AWS for Industries
Highlights from the 2025 AWS Life Sciences Symposium’s Manufacturing Track
More than 1,000 life sciences leaders from over 400 organizations joined Amazon Web Services (AWS) in New York City on May 6, 2025 for the seventh Annual AWS Life Sciences Symposium. They were united by a bold theme: “Building for Breakthroughs – AI-powered Innovations Transforming the Pharmaceutical Value Chain.” Across 26 sessions, featuring insights by 39 industry pioneers, the event spotlighted how advanced AI is driving step-change innovation, accelerating the development of life-saving therapies, reducing costs, and doing so with security and compliance at the core. Catch the highlights from the plenary keynote and learn more about the key announcements that were made around the use of agentic AI for life sciences.
With more than two billion people worldwide managing chronic conditions and over 300 million affected by rare diseases, the urgency for pharma innovation across the value chain has never been greater. Rising regulatory pressures, including those from the U.S. Inflation Reduction Act, only intensifies the call for bringing in step-ladder changes in drug manufacturing and supply chain operations.
That’s why this year’s manufacturing breakout track—curated for operations leaders from the world’s leading pharmaceutical companies—spotlighted real-world, in-action innovations. These modernizations are built by forward-thinking organizations leveraging AWS to address some of the industry’s most pressing challenges.
The goal was simple: create a collaborative platform for leaders to share, learn, and take home tangible advancements that can enable faster, more reliable delivery of life-saving therapies. All achievable while facilitating improved scalability, reduced costs, and building greater resilience across the value chain.
Here were the main takeaways.
Unlocking production and supply chain breakthroughs with Agentic AI
Since generative AI surged into the spotlight in 2023, there’s been a lot of excitement around its ability to transform every stage of the pharmaceutical production lifecycle—from optimizing batch processes and reducing downtime to automating SOPs and converting manual workflows into agile, self-learning systems. Yet despite this promise, the path to scaled adoption remains uneven across organizations. Bridging that gap often requires a shift in perspective— that’s exactly what the opening session, “Learning from Amazon,” delivered. It was a powerful dose of inspiration and a fresh lens on what’s possible when AI, robotics, and digital infrastructure are applied at scale.
Drawing on decades of operational excellence in retail and fulfillment, the session by Amazon Retail offered a behind-the-scenes look at how Amazon Retail uses AI to power everything from real-time demand forecasting and inventory optimization to vendor management, computer vision-based quality control, and last-mile distribution. The session laid out a practical, step-by-step framework that life sciences leaders could adapt to their own complex challenges—highlighting proven, tech-driven strategies that deliver results.
In the second half of the session, the spotlight shifted to the shop floor, where attendees explored how AI agents are already boosting productivity, enhancing defect detection, reducing training time, and reimagining core operations—transforming maintenance workflows to MES (Manufacturing Execution Systems), and even the production meeting itself.
The message was clear: with the right tools and mindset, transformation is not just possible—it’s within reach.
Redefining autonomous pharmaceutical manufacturing with cutting-edge digital technologies
Attendees next heard from Dave Tudor, Managing Director of the Medicines Manufacturing Innovation Centre. He took the stage to outline a transformative roadmap for the future of pharmaceutical manufacturing—one powered by AI, cutting-edge digital technologies, sustainable practices, and deep cross-sector collaboration. Tudor shared how his organization is tackling some of the industry’s toughest challenges by reimagining four critical areas of production:
- Developing a digitally twinned Continuous Direct Compression (CDC) platform to enable faster, more energy-efficient oral solid dose manufacturing;
- Enabling just-in-time, automated clinical trial drug production to eliminate delays and reduce waste;
- Pioneering sustainable, scalable methods for oligonucleotide production to unlock therapies for currently untreatable diseases; and
- Accelerating the adoption of machine learning, AI, and robotics to drive pharma manufacturing 4.0.
At the heart of this vision is a decentralized, autonomous factory model, underpinned by a unified data foundation built on AWS. This foundation integrates data across machines, sites, and operators, streamlines pipelines for data curation and harmonization, enables the creation of consumable datasets, and democratizes the creation of reusable data products. With this strong underlying foundation in place, the Centre is helping leading pharma organizations reduce development risk and accelerate time to market, equipping the industry to deliver with greater efficiency, agility, and precision, and thus ushering in a new era of lights-out, intelligent manufacturing.
