From fragmented to focused: Transforming field engagement with agentic AI

Despite billions invested in customer relationship management (CRM) systems and analytics platforms, pharmaceutical field teams still spend excessive time navigating fragmented systems, rather than engaging with healthcare professionals (HCPs). This operational inefficiency represents more than lost productivity. It can be a barrier to delivering the personalised, compliant, and scientifically rigorous engagement that today’s healthcare environment demands.

A new generation of digital agents powered by agentic artificial intelligence (AI) promises to bridge this gap. Unlike traditional automation tools that follow predetermined scripts, these intelligent systems can reason across complex data landscapes, anticipate the needs of field professionals, and orchestrate workflows that previously required hours of manual coordination. This can fundamentally reshape how field teams prepare for, conduct, and follow up on HCP interactions.

The hidden cost of disconnected systems

Today’s pharmaceutical field teams operate in an environment of growing complexity. Sales representatives juggle multiple systems daily, from customer relationship management (CRM) platforms and territory management tools to formulary databases and compliance-tracking systems. Medical science liaisons (MSLs) face even greater complexity. Along with their core engagement responsibilities, they’re tasked with navigating scientific databases, publication-tracking systems, congress planning tools, and other platforms to prepare for meetings with key opinion leaders (KOLs).

According to recent research, 41% of companies struggle with fragmented data across their organisations. This fragmentation directly impacts field force excellence and the ability to deliver coordinated customer engagement. Effective data organisation and governance become essential before scaling AI models, spanning everything from sales and HCP data to patient information, omnichannel engagement metrics, payer insights, third-party sources, market intelligence, and real-world evidence.¹

Customer data typically remains scattered, with demographics in one place, activity logs in another, and formulary status buried in a separate dashboard. Even “next best action” systems face implementation hurdles, as their recommendations may overwhelm users or get buried among CRM notifications. Poor visibility and actionability hinder adoption.

This disjointed experience limits quality of engagements. When key data points aren’t synthesised, field teams are forced to make decisions with incomplete information. This includes pivotal factors, such as prescribing trends, formulary access, clinical involvement, and prior discussion notes. Strong pre-call planning requires more than just knowing who to see. It demands a clear, connected view of what currently matters most to that HCP.

Understanding agentic AI’s transformative potential

While traditional AI systems respond to specific queries or execute predefined workflows, agentic systems operate with goal-oriented autonomy. They continuously evaluate multiple data streams, weigh contextual factors, and generate recommendations that reflect real-world complexity.²

Consider a sales professional with an unexpected gap in their schedule. Instead of toggling between dashboards, they ask their digital agent, “Who should I prioritise seeing tomorrow?” The system evaluates prescribing trends, HCP engagement history, territory geography, scheduling availability, and recent call notes. It then delivers a prioritised list with clear rationales for each recommendation.

During pre-call planning, the agent surfaces what matters most, including previous discussion points, open questions, brand performance in the territory, and relevant formulary changes. By consolidating these insights into a single, intuitive view, field professionals can personalise every interaction and deliver timely, relevant conversations that build on prior engagements.

Precision support for scientific engagement

MSLs navigate a dense web of scientific content to generate evidence, prepare for HCP interactions, and more. The data needed to prepare for an interaction with a KOL is vast and often scattered, ranging from clinical trial involvement and publication history to advisory board participation and digital presence.

Agentic AI helps bring this complexity into focus. A digital agent can quickly reveal what matters most to each KOL by synthesising structured and unstructured data from expert profiles and conference activity into real-time digital signals. With a single query, MSLs can learn that a KOL recently presented at a major congress, published new findings, or raised specific scientific questions in a prior interaction.³

This level of personalisation isn’t just efficient. It’s essential. When MSLs can tailor conversations to the current interest of each KOL and build on previous engagements, they’re better positioned to deliver credible, relevant, and timely scientific dialogue. This approach directly supports field force excellence by enabling more meaningful scientific exchange.

Building the foundation for AI-driven transformation

The successful implementation of agentic AI requires organisational readiness across multiple dimensions. Companies that achieve meaningful impact focus on three critical areas — strategic clarity, data readiness, and user adoption.

