Life sciences companies have spent years building out their commercial analytics capabilities. As a result, dashboards look better, data volumes keep growing and reports land faster. So why do so many commercial teams continue to feel as if they are working harder than they should to turn information into action?

The issue is usually not the data. More often, it is the time between what the data reveals and when a team needs to make a call. That distance is starting to shorten with agentic AI, which can reason through layered business questions instead of returning a static report.¹

The agentic model comes at a time when industry is rethinking how digital technologies are evaluated and approved. Current research points to a landscape uniting behind evidence-first and integration-first approaches to technology integration, with stakeholders.² Commercial analytics is no exception to this trend. For most teams, the conversation has moved past whether AI belongs in the picture and toward something more pressing: how to get from a promising proof of concept to something that works at scale.³

A novel user experience

SaaS-based analytics tools, the standard of commercial ecosystems, perform very specific tasks with a high degree of predictability. They have clearly defined menus, filters and outputs. A user picks a territory, clicks a few buttons and gets a report. Everyone knows what to expect, which is both the appeal and the constraint.

Agentic AI works differently. Instead of clicking through a fixed interface, users are essentially having a conversation with the software.¹ A brand manager can type a question in plain language, and the system pulls from integrated data sources and domain knowledge to build a response that fits the context. If one were to ask about patient adherence for a specific drug over the last 90 days, the agent would find the right data and run the calculation without the user needing to know which database it lives in.

This changes the expectations that people expect from analytics. Users assume the software understands the business and which data sources talk to one another and that it can follow the thread of a question without being told every step.^1,3 When someone asks why new patient starts are falling, they want the system to consider competitive moves, barriers to access and prescribing shifts together, not just respond back with a trend line.

Why getting to production is harder than it looks

Setting up an agentic proof of concept can be fairly easy. A team equipped with the right tools can have a capable model ready in weeks.³ But then comes the hard part: getting these same capabilities to perform reliably at scale when hundreds of different users engage with the system.

People ask unexpected questions and expect the system to handle ambiguity like a trusted colleague. Domain expertise, data governance, compliance and change management suddenly matter far more than the model itself.³ A large language model (LLM) can get things started, but staying power requires expert data integration, model training and user familiarity. This involves structuring data, educating the system on industry terminology and building operational feedback loops that allow the system to improve over time.⁴

Redesigning the workflow, not just the tool

Many people are treating agentic AI as a SaaS tool of days past, instinctively dropping an AI agent into an existing process and then waiting for the efficiency gains to appear. But the teams getting the most out of this technology are doing something more ambitious: They are conceptualizing agentic as a paradigm shift, rethinking broader systems and workflows as this new modality comes to bear.³

Think about how field analytics traditionally works. A sales representative plans the week around a preset territory alignment, skims a dashboard before hitting the road and manually enters notes after a customer call. An agentic system can recalibrate this routine to increase efficiency and provide critical knowledge support. It can learn that a specific rep visits certain hospitals on Tuesday mornings, pull together prep materials for the physicians on that schedule, flag alternative visitations if a doctor is out and pull crucial competitive context at a moment’s notice.¹

At this point, adaptive personalization — the ability for a technology to learn a specific user’s core needs and change itself to better serve those needs — becomes tangible instead of conceptual. Every rep works a little differently and has their own habits. A well-built agent picks up on those patterns and, over time, shifts from reacting to questions to anticipating them and providing relevant, contextual information.

The same logic applies to the brand strategy side of commercial. Instead of kicking off a multiweek deep dive every time new-patient starts decrease, a brand team can ask their LLM a direct question. The system will then provide a working thesis built from a robust range of validated data streams, including prescribing data, competitive activity and payer dynamics. Critically, the agent is not doing the thinking for the team. Instead, the system handles the assembly work so the team can have strategic conversations and align on key decisions sooner.⁵

Getting the foundation right

None of this holds together without the right groundwork, and three things matter most: data quality, domain expertise and structured human oversight. They are not independent levers. They build on each other.³

Data quality goes beyond clean records. It means making sure metadata is in place so agents can make sense of what they are looking at and design data architecture for accessibility rather than duplication across disconnected systems.⁵ A user should be able to ask a question that spans claims data, call notes and competitive intelligence without knowing which silo holds which piece of information.

Domain expertise matters just as much. Agents need to learn the industry’s vocabulary, not just general language. In pharma, context is everything. The same acronym can carry a completely different meaning depending on the therapeutic area. Organizations that bring subject matter experts into the training process early help the model absorb real-world nuance and end up with a system that does more than generate answers. It generates the right ones.⁴

Then, there is the human element. Oversight should not be a phase that eventually gets switched off. It should be wired into the design from its inception. This includes the system knowing clear handoff points and when to route the conversation to a person,⁴ plus ways for users to flag hits and misses so the process continues to get sharper.³

Where we will find value

The way commercial leaders think about the return on agentic AI is evolving. The first wave of value is tangible: faster development cycles, fewer analysts needed for routine work and quicker turnaround on requests that used to take weeks. Those wins are real, and for many teams, they represent the clearest near-term proof point.³

The bigger prize is turning analytics into something that actively shapes commercial decisions rather than documenting them after the fact. When an agentic system can synthesize prescribing data, competitive moves and payer dynamics into a coherent thesis for leadership review, the role of analytics changes. It stops being a support function and starts operating as connective tissue between brand strategy, field execution and market performance.

References

1. Roy A. Exploring the value of agentic AI in life sciences. IQVIA Blog. 2026 Jan 28.
2. IQVIA Institute for Human Data Science. Digital health trends 2025: business models, evidence requirements, and revenue opportunities. IQVIA Institute Report. 2025 Dec 11.
3. Roy A. Smarter, faster, bolder: AI’s new role in pharma strategy. Dedicated Dialogue. Pharmaceutical Commerce. 2025 Sep 4.
4. Roy A, Rink C Sr. Where to deploy generative AI in life sciences: key strategies and insights. IQVIA Blog. 2025 Aug 5.
5. Chaddha K, Jaiswal A. Agentic AI and data accessibility: a strategic shift for life sciences across the product lifecycle. IQVIA Blog. 2025 Oct 2.

About the authors

Rohit Vashisht leads products and platforms for Pharma Commercial Solutions at IQVIA, driving innovation at the intersection of AI, data and life sciences. A seasoned tech entrepreneur, he is the co‑founder of WhizAI, a generative AI analytics platform for life sciences and healthcare that was recognized by Inc. as one of America’s 500 fastest‑growing private companies and acquired by IQVIA in 2025. Previously, Rohit co‑founded and served as CEO of Sverve — later acquired by Bloglovin and rebranded as Activate — where he led product, sales and marketing to scale a leading influencer marketing platform. With over 20 years of experience across product management, engineering, sales and strategy, Rohit combines deep enterprise software expertise with a visionary mindset. He holds an MBA from NYU Stern and an engineering degree from IIT Delhi.

Stephanie Zinda is the global offering lead for Patient and Brand Analytics at IQVIA, where she leads analytics and AI offering design and go‑to‑market strategy. She partners with life sciences companies to embed predictive analytics, machine learning and agentic AI into commercial and marketing workflows at scale, delivering measurable, real‑world impact. With 13 years at IQVIA across sales, offering management and delivery leadership roles, Stephanie helped build the firm’s AI solutions practice and is a trusted advisor to life sciences leaders seeking to translate advanced analytics into better brand decisions and outcomes. She holds an MBA/MPH from UC Berkeley and a BA in biology from the University of Chicago.