Unlocking pharmaceutical manufacturing quality control with AI
Behind every dose of medicine is a person—a parent, a healthcare worker, a friend, a child—who depends on its safety and effectiveness. The session by Michael Morelli, Vice President of Platforms & Products – AI/ML, Data & Analytics at Pfizer, highlighted how this responsibility drives innovation at scale across Pfizer manufacturing. He introduced the “QC Sidekick,” a generative AI-powered companion app designed to elevate quality and safety in Pfizer’s QC (Quality Control) labs.
Built on the robust data infrastructure of AWS and powered by Amazon Bedrock, the QC Sidekick helps lab analysts reduce laboratory incident reports (LIRs), and identify root causes. It also prevents repeat issues through intuitive data visualization, trend analysis, and quick access to SOPs and method IDs. The result? Fewer errors, faster decision-making, and a safer, more efficient lab environment—contributing directly to Pfizer’s broader goal of margin acceleration by reducing product defects and costly batch rejections. Already being scaled across English-language manufacturing sites worldwide, the QC Sidekick is a solid reflection of Pfizer’s nearly 175 years of manufacturing excellence—now infused with the power of generative AI.
Unlocking visibility from shop-floor to top-floor
“What gets measured gets improved.” That principle was brought vividly to life in Merck’s session, led by Wincy Fung, AVP of the Digital Services Organization in Merck’s Manufacturing Division, and Ram Silai, Executive Director of Data Platform and Products. Spotlighting Merck’s Visual Factory initiative, the session demonstrated how the company is standardizing work and driving accountability across its manufacturing operations through the PSQDC (People, Safety, Quality, Delivery, and Cost) framework—delivering real-time visibility from the shop floor to the top floor.
Built on “Mantis”—Merck’s GMP-qualified, centralized data platform powered by AWS that integrates data from over 120 systems— the platform offers tailored analytics and dashboards, and access to customized insights. Shop-floor teams track metrics like overall equipment effectiveness (OEE) in real time. Site leaders get a comprehensive daily performance view, and executives gain standardized, network-wide insights into supply chain performance. The result is a powerful shift toward radical transparency, faster decision-making, and a unified, data-driven culture across Merck’s global operations.
Looking ahead, Merck is working to scale these solutions enterprise-wide, fully standardize KPIs across all levels, and infuse generative AI to accelerate root cause analysis and smarter, faster decision-making.
From Reactive to Predictive: Transforming product complaint reporting with AI
Despite rigorous quality controls and KPIs tracking, pharmaceutical and medical device companies still face costly product complaints and deviations. They spend an average of $15 million annually, with complaints taking up to 20 days to resolve and root cause identification stretching to 30 days—with an alarming 89 percent error rate in accurately finding those causes. This inefficiency stems largely from manual, resource-intensive processes and a lack of visibility into historical data.
To address this, PwC showcased their AI-powered Quality Management System (QMS) Toolkit, built on Amazon Bedrock, which automates the detection, classification, and resolution of complaints and deviations. The toolkit integrates seamlessly with any pharma quality management system, and offers features such as complaint detection from narratives, AI-generated investigation summaries, automated deviation report grading, and global trending dashboards powered by a generative AI chatbot.
The impact is significant: for one top pharmaceutical company, the toolkit reduced complaint resolution time from 20 days to just one, and accelerating root cause identification from 30 days to minutes, thus saving over 100 full-time equivalents (FTEs) annually, resulting in substantial cost savings and greater operational efficiency.
The new era of life sciences is here
As generative and agentic AI continue to reshape what’s possible, it is apparent: the new era of life sciences innovation is here.
As organizations are moving beyond experimentation and into scaled, real-world impact, the foundation for long-term success is beyond just technology—it’s the intersection of people, process, and data. Whether you’re driving innovation in R&D, clinical development, manufacturing, or commercial, AWS is here to help you scale what works and help you lead with confidence into what’s next.
Contact an AWS Representative today, and see why 9 out of the top 10 pharma companies globally choose AWS for generative AI and machine learning. The future is being built today—let’s build it together.
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 Commercialization 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