The life sciences industry has witnessed remarkable technological advancements over the past three decades—from groundbreaking therapies to sophisticated marketing approaches. Yet beneath these innovations lies a paradox: The fundamental infrastructure powering our analytics ecosystems has remained largely unchanged. While end capabilities evolve, core data management principles continue to follow frameworks established when digital transformation was in its infancy. This stagnation has created several challenges facing modern biopharmaceutical organizations, including data use limitations.

The data utilization disconnect

Leading companies typically allocate millions of dollars annually to maintain and enhance commercial data and analytics ecosystems. Despite this substantial investment, incorporating new datasets or introducing novel key performance indicators (KPIs) remains extraordinarily time-consuming.

When a brand manager wants to integrate insights from market research into their performance dashboard, for instance, they face a timeline of two to five months of development before that single KPI becomes operational.¹

This implementation delay has a cascading effect throughout commercial operations. By the time insights materialize, market conditions may have evolved, diminishing the relevance of the enhancement. Decision-makers often resort to alternative approaches and use unconnected spreadsheets or separate data sources outside the governed systems. Doing so compromises data integrity and fosters inconsistent versions of the truth throughout the organization.

The issue is further complicated by what experts term the “data-rich, information-poor” syndrome, where valuable information is held in isolated warehouses, leading to poor knowledge management and, subsequently, data utilization. This technological challenge carries a significant opportunity cost for businesses.

An evolving intelligence landscape

Large language models (LLMs) have evolved rapidly, each iteration bringing increasingly sophisticated capabilities to life sciences data management. Early standard LLMs performed with the proficiency of an entry-level data analyst, primarily handling well-defined tasks with clear parameters. As reasoning capabilities emerged, these systems advanced to match senior data analyst competencies, conducting more nuanced data interpretation. Today’s deep-research-capable models operate with the strategic insight of a senior consultant, addressing complex business questions with minimal guidance.

This evolution has dramatically expanded the range of use cases that artificial intelligence (AI) can effectively address. More importantly, with each advancement, these systems have demonstrated an increasing ability to tackle the multifaceted challenges that previously required extensive human intervention across the data life cycle.

Agentic AI’s emerging potential

The rapid advances in model capabilities have set the stage for a fundamental shift in how AI functions within data ecosystems. This is where the concept of agentic AI emerges as a transformative force in pharmaceutical and healthcare analytics.

Unlike basic generative AI, which responds to specific prompts or executes predefined tasks, agentic AI combines multiple AI streams to operate with greater autonomy. This approach creates a multitiered intelligence ecosystem: Data agents access and prepare information from various sources; analytical agents apply algorithms and statistical methods to this prepared data; and orchestrating agents coordinate these activities to deliver coherent insights aligned with business objectives.

Rather than moving sequentially through distinct systems, this orchestrated approach enables direct connections with data sources, automatic processing and standardization, verification against business rules, and dynamic visualization creation—all with significantly less reliance on traditional development cycles.

Early exploratory work suggests this model can deliver considerable benefits, with initial proofs of concept indicating potential efficiency improvements of 50% to 70% in metric development processes.¹

While these results are preliminary, they suggest the possible impact agentic AI could have on data management in an information-driven life sciences environment.

Managing implementation

Despite these promising possibilities, biopharma companies should approach agentic AI exploration with circumspection. The concept represents a significant departure in how information might flow, and any successful implementation would require both technical preparedness and organizational alignment.

Leaders should understand that while the vision of highly autonomous data systems represents an ambitious objective, the journey would require intermediate milestones. A phased implementation approach should be considered:

1. Begin with defined proof-of-concept projects that solve a core problem for a single brand or target segment.
2. Establish transparent metrics for success, focusing on improvements in efficiency and quality.
3. Document insights and refine the process before expanding to broader applications.
4. Plan for appropriate human oversight, particularly for business-critical analyses.

This measured approach recognizes that while agentic AI shows transformative potential for data ecosystems, current implementations remain primarily conceptual.

By focusing on small-scale experiments, life sciences organizations can build and foster confidence in the approach while developing institutional knowledge that would support broader exploration.

Maintaining the human element

An important consideration when implementing sophisticated AI systems is their non-deterministic nature. Unlike traditional analytics tools that produce identical outputs given the same inputs, advanced language models may generate slightly different content when asked the same question multiple times.

While key facts and insights remain consistent, variations in presentation or emphasis might occur—such as how different consultants might approach the same business problem with unique perspectives. In regulated environments, this characteristic necessitates appropriate governance frameworks and human oversight to ensure consistency in critical decision-making contexts. Thus, it is important to maintain “human-in-the-loop” validation for sensitive or high-stakes analyses, with AI handling mechanical aspects while domain experts provide context.

For example, when crafting content for executive dashboards, agents might autonomously update metrics and generate preliminary visualizations but have a business analyst examine the outputs before distribution. This would ensure that anomalies receive appropriate investigation and insights align with business context that may not be fully captured in the data alone.

Beyond analytics: Future applications

While current conceptual frameworks primarily center on streamlining data workflows and improving insight generation, agentic AI could have other valuable applications, including:

• Custom insight narratives that adjust not only to the data but to the preferences and priorities of individual stakeholders.
• Recommendation systems that propose not just what occurred but which strategies might most effectively address emerging trends and challenges.
• Assistance with assembling briefing documents that compile relevant information before key meetings or decisions.
• Support with refreshing forecasts to incorporate new market signals with minimal manual intervention.²

Perhaps most significantly, these capabilities might democratize access to analytics. Rather than demanding specialized technical proficiency to create new insights, business users could interact with agents that comprehend their needs and convert requests into appropriate analyses—shifting from tool-centric to purpose-centric analytics.

Breaking the data stalemate

The 30-year stagnation in life sciences data management principles presents both a challenge and an opportunity. Organizations that successfully explore the development and integration of agentic AI could achieve incremental gains in efficiency and meaningful progress in how insights inform decision-making.

For executives examining this emerging area, here are four guiding principles to consider:

• Begin with business challenges instead of technology capabilities and identify specific decisions that would benefit from more responsive insights.
• Consider investments in foundational data governance that would allow future AI systems to access high-quality, well-documented information.
• Assemble cross-functional teams that combine technical expertise with domain knowledge and change-management capabilities.
• Establish appropriate review processes for outputs that require absolute consistency.

The life sciences industry is at a potential turning point in how data is managed and insights are generated. By approaching agentic AI concepts with strategic vision and operational discipline, it’s possible to move beyond decades-old limitations and create analytics ecosystems that enable better and improved data-driven decision-making in complex markets.

Tanveer Ahmed Nasir is Vice President and General Manager, Product Management, IQVIA; William O’Reilly is Director, Product Offering Development, IQVIA

References

1. Rink, C.; O’Reilly, W. Where to Deploy Generative AI Solutions in Life Sciences. IQVIA white paper. March 19, 2025. https://www.iqvia.com/library/white-papers/where-to-deploy-generative-ai-solutions-in-life-sciences

2. Nasir, T.A.; Haslam, T. Inside Agentic AI: Reshaping Decisions and Orchestration in Life Sciences. IQVIA blog. February 28, 2025. https://www.iqvia.com/blogs/2025/02/inside-agentic-ai-reshaping-decisions-and-orchestration-in-life-sciences.iqvia.com/library/white-papers/where-to-deploy-generative-ai-solutions-in-life-sciences