Reimagining Field Force Excellence with AI

Introduction

As life sciences marketers and sales leaders navigate mounting pressure to boost productivity, enhance decision-making, and deliver measurable impact, artificial intelligence (AI) has emerged as a promising solution. While AI’s potential is widely acknowledged, its real-world effectiveness and business value remain under scrutiny. Navigating this evolving AI landscape requires cutting through the hype to uncover practical, data-driven opportunities.

In the pharmaceutical industry, AI is being applied across various use cases, from drug discovery and clinical trials to commercial optimization and patient engagement. Yet, regardless of the application, one question remains central for all business leaders: Why do they need AI? To answer this, business leaders must identify the key drivers, recognize where AI adds value, differentiate between use cases, and understand the barriers to successful adoption. This article explores the rise of the AI-augmented field force and examines how pharmaceutical leaders can harness AI to achieve real business impact. For this article, the term “AI” encompasses various forms, including generative AI, agentic AI, and traditional AI/ML approaches.

Transformative Forces in the Pharmaceutical Sector

The pharmaceutical industry stands at a critical inflection point where disruption is being driven by the rise of specialty therapies, a shift towards commercial design strategies for organized customer groups, evolving market access dynamics, and growing regulatory pressure. At the same time, the healthcare landscape is being reshaped by personalized medicine, digital transformation, and an explosion of patient-centric data. For commercial leaders, this means navigating growing complexity, marked by shifting portfolios, evolving stakeholder expectations, and tighter regulatory demands. In this environment, traditional sales models are no longer sufficient. To stay competitive, companies must reimagine field force strategies to deliver greater agility, precision, and HCP engagement. Three major forces accelerating this transformation of the pharmaceutical commercial model include:

• Accelerated Timelines Post-COVID-19: The pandemic compressed R&D and launch timelines from years to months, setting a new standard for speed and demanding more agile, responsive field strategies.
• The Inflation Reduction Act (IRA) and Pricing Pressures: With the introduction of the IRA, pharmaceutical companies face pricing constraints and compressed revenue windows. Maximizing product ROI within these shorter timeframes has become a strategic imperative, intensifying the need for high-impact, data-driven field execution.
• The Rise of AI, including generative AI, agentic AI, and traditional AI/ML approaches: Advanced AI capabilities are redefining the boundaries of commercial excellence. From intelligent segmentation, dynamic targeting, and predictive analytics to content personalization and autonomous task execution, AI enables life sciences organizations to operate with greater efficiency, accuracy, and customer focus than ever before.

These shifts demand that leaders address three commercial imperatives in every life sciences initiative: speed, personalization, and intelligence.

• Need for Speed: In today’s competitive environment, speed is a strategic imperative across every product lifecycle stage, from R&D and regulatory approval to market launch and the limited exclusivity window. This accelerated timeline requires moving beyond static planning to agile, real-time decisions for field teams, especially for specialized therapies.
• Need for Personalization: Every HCP, account, and patient expects tailored engagement with the right message delivered at the right time, through the right channel. The variety of customer types (HCPs, hospitals, IDNs, payers) makes personalization essential, given different customer stakeholders’ unique objectives and priorities. At the same time, advances in AI are enabling the development of more personalized treatment plans, moving beyond one-size-fits-all approaches to meet each patient’s unique needs.
• Need for Intelligence: The healthcare ecosystem generates vast amounts of complex data, and field teams must rapidly access, interpret, and act on insights to stay competitive. This rising demand for agile, actionable intelligence is driving widespread adoption of AI across the life sciences value chain, from pre- to post-discovery.

Empowering Customer Engagement: The Rise of the Augmented Field Force

As the pharmaceutical industry adapts to evolving market dynamics, its most influential customer engagement channel—the field force—is experiencing a significant transformation.

Despite speculation about the need for field reps, face-to-face (F2F) engagement remains one of the most effective channels for driving sales, according to a survey by BCG¹. The survey also revealed that hybrid approaches, which combine in-person visits with non-personal promotion (NPP), such as email campaigns, digital content, and remote detailing, outperform siloed tactics.

