Beyond the Hype: Top 10 Use Cases for AI in Pharma Commercial

Use Case Spotlight: NBA Deployment to 600 Sales Reps Field Force

Summary of the Use Case

• NBA suggestions were orchestrated across multiple channels and integrated seamlessly into the sales reps’ existing workflows.
• The system provided daily optimized recommendations, complete with transparent reasoning for each suggested action, such as the best time, channel, and content to use for each HCP.
• The initial rollout covered 600 sales reps, 8 therapeutic areas, and 20 brands.

Key Outcomes and Testimonial Highlights

• Reaching Unresponsive HCPs: Sales reps were able to engage doctors who had previously been unresponsive, uncovering new opportunities and maximizing engagement through timely, personalized outreach.
• Sales Impact: Reps who frequently used NBA recommendations saw a 14% increase in sales within just nine months of implementation.
• Direct Financial Results: Between 2% and 6.5% of the sales lift was directly attributed to NBA usage, translating to $2.5–$8 million in annualized incremental sales. Projections indicated that broader adoption could unlock over $30 million in additional annual upside.
• Field Testimonials:
• One oncology sales rep described successfully scheduling a meeting with a previously unreachable physician by acting on a timely NBA alert, stating: “I had a NBA alert from an HCP who is ‘difficult to see.’ Today, I sent him a proactive email [based] on an alert offering patient resources, sample, etc. He responded immediately! This is a big deal for me.”
• Another rep noted a 70–80% open rate for emails to customers who had never met them before, attributing this success to NBA-guided outreach.

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Use Case Spotlight: Territory Optimisation for U.S. Launch

Summary of the Use Case

A leading U.S.-based pharmaceutical company with a diversified portfolio (respiratory, diabetes, cardiovascular, oncology, neuroscience) and over 500 sales representatives sought to optimize its call planning for a new drug launch. The challenge was to create a tailored, data-driven call plan that could adapt to the complexities of multiple therapeutic areas and ensure high physician engagement.

Challenges Faced

• Difficulty aligning sales strategy across diverse therapeutic areas.
• Existing call plans lacked data-driven insights, resulting in suboptimal targeting and call frequency.
• Limited actionable intelligence on physician value and preferences.
• Need for a dynamic system to update call plans and secure sales force buy-in.

Data & AI-Driven Solutions

• Segmentation and Alignment: Advanced analytics were used to segment the physician universe by combining 10+ data sources, enabling precise targeting of the most influential and receptive physicians at the right times.
• Call Plan Optimization: The call plan was revised using insights from both field feedback and leadership goals, ensuring alignment with market dynamics and physician behavior.
• Primary and Secondary Research: Interviews with physicians and industry experts provided additional insights, further refining the targeting strategy.
• Sales Force Buy-In: Regular communication and updates ensured the field team understood and adopted the new call plan, with field intelligence used to make real-time adjustments.
• Continuous Improvement: Machine learning models and analytics enabled ongoing plan refreshes, allowing the sales force to adapt quickly to changing market conditions

Impact Delivered

• Improved Sales Force Utilization: The optimized call plan led to better targeting and higher-quality calls, maximizing the effectiveness of the 500+ sales reps.
• Reduced Time to Market: Efficient, data-backed call plans accelerated the drug launch process.
• Increased Adherence and Satisfaction: Enhanced call adherence and higher field force satisfaction due to clearer, more actionable plans.
• Sustainable Process: The company established a dynamic, continuously improving call planning process, ensuring ongoing alignment with business objectives and market needs

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Use Case Spotlight: Journey Optimisation for Top-10 Pharma

Summary of the Use Case

A top-10 pharmaceutical company sought to overcome fragmented customer experiences caused by siloed channel operations—where digital marketing, sales reps, and other touchpoints acted independently—by orchestrating a seamless, personalized journey for healthcare professionals (HCPs).

Implementation Approach

• AI-Driven Orchestration: every major data source (CRM, HCP interaction history, digital behaviors) and commercial tech stack component was integrated, enabling real-time, contextually relevant Next Best Experience (NBE) recommendations
• Personalized Engagement: AI algorithms identified the unique needs and preferences of each HCP at specific touchpoints, ensuring content and channel selection were optimized for maximum relevance and impact
• Contextual Nudges: Field teams received timely, actionable nudges (e.g., reminders, suggested follow-ups, key discussion points) on their mobile devices, based on triggers like upcoming meetings or insights from previous interactions
• Continuous Learning: The system dynamically updated recommendations as new data was captured, ensuring that every interaction reflected the latest intelligence about each HCP

