As generative AI matures in life sciences, many organisations struggle to scale beyond pilot programmes. Christopher Wooden, Sr. Director, Global Market Insights, and Charles Rink, Sr. Principal, Information Management & Analytics Technology at IQVIA, explore how agentic AI offers a path to enterprise value. They examine the challenges of adoption, the criteria for successful implementation and the strategic opportunities for commercial leaders looking to integrate intelligent systems into daily workflows for measurable business outcomes.
The use of Artificial Intelligence (AI) in life sciences has reached an inflection point. While Generative AI captured the initial attention of companies with impressive demonstrations and pilot programmes, the industry now confronts a sobering reality: More than 70% of Generative AI experiments fail to scale and deliver meaningful business outcomes. This failure represents more of a challenge to find appropriate ways to strategically implement these programmes than a lack of technological capabilities.
The path forward does not mean abandoning these initiatives but instead means we must evolve basic generative models. Using agentic systems that can independently create content, make decisions, take actions and learn contextually would be more beneficial. For commercial leaders, this evolution represents both the greatest opportunity and the most complex scaling challenge of the digital era.
The agentic advantage: Moving beyond generative to action
While generative models focus on creating humanlike output, agentic systems take the next critical step: autonomous action. These platforms do not just analyse data or generate reports; they execute tasks, apply reasoning and work semi-autonomously toward defined goals within human workflows.
This distinction matters enormously in life sciences, where companies wrestle with growing volumes of unstructured data, which includes regulatory reports, medical literature, market research and field interactions. Traditional analytics struggle to keep pace, and even sophisticated generative models prove limited without the ability to provide actionable insights.
The timing could not be more strategic. Life sciences companies have vast data repositories with enormous potential value that remain largely inaccessible. Consider this: A single enterprise may accumulate 50 million marketing messages from healthcare provider (HCP) interactions across 36 countries over 12 years, spanning 20 or more languages. Before large language models (LLMs) existed, extracting actionable intelligence from this trove of data required prohibitive time and investment.
The strategic selection framework: Three critical questions
Industry leaders who successfully scale agentic AI have discovered that rigorous use-case evaluation separates transformative deployments from costly pilot programmes. These organisations consistently apply a proven three-question framework before committing resources:
- Is it valuable? Successful implementations solve immediate pain points instead of theoretical problems. They target areas where existing workflows create bottlenecks or where data overload actively slows outcomes. The most promising applications address processes that consume significant human hours: clinical research associates checking trial data, financial processing workflows or regulatory literature reviews that tie up specialised teams for weeks.
- Does it outperform existing methods? Not every task benefits from advanced technology. Simple numerical analysis often remains better handled through traditional programming. Viable applications demonstrate that agentic systems offer faster outcomes, higher-quality insights or fundamentally new capabilities that were not previously possible. The technology should represent a clear competitive advantage, not just a different approach.
- Can users adopt it easily? Even powerful tools fail without seamless workflow integration. Users — whether they are safety scientists, medical science liaisons or marketing teams — must be able to engage naturally with outputs and trust the results. Adoption accelerates when solutions save users time, like reducing call preparation from hours to minutes.
When applications satisfy all three of these criteria, they tend to demonstrate significantly higher scaling success rates and generate measurable returns on investment (ROIs).
Proven applications delivering enterprise value
We have already seen evidence of the transformative potential of scaled deployments.
Here are just a few:
Pre-call briefing assistants represent one of the most successful commercial implementations, with some deployments spanning over 20 countries. These platforms automatically synthesise clinical trial data, physician interaction histories and product information into personalised briefs that enable field reps to be fully prepared to handle conversations with HCPs. However, industry experience reveals that success depends heavily on data availability. For example, user engagement can quickly diminish after initial excitement when customer relationship management (CRM) platforms lack access to sufficient customer information or fail to surface it effectively.
Message recall analysis demonstrates the power of intelligent automation to reveal previously inaccessible insights. One implementation processes millions of HCP verbatim responses across multiple languages and geographies to identify which marketing messages truly resonate versus those that miss the mark. Instead of spending months manually coding responses, commercial teams can now identify critical trends and optimise messaging strategies in real time. This capability proves especially valuable given that sales forces represent the preponderance of pharmaceutical companies’ promotional costs.
Pharmacovigilance automation showcases the ability of intelligent systems to handle complex regulated processes. These platforms accelerate the processing of adverse event reports through automated triage, duplicate detection and initial narrative drafting while maintaining full regulatory compliance. At scale, these implementations process over 1 million CRM records, 4 million digital records and 100,000 emails and PDFs annually while supporting more than 180,000 virtual assistant interactions. The result: Faster reporting timelines, improved patient safety and the ability of human experts to focus on the highest-risk cases.
In qualitative research synthesis, intelligent agents now process interview transcripts and survey responses four times faster than traditional methods, which transforms unstructured feedback into actionable insights. These systems support hundreds of users who analyse vast volumes of text to identify sentiment, key themes and emerging patterns, which previously required weeks of manual coding.
Each success story shares common characteristics, including high manual effort requirements, structured or semi-structured data availability and existing regulatory frameworks adaptable to automated outputs.
Navigating the scaling challenge
Converting pilots into enterprise solutions requires addressing both technical and human factors that often catch companies unprepared.
Technical complexity proves greater than anticipated. Successful workflows typically require more than 20 integrated tools working in concert, mixing intelligent agents, traditional robotic process automation and custom platform development. Companies quickly figure out that some process steps need advanced understanding while others require simple deterministic data movement. The key lies in controlling platforms where possible, enabling stable application programming interface (API) integrations and predictable performance.
Change management presents unexpected challenges. Expert users often resist automated tools most strongly. They have spent careers optimising existing approaches and maintain low tolerance for limitations or errors. Meanwhile, these experts tend to gravitate toward automating the intellectually stimulating aspects of their work, such as complex analysis or creative problem-solving, rather than repetitive administrative tasks, potentially eliminating sources of job satisfaction without generating proportional time savings.
Remodelling standard approaches
The intelligent automation market is evolving rapidly from custom solutions toward modular integrated offerings. Industry leaders predict this technology will become embedded across platforms rather than remain as a standalone software. Users will increasingly buy, rent or configure agents for specific needs instead of building everything from scratch.
This evolution carries profound implications for commercial operations. As intelligent systems become more sophisticated and trustworthy, traditional sales interactions will fully accommodate automated, multichannel touchpoints that support sales representatives’ crucial face-to-face work.
Strategic imperatives for commercial leaders
For commercial leaders ready to move beyond experimentation, the opportunity lies in identifying high-impact applications that satisfy the previously discussed three-question framework, building internal capabilities for the modular ecosystem and establishing partnerships that accelerate rather than complicate deployment.
The future belongs to companies that strategically augment human capabilities with intelligent assistance rather than replacing human expertise with automation. In the hands of empowered marketing, field representatives and analysts, advanced systems become a force multiplier that enables faster insights and smarter decisions.