The sales rep’s role is not disappearing; it is being redefined and augmented with AI to meet modern commercial demands. Today, an augmented rep is a modern field professional who is ecosystem-aware, technology-enabled, and insight-driven. This evolution requires a broader skill set that includes:

• Patient focus: Understanding the whole patient journey to identify barriers to therapy initiation and make timely interventions.
• Customer-centric: Acting as a strategic partner by understanding each HCP’s priorities and challenges, offering support across data, clinical decisions, and resources. This deeper engagement includes problem-solving skills, knowledge of the care environment, meaningful case discussions, and coordination across sales, medical, and account roles. In many markets, especially for specialty and rare disease areas, pharma companies are shifting to localized, account-based models, where fewer but more specialized reps manage HCPs and institutions. Companies expect these reps to build deeper, multi-dimensional relationships and deliver measurable value beyond the product.
• Omnichannel planning: Engaging across multiple channels, including F2F, virtual, digital, and non-personal.
  

Source: Axtria Inc.

AI’s Expanding Role in Pharma Commercial Excellence

Artificial intelligence is rapidly transforming the pharmaceutical commercial value chain, reshaping how companies engage customers, optimize operations, and unlock growth. From customer segmentation to territory alignment, call planning, and incentive compensation, AI is no longer an emerging trend but a strategic imperative.

As AI adoption matures, the focus shifts from experimentation to scaled deployment, making it critical to identify which use cases are gaining traction and which are still in early exploration. Pharma sales and brand leaders seek AI use cases that can be scaled across the organization to deliver measurable business impact. The motivation is clear: companies that successfully industrialize AI have the potential to double their operating profits.

The illustration below shows some of the use cases for AI in pharma and qualifies their position in the adoption journey within the pharma industry. The bubble size represents the number of companies adopting these use cases, while the ‘Y’ axis indicates the impact or business value they generate.

From Pilots to Scale: Where Is AI Gaining Ground?

Source: Axtria Inc.

For example, most life sciences companies now use Next Best Actions (NBAs) and triggers to guide engagement. Drawing on diverse data, NBAs help prioritize actions and refine strategies, which is increasingly important as HCP preferences and behaviors now shift more frequently than in the past.

On the other hand, omnichannel dynamic targeting use cases are gaining traction and generating substantial business value by integrating personal and non-personal channels to create a more agile and comprehensive targeting strategy. Unlike traditional quarterly planning, dynamic targeting answers key questions in near real time. Who to engage, when, and with what content are all answered quickly and easily. Field teams can make more timely and impactful decisions by identifying target-rich environments and adapting promptly, especially for launch brands, specialty products, or during competitive shifts. Axtria’s 2025 Customer Engagement Planning and Execution Benchmarking Study shows that adoption of dynamic targeting by large pharma companies rose from 17% in 2023 to 25% in 2024—a shift driven by the need for agility in an increasingly complex marketplace.²

Companies are also exploring early-stage use cases such as identifying the factors that drive strong field performance. By using AI to understand what sets top-performing reps apart, they aim to apply those insights across the wider team.

AI also enables a range of high-impact applications across the commercial value chain, which can be used for everything from improving data quality and streamlining incentive planning to equipping reps with AI copilots and digital twins. These intelligent tools offer real-time insights, recommendations, and support, helping field teams make smarter decisions and engage with customers more effectively.

Together, these use cases deliver measurable value and accelerate the transformation of the pharmaceutical commercial model.

AI in Action: Tangible Results Across the Value Chain

Companies that effectively apply AI best practices are realizing substantial business benefits across multiple areas. Some of the benefits Axtria has observed across its pharmaceutical partners showcase the significant value of AI:

• Top-line Growth:Brands have seen a 2-6% lift in revenue, translating into millions or even billions of dollars, in top-line growth.
• Customer Reach:AI-driven customer engagement strategies can improve customer coverage by up to 20%.
• Rep Productivity:AI-enabled reps are up to 30% more productive.
• Faster Execution: AI helps reduce cycle times across commercial processes by nearly 40%.