Key Outcomes

• Sales Impact: The company reported a 14% increase in field sales impact within nine months of deployment, with $30 million in projected annual upside for a priority brand
• HCP Engagement: Field teams were able to engage HCPs with personalized, timely content across multiple channels, resulting in deeper, more meaningful connections and improved engagement metrics
• Operational Efficiency: The orchestration platform enabled field teams to act on a higher percentage of insights, improved the quality of customer journeys, and fostered a culture of data-driven decision-making
• Scalability: The solution supported 600 reps across 8 therapeutic areas and 20 brands, demonstrating effectiveness at enterprise scale

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Use Case Spotlight: HCP Segmentation Based on 10+ Data Sources

Summary of the Use Case

A leading pharmaceutical company decided to change its HCP segmentation and targeting strategy by using advanced analytics, real-world data, and artificial intelligence. The goal was to move beyond traditional, static segmentation and instead create dynamic, high-impact HCP segments that could be continually refined and acted upon for commercial success.

How Data & AI Were Used

• Integration of Diverse Data: The company combined real-world data (prescribing behavior, influence networks), AI-driven analytics, and deep healthcare domain knowledge to identify high-impact HCP segments and key opinion leaders (KOLs).
• Predictive Targeting: AI models analyzed vast datasets to predict which HCPs were most likely to amplify messages within their professional networks and which were likely to adopt new therapies, enabling precise, proactive targeting.
• Dynamic Segmentation: Machine learning and language-based AI models rapidly analyzed HCP interactions across channels, detecting emotions, attitudes, and pain points. This enabled the creation of evolving, data-driven personas and customer journey maps.
• Actionable Insights: The system provided marketers with real-time, actionable insights, allowing them to adjust campaigns and outreach based on up-to-date HCP behaviors and preferences.
• Humanization of Data: The company used conversational AI (custom chatbots trained on persona data) to enable ongoing, interactive engagement with HCP personas, deepening empathy and refining targeting strategies.

Key Benefits and Outcomes

• Optimized Targeting Efficiency: Marketers could focus efforts on HCPs most likely to drive message amplification and early adoption, maximizing ROI for product launches and campaigns.
• Personalized Engagement: The approach enabled highly personalized, relevant outreach, increasing HCP engagement and satisfaction.
• Continuous Improvement: Dynamic segmentation ensured that targeting strategies remained current with evolving market and HCP behaviors, supporting sustained commercial growth.
• Faster, More Informed Decision-Making: Teams could rapidly access and act on insights, improving agility in competitive therapeutic areas.

Testimonial Highlight

"Harnessing these segments as priority targets can optimize marketing efforts across the board—from targeting efficiencies to establishing leadership and market presence."
—Pharmaceutical Executive

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Use Case Spotlight: Simulating Launch

Summary of the Use Case

A major pharmaceutical company undertaked transformation of its launch readiness and market entry analytics using advanced data integration and AI. Facing the risks of revenue loss, delayed patient access, and competitive disadvantage due to fragmented data, slow market intelligence, and static planning, the company sought to operationalize AI across its launch ecosystem.

How Data & AI Were Used

• Precision Targeting with Real-World Data:
  AI models integrated and analyzed over 50,000 HCP records, segmenting by behavioral signals, geography, specialty, and prescribing patterns. Predictive analytics surfaced early adopters and underserved populations, while AI-generated engagement prompts optimized rep call plans based on physician preferences.
  Impact: Up to 50% reduction in HCP data collection time, accelerating campaign readiness and message alignment.
• Real-Time Competitive Intelligence:
  AI monitored over 1 million annual pricing changes and regulatory updates globally, providing launch teams with real-time alerts on formulary shifts, competitor approvals, and market access hurdles. Dynamic dashboards informed price positioning and access strategies.
  Impact: Up to 99% pricing event detection accuracy, enabling agile responses and minimizing margin erosion.
• Personalized Content & HCP Engagement:
  Generative AI enabled on-demand creation of tailored promotional content, sales materials, and KOL briefings. Real-time analytics personalized messaging and follow-up, while AI-driven simulations trained field teams.
  Impact: Reduced marketing content turnaround from 4–5 weeks to 8 days, unlocking $200K+ in quarterly savings.
• Simulations to Stress-Test Launch Scenarios:
  Digital twins and predictive modeling forecasted adoption curves, competitor responses, and the impact of access decisions. Insights were integrated into launch dashboards and supply planning to reduce disruptions.
  Impact: Launch setup time reduced by up to 67%, improving stakeholder alignment and commercial preparedness.

Early Results & Measurable Benefits

• Over $2.4 million in potential savings from automated clinical trial reporting and data visibility.
• Up to 60% acceleration in marketing cycles

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Use Case Spotlight: Mapping a Patient Journey in Practice

Summary of the Use Case

A prominent pharmaceutical company faced the challenge of adapting to evolving treatment pathways after new drugs disrupted established standards of care. The company needed to understand how these changes impacted the traditional patient journey and how physicians’ treatment strategies were shifting in response.