Strategic clarity begins with identifying specific use cases where AI can deliver measurable value. Initial implementations should address pain points while building organisational confidence, whether streamlining pre-call planning, enhancing the capture of insights, or optimising territory coverage.

Data readiness represents both a challenge and opportunity. The key insight is that organisations don’t need perfect data integration before beginning their AI journey. Current agentic systems can deploy data agents on top of existing silos, creating cross-system intelligence. Once these traditionally siloed data sources are made “agent-ready,” a digital agent can generate insights across this set of agents as a super-orchestrator, solving the need for constant data harmonisation.²

User adoption ultimately determines success. Field teams may initially view AI as an additional risk to be managed in an industry built on regulation. Successful implementations address these concerns through transparency and demonstrate how AI augments, rather than replaces, human judgment. When field professionals see that digital agents can help them spend less time on administration and more time building relationships, resistance transforms into enthusiasm.³

Preparing for an orchestrated future

The evolution toward AI-driven field operations has already begun. As these technologies mature, digital agents will evolve from individual productivity tools to connected intelligence networks. Future systems will coordinate across entire field organisations, identify patterns invisible to individual users, and predict the needs of an HCP before they’re expressed.

Integration with omnichannel marketing platforms will create seamless customer journeys that adapt in real-time to personal preferences. Field teams will no longer operate in isolation from digital engagement, but as part of orchestrated experiences that deliver consistent, personalised value across every touchpoint.

The imperative for action

The pharmaceutical industry cannot afford to wait for perfect solutions before embracing agentic AI. Organisations that begin building capabilities now, even through modest pilot programs, will develop the experience, infrastructure, and cultural readiness necessary for broader transformation.

The path forward requires balancing ambition with pragmatism. Start with clear objectives that demonstrate value quickly. Build data capabilities incrementally and focus on making existing assets AI-ready instead of pursuing massive integration projects. Most important, invest in change management that brings field teams along as partners in digital transformation.

The promise of agentic AI extends beyond operational efficiency. Freeing field teams from administrative burdens and empowering them with intelligent insights enables deeper, more meaningful engagements with HCPs. In an industry where relationships and trust remain paramount, digital agents don’t replace human connection. They enhance it, allowing field teams to focus on what they do best and build partnerships that improve patient outcomes.

References

1. Rink C, O’Reilly W. Where to deploy generative AI solutions in life sciences. IQVIA White Paper. 2025 Mar 19. Available from: https://www.iqvia.com/library/white-papers/where-to-deploy-generative-ai-solutions-in-life-sciences.
2. Nasir TA, Haslam T. Inside agentic AI: Reshaping decisions and orchestration in life sciences. IQVIA Blog. 2025 Feb 28. Available from: https://www.iqvia.com/blogs/2025/02/inside-agentic-ai-reshaping-decisions-and-orchestration-in-life-sciences.
3. Pahare A. Driving field force excellence in pharma with agentic AI and digital agents. IQVIA Blog. 2025 Aug 1. Available from: https://www.iqvia.com/blogs/2025/07/driving-field-force-excellence-in-pharma.

About the authors

Amber Pahare is the senior director of product management for IQVIA’s Global Commercial Analytics team. With over 15 years of experience, Pahare has been instrumental in building enterprise-grade data, analytics, and AI platforms for global healthcare organisations. In his current role, he leads the product team focused on delivering advanced analytics and agentic AI solutions that empower life sciences commercial organisations worldwide. Pahare has previously spearheaded product teams to design and launch real world evidence platforms and CRM solutions tailored for the life sciences industry. His strong foundation in computer science and management, combined with deep expertise in healthcare and life sciences, enables him to create innovative, consumer-grade solutions that drive measurable impact for IQVIA’s customers.

Avinob Roy is the global VP and GM of commercial analytics product offerings at IQVIA. He has 18-plus years of experience in technology, data, and analytics and leads teams that develop cutting-edge analytics solutions specifically tailored to navigate the complexities of the healthcare and life sciences industry. With an extensive background in pharmaceuticals, Roy leverages his deep industry insights to craft technology and AI-driven tools meticulously designed to foster business growth and market transformation for his clients.