Business Benefits of AI

Source: Axtria Inc.

According to PwC, a high degree of industrialization of AI use cases could add $254 billion in annual operating profits to the global pharma industry by 2030, including $155 billion in the US and $33 billion across Europe.³

The message is clear. Companies that embed AI across their commercial value chain and support it with strong data foundations and change management are pulling ahead. As use cases evolve from pilots to enterprise-wide platforms, the opportunity to transform customer engagement, accelerate performance, and gain a competitive advantage has never been greater.

AI: Powering Precision Engagement

Generative Insights

Source: Axtria Inc.

Artificial intelligence redefines how sales reps plan, engage, and drive impact. By integrating data across sources, AI empowers reps to tailor their strategies based on micro-segmentation, optimized messaging, and real-time triggers—ultimately improving HCP engagement and product uptake. Take, for example, personalized messaging. AI-driven micro-segmentation and best-message algorithms help reps deliver highly relevant, targeted communications to healthcare providers (HCPs). In market access conversations, this means addressing real patient challenges, like affordability, insurance coverage, or reimbursement eligibility. By using messaging that reflects the HCP’s day-to-day patient population and aligning with the HCP’s perspective, reps can engage in value-driven conversations that resonate and influence prescribing decisions.

Customer insights are also gaining ground, especially in specialty therapies. By leveraging lab data and biomarkers, pharmaceutical companies can detect when a specific test result (e.g., a biomarker submission) becomes available. This insight allows reps to time their outreach strategically, delivering the right information or support at the most relevant, clinical moment, often ahead of treatment decisions. Such real-time intelligence ensures reps are not just better informed but more impactful.

In essence, AI enables reps to understand HCPs and patients better, turning every interaction into a tailored, data-informed opportunity to drive results.

Generative Business Intelligence: Enabling Smarter, Data-Driven Field Performance

Generative Business Intelligence

Source: Axtria Inc.

Generative business intelligence (BI) is emerging as a powerful enabler for pharmaceutical companies seeking to align incentive strategies with commercial outcomes. Many of Axtria’s clients leverage generative BI to optimize incentive compensation and uncover actionable insights that directly impact revenue and profitability across geographies.

By synthesizing diverse data sets, from rep activity and call volumes to geographic coverage and quota attainment, generative BI provides a clear view into what’s working and where intervention is needed. For instance, high-performing regions often show strong call frequency and efficient coverage. In contrast, other regions may reveal challenges such as underperformance against quotas or extended drive times that reduce overall productivity.

Field AI Assistant

Source: Axtria Inc.

A key advancement in this space is the evolution of field AI assistants, such as “Ask Me Anything” tools. These tools integrate data across multiple domains, like customer interactions, HCP crediting, pre-call planning, and territory insights. They offer reps a consolidated view of their performance and opportunities.

The real value lies in centralizing this intelligence. When reps can access all relevant data in one place, they’re better equipped to analyze customer trends, prioritize opportunities, and execute NBAs more effectively. Generative AI-powered assistants act as real-time copilots, guiding decisions, streamlining workflows, and elevating field performance. Generative BI transforms data into a strategic asset, enabling a more agile, responsive, and performance-driven commercial field force. Ultimately, this ties the entire rep experience together, making them more effective and efficient and driving sales growth.

Harnessing AI in Field Force Operations: Key Considerations and Strategic Insights

AI is a Journey

Source: Axtria Inc.

Artificial intelligence in pharma is not a one-time solution but an ongoing journey. Realizing its full potential requires steady progress across infrastructure, data, and people. Based on real-world pharma AI journeys, the following are essential considerations for driving successful implementation:

• Data Foundation: AI systems rely on high-quality, structured, diverse data to deliver accurate, actionable insights. For a pharmaceutical company, this means integrating diverse sales, marketing, clinical operations, and external datasets (such as lab results, claims, and health system data) into a unified, patient- and customer-centric view. Further, establishing robust data architectures like data lakes is essential to managing both structured and unstructured data.
• Data Privacy and Governance: AI systems thrive on data, and ensuring data privacy is a top priority, especially when dealing with sensitive health and patient information. Maintaining compliance with regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) is crucial to avoiding legal issues and building trust with stakeholders. Further regulations and guidelines by the U.S. Food and Drug Administration (FDA) ensure data privacy and ethical practices. Further, misinterpretations or unqualified outputs can lead to compliance issues or reputational risks, underscoring the need for clear governance and caveats in AI-generated guidance.
• Infrastructure capabilities: Organizations must build scalable, flexible infrastructure to support the growing role of AI across commercial operations. Success starts with clean, accessible data, cloud platforms, integrated data ecosystems, analytical capabilities, and strong governance.
• Aligning Incentives: There can be a disconnect between stakeholders (e.g., data scientists, business leaders, and regulatory bodies), leading to conflicting goals. Aligning incentives early in the process helps ensure smoother execution and a shared understanding of objectives and cross-functional collaboration throughout the AI initiative.
• Preference for simplicity: AI’s power lies in its ability to provide clarity and simplify decision-making. Complex algorithms or overly technical approaches can backfire if they aren’t intuitive or fail to address core business needs.
• Prioritizing proven, practical solutions: The latest AI trends can be distracting, and chasing hype over value often leads to wasted effort. Prioritizing proven, practical solutions over novelty is key to driving meaningful and sustainable impact
• Prioritize Customer-Centered Engagement: AI should elevate how field teams connect with customers by enhancing personalization, relevance, and service quality. Keeping the customer experience at the core ensures AI adds real value—enabling timely, insight-driven interactions that shift engagement from transactional to relationship-based and fostering deeper satisfaction and long-term loyalty.
• Rep Enablement and Training: Pharma companies must invest in training field reps to confidently use AI-powered tools—such as chatbots, virtual assistants, real-time insights, generative and agentic AI. AI chatbots are evolving into intelligent virtual coaches, supporting a range of users—from field reps to sales, brand, and commercial operations leaders. These virtual coaches explain the rationale behind insights and offer actionable recommendations. With GenAI advancements, these assistants deliver more natural, context-aware interactions, enhancing user experience and freeing teams to focus on high-value, strategic work.

Conclusion: AI as a Strategic Enabler

AI is becoming an integral part of pharma commercial operations. However, its effectiveness hinges on human-machine coexistence, ongoing learning, well-orchestrated implementation, and ethical principles. Companies are beginning to pilot AI tools in an effort driven by increasing feedback from end users who recognize the benefits of AI-driven insights, improved targeting, and faster decision-making. Organizations can create a meaningful impact across the field force by approaching AI with a strategic mindset and operational discipline, transforming productivity and delivering value to patients, physicians, and shareholders.

Early adopters are already measuring engagement, collaboration, and sales performance improvements, indicating that AI can deliver real value when implemented thoughtfully. The key lies in starting with focused, measurable use cases and scaling responsibly with the right talent, technology, and cross-functional coordination. AI will undoubtedly play a central role in the future of pharma, but realizing its full potential will require careful planning, organizational alignment, and a commitment to value-driven innovation.

Sources

• Wolf F, Schulze U, Shah S, D’Avanzo I, Moeckel F, Lago E, Boston Consulting Group, For Physicians and Pharma, Hybrid Engagement Is the New Normal, Accessed on June 20, 2025, Available at: https://www.bcg.com/publications/2023/hybrid-engagement-is-the-new-normal-for-physicians-and-pharma-companies
• 2025 Customer Engagement Planning and Execution Benchmarking Study, Accessed on June 20, 2025, Available at:https://insights.axtria.com/reports/2025-customer-engagement-planning-and-execution-benchmarking-study
• Kaspar C, Solbach T, Ahrens H-F, Dizinger J, Müller J, Azar C, Strategy &, PricewaterhouseCoopers, Re-inventing Pharma with Artificial Intelligence, Accessed on June 20, 2025, Available at:https://www.strategyand.pwc.com/de/en/industries/pharma-life-sciences/re-inventing-pharma-with-artificial-intelligence/strategyand-re-inventing-pharma-with-artificial-intelligence.pdf

FAQs