How Data & AI Were Used

• Comprehensive Data Integration:
  The company deployed advanced AI technologies to analyze multiple longitudinal claims datasets, time series databases, and electronic health/medical records (EHR/EMR). This enabled a holistic, real-time view of patient experiences and treatment pathways.
• AI-Driven Analytics:
  Machine learning models identified key patient journey pathways, mapped critical diagnostic and treatment touchpoints, and revealed the correlations between test results, physician actions, and specific drug classes prescribed.
• Consulting Layer for Strategic Insights:
  On top of the analytics, a consulting layer was added to pinpoint strategic leverage points—moments in the journey where targeted interventions could positively influence patient outcomes and physician prescribing toward the company’s drug.

Key Outcomes and Benefits

• Multiple Journey Pathways Identified:
  The analysis uncovered diverse patient pathways and critical decision points, enabling the company to tailor engagement and support strategies to each segment.
• Optimized Interventions:
  The insights allowed for the design of interventions—such as targeted education or support programs—at pivotal moments, improving patient adherence and outcomes.
• Commercial Impact:
  The company could influence the patient journey toward their therapy where appropriate, supporting both better patient care and commercial objectives.
• Faster, More Accurate Insights:
  AI-driven analysis dramatically reduced the time and effort required to map and understand complex, evolving patient journeys compared to traditional, manual methods.

Testimonial Highlight

“This AI-powered approach equipped the pharmaceutical company with data-driven insights, empowering them to make informed decisions and optimize patient outcomes in the competitive healthcare market.“

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Use Case Spotlight: Removing Bias in KOL Identification

Summary of the Use Case

A leading Fortune 500 pharmaceutical company faced significant challenges in identifying and engaging the most influential Key Opinion Leaders (KOLs) across diverse therapeutic areas and geographies. Traditional methods were limited by regional bias, personal networks, and static data, restricting the company’s ability to discover new scientific leaders and optimize collaborations.

How Data & AI Were Used

• AI-Driven KOL Management Platform:
  The company deployed advanced analytics to analyze and rank potential KOLs based on a comprehensive set of factors—research activity, influence networks, associations, area of expertise, publication history, patents, and clinical trial involvement.
• Unbiased, Dynamic Profiling:
  The AI solution provided real-time, unbiased profiles of KOLs, enabling the company to extend beyond its existing contacts and discover top clinical investigators and experts previously outside its network.
• Operationalization:
  All KOL data and tools for internal collaboration were integrated, supporting strategic decision-making and innovation projects.

Key Outcomes and Benefits

• Broader, More Diverse KOL Pool:
  The company identified and engaged a wider range of KOLs, including those in emerging fields and new geographies, overcoming previous network and regional limitations.
• Improved Collaboration and Innovation:
  Dynamic, data-driven KOL insights enabled more productive partnerships, supporting clinical trials, research, and product launches.
• Enhanced Decision-Making:
  Centralized, real-time KOL intelligence improved the quality and speed of decisions related to partnerships and scientific collaboration.

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Use Case Spotlight: Integrating Access & Pricing Data Sources as the Key

Summary of the Use Case

A major pharmaceutical company transformed its drug pricing and market access strategies using advanced data integration and AI-driven analytics. The challenge was to navigate increasingly complex pricing environments, reduce inefficiencies, and ensure timely patient access to therapies in a rapidly changing global healthcare landscape.

How Data & AI Were Used

• Integrated data source:
  The integration of drug agreement library and global pricing and market access database provided a comprehensive, real-time view of global pricing trends, reimbursement frameworks, and access agreements.
• AI-Driven Analytics:
  AI algorithms analyzed vast datasets from both platforms, uncovering patterns in payer behavior, pricing benchmarks, and reimbursement outcomes. This enabled the pharma company to simulate pricing scenarios, optimize rebate strategies, and anticipate market access barriers.
• Actionable Decision Support:
  The combined solution empowered market access teams with actionable insights—enabling smarter, faster, and more equitable pricing decisions. Teams could rapidly adjust strategies in response to competitor moves, regulatory changes, or payer demands.
• Efficiency & Equity:
  By automating data analysis and streamlining rebate management, the company reduced administrative burden and improved the speed and fairness of pricing and access decisions. The result was more efficient negotiations, optimized pricing models, and faster patient access to life-saving therapies.

Key Outcomes & Testimonial Highlights

• Reduced Inefficiencies:
  The system powered by advanced analytics minimized administrative effort and human error in rebate and pricing management.
• Improved Patient Access:
  Faster, data-driven pricing and access decisions meant patients received therapies more quickly.
• Smarter, Fairer Decisions:
  The platform enabled the company to make pricing and market access decisions that balanced commercial goals with healthcare system sustainability.

