AWS for Industries

7th Annual AWS Life Sciences Symposium

Dan Sheeran, GM Healthcare and Life Sciences at AWS presenting at the seventh annual AWS Life Sciences Symposium

More than 1,000 industry leaders from all corners of the life sciences industry converged in New York City for the seventh annual Amazon Web Services (AWS) Life Sciences Symposium to discuss the latest advancements in Life Sciences technology. Throughout the 26 sessions presented by 39 customer speakers, one sentiment was loud and clear: the future of generative AI is here, and the life sciences industry is not only embracing it, but leading the charge.

Together, we explored what’s working (and what’s not) with generative AI, and dove into how leading life sciences companies are building for agentic AI success.

Making data your differentiator

In the race to adopt generative AI, one truth remains constant: your AI is only as smart—and as strategic—as the data behind it.

That’s not a new idea. What is new is how rapidly we’re helping customers transform fragmented data silos into a unified, strategic asset—turning data into their most powerful differentiator.

At AWS, we think about data in three key categories:

  1. Your existing data
  2. New data you create
  3. Third-party data

The real magic of AI happens when all three come together. Let’s start with the first two—your own data. We’re working with customers to not only migrate and unify their existing data, but also to close the loop by tapping into the valuable new data being generated across their organizations.

Matt Studney, Senior Vice President at Merck Research Labs, showcased how his team created a clinical data layer on AWS that brings together clinical and operational data across studies into a single platform, accelerating processes through purposeful applications of automation.

Now, let’s talk about third-party data.

Real-world data (RWD) is becoming essential for pharmaceutical companies, healthcare providers, payors, and government health agencies. It’s transforming how clinical trials are designed, how recruitment is managed, and how drug safety and efficacy are evaluated in real-world scenarios. In fact, 85% of FDA drug approvals (including biologics) from 2019 to 2021 leveraged real-world evidence (RWE).

Despite its promise, turning RWD into actionable insight is still a challenge—it’s fragmented, expensive, and time-consuming.

That’s why, together with our partners, AWS is streamlining real-world data workflows. We’re making it faster and easier to discover, evaluate, access, and derive insights from diverse RWD sources—reducing a process that used to take four months down to as little as two weeks.

To learn more, check out our work with Datavant and our other partners in Accelerating Real-World Evidence with AWS. Customers like Boehringer Ingelheim are already seeing the impact from these capabilities. We invite you to learn more and join the waitlist for the Lighthouse Partner Program to get early access to these powerful, secure data collaboration tools.

We were also excited to discuss a new collaboration with the Broad Institute and Manifold AI. This collaboration will enable seamless data access and delivery combined with cutting-edge analysis and collaboration capabilities. Manifold is modernizing Broad’s data platform and data science environment—including Terra—by using the latest AWS services: AWS HealthOmics, Amazon Bedrock, and Anthropic’s Claude in Amazon Bedrock to power scalable, AI-driven research.

Most of our pharma customers and industry collaborators already operate on AWS. This partnership allows us to enable data access and delivery seamlessly, with powerful new capabilities for analysis and collaboration. – Benjamin Neale, core value member at Broad Institute.

Generative AI: Doing More of What Works

Across life sciences, we’re seeing a clear trend: organizations are no longer experimenting with generative AI for the sake of innovation—they’re scaling what delivers results. Our customers are increasingly focused on streamlining and centralizing the use of generative AI to amplify impact where it matters most.

A standout example is Johnson & Johnson, which recently announced a strategic pivot to concentrate exclusively on their highest-value generative AI use cases. This kind of focus enables faster ROI, better resource allocation, and sustainable innovation at scale.

Another leader in this space is Sanofi. Emmanuel Frenehard, EVP and Chief Digital Officer, shared at the event how Sanofi has scaled generative AI across every level of the organization with powerful results.

One of their flagship solutions is Concierge, an AI-powered assistant that delivers curated, contextual answers to company policies and procedures. It’s now used by over 80% of Sanofi employees, demonstrating how deeply embedded AI can become when it solves a real, daily need.

Sanofi’s digital accelerator is driving innovation at scale by leveraging AWS technology. Through fine-tuned third-party models and the use of high-performance computing, storage, and GPUs, they’re optimizing R&D workflows—shortening the path from research to treatment, and ultimately delivering new therapies to patients faster.

Emmanuel Frenehard, EVP and Chief Digital Officer at Sanofi presenting at the SymposiumEmmanuel Frenehard, EVP and Chief Digital Officer at Sanofi presenting at the Symposium

Generative AI in Life Sciences: Real Use Cases, Real Value

Across R&D, clinical, manufacturing, commercial, and enterprise functions, the most successful teams are focused on solving specific, high-impact challenges. Alongside our partner PwC, we discussed the top use cases that we’re seeing deliver real, tangible value to our customers:

  1. Biomarker Discovery – Use AI agents to mine literature and first-party data to accelerate biomarker discovery and qualification.
  2. Regulatory Amendment Analysis – Reduce costly protocol amendments through AI analysis of regulatory feedback databases.
  3. Clinical Trial Analysis – Leverage AI-guided exploration of clinical trial datasets to unlock deeper insights, faster.
  4. MES (Manufacturing Execution System) Chat – Use natural language processing (NLP) to extract actionable insights from manufacturing execution systems—enabling quicker, better-informed decisions.
  5. Sales Concierge – Support commercial teams with AI tools that surface timely, relevant data and identify key risks in the field.
  6. 340B Fraud – Detect potential fraud in the 340B drug pricing program with AI-driven anomaly detection.
  7. RWD (Real-World Data) Chat – Unlock enterprise-wide insight from RWD using intuitive natural language interfaces.
  8. Protocol Generation – Accelerate clinical trial design with generative AI–assisted protocol drafting.
  9. AI Assisted Large Molecule Design – Enhance discovery pipelines with AI models that help design complex biologics more efficiently.
  10. Enterprise Knowledge – Break down organizational silos by enabling employees to ask natural language questions and uncover cross-functional insights.

