AWS for Industries
Highlights from the 2025 AWS Life Sciences Symposium’s Commercialization track
On May 6, more than 1,000 life sciences leaders from over 400 organizations convened in New York City for the seventh Annual AWS Life Sciences Symposium. United under the theme, “Building for Breakthroughs – AI-powered Innovations Transforming the Pharmaceutical Value Chain,” the event showcased how generative AI has moved beyond its hype to become a practical catalyst for pharmaceutical transformation. Over the course of 26 sessions, featuring 39 expert speakers, attendees explored how advanced AI is driving measurable impact—accelerating innovation, reducing costs, and supporting compliance at scale. (Keynote video and highlights here)
It’s real-world impact was evident in the Commercialization Breakout Track, where industry leaders discussed how AI is being applied to address some of pharma’s most urgent and complex commercial challenges.
Pharma Commercialization at a Crossroad: The Need for a New Playbook
Pharma commercialization today is at a critical inflection point. Despite significant annual sales and marketing investments industry-wide, many pharmaceutical companies face challenges optimizing commercial returns in today’s increasingly complex healthcare ecosystem. Only half of healthcare providers (HCPs) are accessible to sales representatives, and those who are available are contacted approximately 1.4 times per working hour. Meanwhile, the competitive landscape is intensifying, with over 830 new drug launches projected by 2031, placing enormous pressure on commercial teams to differentiate and deliver.
These challenges are further compounded by systemic structural shifts in the industry: evolving payer dynamics, tighter regulations, decentralized care, and rising patient expectations. This also includes healthcare providers seeking more relevant, evidence-based engagements that enhance their clinical decision-making capabilities. Meanwhile, fragmented operations across commercial, medical, and support functions lead to inconsistent messaging and disjointed HCP experiences.
To succeed, organizations need a unified, intelligent, and data-driven model—one that replaces outdated processes with AI-powered operations. This means contextualizing the HCP and patient journey through variables like geography, social determinants of health, and local market dynamics—to deliver personalized experiences at scale. Achieving this vision requires a modern data infrastructure, cloud-native technologies, and a new mindset around how commercial teams operate.
The commercialization breakout track featured organizations already making this leap—sharing real-world use cases, insights, and recommendations—turning vision into action. Here are the key takeaways
Gilead: Building AI-Powered Sales Excellence on a Strong Data Foundation
The track opened with a session by Pradeep Yathira, Global Head of G&A, Commercial Analytics, and Cloud at Gilead, who was joined by Faiz Merchant, Principal at ZS. They shared how a connected, compliant, and cloud-first data strategy is the cornerstone of AI-powered commercialization.
Moving from a fragmented, multi-cloud environment, Gilead now runs 60 percent of its workloads on AWS, with a goal of reaching 90 percent by 2025. Central to this transformation is their AI-ready enterprise platform built on AWS, which supports a data mesh architecture that powers everything from self-service analytics and reusable ingestion frameworks, to conversational AI and deep learning. This infrastructure supports key strategic imperatives including data democratization, automation, agile operations, and strong governance. With this robust foundation in place, Gilead is identifying AI use cases across commercial applications, conducting pilots, and scaling successful solutions to transform operations – from MLR reviews optimization, to next best action patient alerts, and more.
One of the most powerful illustrations is the transformation of the sales representative experience: moving from manual, reactive workflows to intelligent, proactive engagement. AI-powered assistants now surface insights, recommend next steps, and support regulatory and promotional compliance requirements with just a few clicks—ensuring interactions remain within approved guidelines while freeing up reps to focus on high-value HCP engagement. Given that a substantial portion of SG&A (Selling, General, and Administrative) budgets (typically ranging from 30-50 percent) are tied to field teams, these improvements directly impact business performance.
From intelligent chatbots and automated content generation to streamlined medical/legal reviews, this AI-driven approach to commercialization reflects a deep commitment to building from the foundation up. Their biggest lesson? Successful AI isn’t about adding tools—it’s about enabling them with clean, connected, and governed data from the start—and not the other way around.
Johnson & Johnson: Moving from Static Plans to Dynamic Personalization
The session led by Dharmesh Thakkar, Head of Technology & Product Group Leader – Commercial Operations, Data & Analytics, and Shyam Mohapatra, IT Director, Data & Insights, at Johnson & Johnson Innovative Medicine, expanded the conversation around personalization in pharma marketing at scale.
As Johnson & Johnson’s product portfolio evolves over the next three years, the company is proactively reimagining its commercial strategy to meet the shifting needs of patients, providers, and stakeholders. At the core of company’s approach is a holistic data strategy designed to power insight generation at scale.
Central to this is a self-service data marketplace, built on AWS, that unifies high-quality, analytics-ready data across business domains and delivers it to users through an intuitive, low-code or no-code interface. This platform serves as a single source of truth—streamlining data access, and unlocking actionable insights across brands and accounts. It also promotes data reusability across teams while ensuring strong governance, data lineage, and compliance.
The results are impressive: a 50 percent reduction in time to launch, 18–20 hours saved through automation, and a 2x boost in analytics efficiency. This shift marks a move from rigid, one-size-fits-all strategies to adaptive, data-informed planning—enabling teams to navigate today’s market complexity and prepare for tomorrow’s innovation.
EVERSANA: Accelerating MLR with Generative AI
While foundational data is essential, speed is equally critical—especially in marketing content operations. However, the medical, legal, and regulatory (MLR) review process for new content generation continues to remain one of the most persistent bottlenecks, with turnarounds ranging 24-45 days for each asset.