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Use Case Spotlight: Personalized Content Generation in Practice

Summary of the Use Case

A leading global pharmaceutical company implemented AI-driven content personalization and recommendation engines to transform its engagement with healthcare professionals (HCPs). The company faced the challenge of delivering relevant, timely, and compliant content to HCPs across multiple channels, while ensuring that each interaction was tailored to individual preferences and clinical needs.

How Data & AI Were Used

• Personalized Content Generation:
  AI models analyzed vast datasets on HCP preferences, behaviors, and historical interactions. This enabled the company to generate and curate highly personalized content flows, moving beyond generic messaging to deliver information relevant to each HCP’s specialty, interests, and patient population.
• Centralized Content Repository:
  The company leveraged a pre-approved repository of content, with AI systems assembling and sequencing the most effective assets for each customer journey. This ensured compliance and consistency while enabling rapid customization.
• Omnichannel Delivery:
  Machine learning predicted the optimal channels (email, webinars, digital ads, etc.) and timing for each HCP, maximizing engagement and message retention. Platforms like Salesforce and Veeva were used to orchestrate these personalized journeys and automate next-best-action recommendations.
• Sales Rep Enablement:
  AI-powered tools, such as Veeva’s CRM Bot, assisted sales reps in generating personalized content at scale, empowering them to address HCP needs more effectively and efficiently.

Key Outcomes and Benefits

• Enhanced HCP Engagement:
  The company achieved higher open and response rates, as HCPs received content that was more relevant and valuable to their practice
• Optimized Customer Journeys:
  Tailored content and channel sequencing improved the overall customer experience, leading to deeper relationships and increased loyalty
• Scalability and Efficiency:
  AI-driven automation enabled the company to personalize communications at scale, supporting large and diverse HCP audiences without increasing manual workload
• Compliance and Speed:
  Centralized, pre-approved content and AI-assisted review processes ensured that personalization did not come at the expense of regulatory compliance or speed to market

Testimonial Highlight

“AIs can analyze vast amounts of data on healthcare provider preferences, behaviors, and past interactions to generate highly personalized content to meet the needs of HCP audiences. Today, more companies employ AI models to assemble and curate the most effective content from a centralized pre-approved repository and deliver tailored message flows across channels.“

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Use Case Spotlight: Social Media Listening with Emotion Analysis

Summary of the Use Case

Novo Nordisk, a global leader in diabetes care, leveraged GenAI-powered social listening to gain deep, actionable insights into patient sentiment and emerging needs within the diabetes community. Traditional social listening provided only basic sentiment analysis, but with AI and GenAI, Novo Nordisk could capture nuanced emotions and trends from massive volumes of social media and forum data.

How Data & AI Were Used

• Advanced Sentiment and Emotion Analysis:
  GenAI models analyzed social media conversations, detecting not just positive or negative sentiment, but also complex emotions like frustration, urgency, and excitement. For example, the AI could identify when patients were struggling with the emotional burden of diabetes or medication side effects, which would have been missed with traditional tools.
• Trend and Issue Detection:
  By combining social media, forum data, and past survey responses, the AI system automatically detected emerging issues—such as medication adherence problems—months earlier than prior manual or rules-based methods.
• Real-Time Competitive and Market Insights:
  The AI platform provided Novo Nordisk with real-time insights into both patient and competitor activity, allowing the company to track the impact of competitor product launches, marketing strategies, and patient reactions.
• Actionable Product Development and Support:
  Insights from AI-driven social listening revealed a growing trend of patients struggling with the emotional burden of diabetes. In response, Novo Nordisk developed the “NovoCare” mobile app, which offered personalized support, connected patients with online communities, and provided access to mental health resources.

Key Outcomes and Benefits

• Faster and More Accurate Insights:
  Novo Nordisk detected new market issues and patient needs up to six months earlier than before, enabling proactive strategy adjustments.
• Increased Patient Engagement:
  The NovoCare app, developed as a direct result of GenAI insights, saw a 40% increase in user engagement within six months.
• Increased Patient Engagement:
  Real-time monitoring and nuanced understanding of both patient sentiment and competitor actions enabled Novo Nordisk to refine its marketing and support strategies, strengthening its leadership in diabetes care.

Testimonial Highlight

“Novo Nordisk leveraged GenAI to analyze social media conversations and identify a growing trend of patients struggling with the emotional burden of their condition. This insight led to the development of a new mobile app called 'NovoCare' offering personalized support, connecting patients with online communities and providing access to mental health resources. The app saw a 40% increase in user engagement within the first six months, demonstrating the power of GenAI to translate social listening into tangible patient benefit.“

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