These use cases aren’t just pilots or hypotheticals—they’re in production and already delivering measurable outcomes.

This brings us to another key theme – creating easier, faster onramps for our customers so they can get started today.

With 9 out of the top 10 global biopharma companies already using AWS for generative AI, we’re seeing a clear pattern: certain high-impact use cases come up again and again. To help our customers get started faster, we’ve created the Life Sciences Generative AI Portal on AWS – a curated catalog of 40+ generative AI demos and customizable prototypes that are delivering value for life sciences customers today. Whether you’re focused on R&D, clinical development, manufacturing, or commercial operations, the portal offers proven solutions to common challenges. Your AWS representative can help you find the right solution for your needs.

Another exciting step in making AI more accessible to our customers is our new collaboration with Latent Labs, designed to democratize access to leading generative AI tools. This partnership will help life sciences organizations accelerate the discovery and development of breakthrough therapies—by bringing advanced capabilities directly into the hands of scientists, clinicians, and business teams.

Agents of Change: Agentic AI is transforming Life Sciences

The title says it all. Throughout Dan Sheeran’s keynote—and echoed across many customer breakout sessions—agentic AI was center stage.

One of the most compelling examples came from John Marioni at Genentech, who shared how they’re using agentic AI capabilities on AWS to democratize and automate scientific research. The impact is real and immediate: one distinguished scientist was able to perform a complex analysis of cell surface receptors in under 10 minutes—a task that previously took two days.

We also heard a powerful example from Thomas Fuchs, Chief AI Officer at Eli Lilly and Company, who showcased how agentic systems are already enhancing scientific discovery and decision-making.

Back to the theme of making it easier to get started—last week, we introduced an open-source agentic AI toolkit, designed to accelerate adoption and development across the healthcare and life sciences industry.

The toolkit, built on Amazon Bedrock, hosts a growing catalog of starter agents purpose-built for healthcare and life sciences use cases, and encompasses supervisor agents that can complete multi-agent workflows. It also includes a developer-facing UI component that can be used to securely assemble, test, and demonstrate multi-agent workflows within the organization’s VPC, helping to bridge the vision gap between IT and functional leads when designing agentic solutions.

The toolkit currently offers starter agents for common life sciences use cases including:

  • Research Agents – For target identification, biomarker discovery, literature search, and experimental design
  • Clinical Agents – To support clinical trial analysis, protocol optimization, and patient stratification
  • Commercial Agents – For competitive intelligence and market insights generation

The toolkit also offers agents designed in collaboration with industry leaders. For example Wiley, one of the world’s largest publishers and a trusted leader in research, released a new agent able to search full-text articles published under the creative commons license, such as Cancer Medicine. This delivers reliable and cited insights in minutes, rather than the current hours- to days-long manual process of discovering and perusing dozens of articles for relevant information.

These pre-built agents serve as a foundation, saving developers and subject matter experts valuable time by eliminating the need to build agents from scratch. Individual agents can be dynamically attached to a multi-agent supervisor by using the multi-agent collaboration capability on Amazon Bedrock. This enables developers to orchestrate across agents to build new agentic workflows.

Dan Sheeran, GM Healthcare and Life Sciences, presenting the AWS Healthcare and Life Sciences Agent Toolkit

Technology Is Moving Fast—We Help You Move Faster

The pace of innovation over the past year has been nothing short of extraordinary—and it’s only accelerating. As new breakthroughs in generative and agentic AI continue to emerge, one thing remains constant: the need for a scalable, secure data foundation.

This foundation is what enables you to adapt, evolve, and keep building—no matter what the next big advancement brings.

To all of our partners, customers, and attendees: thank you for being part of this journey with us. We’re excited about what we’ve accomplished together—and even more excited about what’s next.

Discover more AWS Partners or contact an AWS Representative to learn how we can help accelerate your business.

AWS Keynote speakers

Further reading

Dan Sheeran

Dan Sheeran

Dan leads AWS' Healthcare and Life Sciences Industry Business Unit (HCLS IBU), which supports all AWS customers in Life Sciences, Medical Devices, Payors, Data Services and Healthcare ISVs and OEMs. The HCLS IBU helps customers leverage AWS cloud and machine learning services, and solutions from AWS Partners, to discover and develop new therapies, diagnostics and devices, and to deliver healthcare more efficiently with improved patient outcomes. Prior to joining AWS in 2019 Dan founded and led two digital health startups focused on telehealth and machine learning for chronic disease prevention and management. Dan lives in the Seattle area. He has an MBA from Northwestern University and BS from Georgetown University.

Stephanie Dattoli

Stephanie Dattoli

Stephanie Dattoli is the Worldwide Head of Life Sciences and Genomics Marketing at Amazon Web Services (AWS). Specialized at the intersection of life sciences and cloud technology, Stephanie has spent the last decade helping leading life sciences organizations bring new products to market and expand their market reach. She holds a graduate certificate in genetics from Stanford University, in addition to dual undergraduate degrees in business and strategic marketing.