EVERSANA’s Chief Innovation Officer Faruk Capan, and Senior VP Abid Rahman addressed this head-on, unveiling EVERSANA ORCHESTRATE™ MLR, an AI-powered solution purpose-built to streamline and accelerate MLR workflows. Built on AWS (using services like Amazon Bedrock, Amazon Cognito, and Amazon Textract) and integrated with Veeva, the platform uses generative AI to automate claims extraction, cross-referencing, annotation, and message matching. Human-in-the-loop review ensures regulatory accuracy while dramatically reducing review times—from weeks to days.
Now available on AWS Marketplace, ORCHESTRATE MLR helps commercial teams deliver compliant content faster, accelerate campaigns and reduce regulatory risk. The solution also enables scalable personalization and content modularization through AI-enhanced MLR workflows. It’s a powerful example of how intelligent automation is becoming a strategic advantage in life sciences—improving engagement and responsiveness in an increasingly dynamic market.
Lilly: Reimagining Patient Engagement with First-Party Data
Beyond HCPs, patients are also demanding more seamless, personalized experiences comparable to what they receive in other areas of their lives (like retail, banking, or streaming services). Meeting these expectations requires more than just great products; it demands a complete rethinking of how pharma companies engage with patients across the full healthcare journey.
Lilly, as presented by Global VP Steve Rommeney, is responding by building a first-party data ecosystem that puts patient consent and privacy at the center—with notable implementation in targeted therapeutic areas like their obesity treatment portfolio. By responsibly collecting and activating data directly from patients, Lilly is reshaping the patient experience, moving beyond traditional campaigns to deliver relevant, timely, and meaningful interactions that support adherence, improve outcomes, and build trust.
This cloud-based data platform, built on AWS with Red Hat OpenShift services, embeds governance, privacy, and permissions management from the ground up. The architecture incorporates key building blocks including unified consent capabilities, cross-domain tracking, ad suppression mechanisms, and cross-platform authentication—all essential for delivering compliant, personalized experiences at scale.
On the front end, this integrated digital platform powers patient-facing experiences across Lilly.com and LillyDirect—from research and browsing to fulfillment, onboarding, and ongoing support. On the backend, centralized operations and real-time analytics enable Lilly to streamline engagement across channels, improve service delivery, and continuously optimize for relevance and impact.
Lilly’s approach recognizes that different therapeutic areas require tailored engagement models, with their targeted initiatives serving as a proving ground for how first-party data can transform patient journeys when properly implemented. This is more than a digital upgrade—it’s a strategic reinvention of patient engagement.
Salesforce: Combining Human and Agentic AI Capabilities
Rounding out the breakout track, Tara Helm, VP of Life Sciences Cloud Strategy at Salesforce, an AWS Partner, showcased how the Salesforce Life Sciences Cloud is helping pharma customers embed AI into the entire value chain. At its core is a powerful data engine that unifies data from diverse sources like HCP interactions, prescription data, electric health records (EHRs), and more, to create dynamic, actionable profiles that fuel real-time engagement across patient services, medical affairs, and sales. By integrating AI into critical workflows, the platform helps streamline patient and provider interactions, simplify compliance, and improve operational agility.
Central to this vision is Agentforce for Life Sciences, a system that combines AI agents with human expertise to reduce administrative burden. A powerful example: pre-call planning for field reps. Using Agentforce, reps no longer need to navigate 12–15 tools to prepare for provider visits. Instead, they receive AI-curated insights—allowing them to focus on value-adding activities and high-impact engagement.
Salesforce strategic partnership with AWS creates a robust technical foundation for these innovations. This includes data transformation solutions that makes enterprise information AI-ready, and integration with Amazon Bedrock to securely deploy foundation models—ensuring responsible AI use with enterprise-grade governance. The impact? Pharma customers can deploy generative AI solutions for commercial transformation confidently and responsibly, without needing to building everything from scratch.
Commercialization Reinvented—With Generative AI, Strategy, and Speed
As the stakeholder ecosystem expands beyond what field teams alone can cover, and compliance demands grow alongside an explosion of scientific content, one thing is clear: personalized, scalable engagement is no longer optional, it’s a necessity. And, differentiated, data-driven experiences aren’t just a competitive advantage anymore—they’re a business imperative.
Leading life sciences organizations are reinventing how commercial operations operate, communicate, and create value. They’re embracing it as a driver of holistic transformation—reimagining data infrastructure, operations, and engagement in parallel. And they’re seeing measurable impact: faster product launches, lower costs, deeper insights, and stronger HCP and patient connections.
At AWS, we’re helping our customers go further, faster—whether you’re deploying AI agents to automate content creation, uncover insights from congresses and literature, or transform regulatory workflows.
That’s why 9 of the top 10 global pharmaceutical companies globally use AWS for generative AI and machine learning.
Contact an AWS Representative today to learn how we can help your organization accelerate commercial transformation.
Further Reading
- 7th Annual AWS Life Sciences Symposium: Keynote highlights
- Accelerating Life Sciences Innovation with Agentic AI on AWS
- Highlights from the 2025 AWS Life Sciences Symposium’s Manufacturing Track
- Highlights from the 2025 AWS Life Sciences Symposium’s Drug Discovery Track
- Highlights from the 2025 AWS Life Sciences Symposium’s Clinical Trials Track