
Every successful deployment hinges on one vital factor: a synergistic alignment between the right AI capabilities and a genuine business need.
Identifying Optimal Areas for Scaling GenAI and Agentic AI
Knowing where to implement GenAI and agentic AI is crucial for effective scaling. It isn’t about spreading AI everywhere—it’s about utilizing it judiciously. Scaling tends to flourish under these conducive conditions:
- Repetitive but complex tasks: AI excels when there’s a structured environment with enough complexity that traditional automation falters.
- Auditable outputs: In regulated settings, ensuring that AI-generated results can be tracked and validated is paramount.
- Immediate user benefits: Adoption rates skyrocket when an AI tool can significantly enhance workflow—think of an AI assistant that saves reps 30 minutes before each call.
When these factors align, GenAI and agentic AI transition from being “nice to have” to becoming business essentials.
Evaluating Viable Use Cases for GenAI and Agentic AI
Before diving into a GenAI implementation, teams should rigorously assess potential use cases through three pivotal questions:
Is it valuable?
Does this use case tackle a genuine pain point or bottleneck in existing workflows, rather than serving a theoretical demand?Is it superior to existing methods?
Not all tasks benefit from GenAI. Good use cases should provide faster outcomes, better insights, or capabilities that traditional approaches cannot achieve.- Is it user-friendly?
Even the most powerful AI tools will falter if they don’t integrate seamlessly into user workflows. Consider how easily end users—from safety scientists to marketing teams—can engage with the AI and trust its outputs.
When a use case meets all three criteria, it’s vastly more likely to scale effectively, delivering substantial real-world ROI.
Success Stories: Scaled Wins for GenAI and Agentic AI
AI-Powered Pre-Call Briefing Assistants
In the realm of field operations, preparation is critical. However, the demands placed on representatives and medical science liaisons (MSLs) for pre-call briefings can be overwhelming.
Agentic AI has transformed this process by powering pre-call briefing assistants that automatically gather essential information—such as clinical trial data, historical physician interactions, and comprehensive product details. This synthesis helps field teams enter conversations fully prepared, enhancing both the quality of interactions and operational efficiency.
| Is it valuable? | Call preparation directly impacts call effectiveness; large sales teams utilize this multiple times daily. |
|---|---|
| Is it better than older methods? | Effectively leverages existing customer and product data to enhance relevance. |
| Is it easy to adopt? | Integrates seamlessly via a CRM interface or mobile app. |
Unlocking the Value of Marketing Messages
In life sciences, marketing teams produce a vast array of nuanced messages across brands and channels, but which ones hit home?
AI agents can scrutinize CRM records, email exchanges, call notes, and market research to land on the most impactful messages. This new approach allows commercial teams to refine their strategies and tailor future messaging based on real-world feedback—elevating optimization efforts beyond mere guesswork.
| Is it valuable? | Marketing and brand teams need to assess message impact to enhance conversion rates. |
|---|---|
| Is it better than older methods? | Traditional analyses are often outdated by the time insights are gathered. |
| Is it easy to adopt? | Offered through the IQVIA AI Assistant, integrated into existing analytics platforms. |
Scale AI with Confidence: Partnering with IQVIA
As organizations sharpen their focus on identifying valuable AI applications, the success of scaling GenAI and agentic AI will undoubtedly rise. Rather than pursuing fleeting trends, teams are beginning to prioritize enduring, business-centric use cases.
Here at IQVIA, we seamlessly blend domain expertise, proprietary data assets, and AI leadership to craft agentic AI solutions that yield measurable results. We believe the future is about strengthening, not replacing, human expertise. Empowering professionals in fields such as PV, sales, and analytics allows AI to serve as a trusted partner—facilitating faster insights, informed decisions, and superior patient outcomes.
Are you ready to shift from pilot projects to tangible AI value? Request a demo and discover how IQVIA can guide you in scaling AI with confidence!
References
- Deloitte. “State of Generative AI in the Enterprise.” 2025, Deloitte Report.
By embracing the promising potential of agentic AI, the life sciences sector stands on the brink of a transformation that will redefine operational efficiencies and elevate patient care. Join the movement today!
