Rainmaker, Model N’s flagship annual conference focused on revenue management, brought together top leaders and innovators across life sciences and high-tech earlier this year. The event highlighted cutting-edge strategies and technologies that are driving transformation, offering insights into the future of revenue optimization and compliance.
A highlight of the event was the keynote by Ujjwal Ratan, Artificial Intelligence (AI), Machine Learning (ML) and Data Science Leader for Healthcare and Life Sciences at Amazon Web Services (AWS), who shared insights on the future of AI in life sciences. This blog explores key takeaways from his talk and their impact on the role of AI in the life sciences industry.
The AI Revolution in Pharma
Generative AI continues to evolve rapidly, transforming industries and driving tangible results. “GenAI has advanced quickly over the past few years, moving from proofs of concept in 2023 to scaled production in 2024, and now, in 2025, delivering real business value,” said Ratan. “This is no longer about experimentation – it’s about delivering measurable outcomes.” He reinforced that in life sciences, this evolution is accelerating innovation across drug discovery, clinical trials, and commercialization.
During his presentation, Ratan highlighted use cases where AI enables breakthroughs and operational efficiencies that were out of reach only a few years ago.
Laying the Groundwork: Data Quality
At the forefront of AI innovation, data remains the key to unlocking its full potential. During his discussion, Ratan highlighted an important point: “AI is only as smart as the data you give it”. He emphasized the necessity of breaking down data silos and creating a strong, unified foundation. “Having cost-effective ways to store and harmonize a variety of data types is critical for scaling generative AI, especially in highly regulated industries like pharma.”
This philosophy echoes Model N’s focus on data analytics, supporting life sciences organizations with harmonized, accessible information for better compliance and decision-making.
Building Custom AI for Precision and Compliance
Ratan emphasized that AI customization is critical, stating, “off-the-shelf AI models do not deliver the accuracy or regulatory compliance pharma requires without focusing on customizations ranging from prompt engineering all the way to fine tuning or pretraining.” He explained that while generic models with straightforward prompting can provide a baseline, they lack the specificity needed for complex and highly regulated industries like pharmaceuticals. He highlighted advances such as fine-tuning on proprietary data and retrieval augmented generation (RAG), which allows for tailored predictions, improved accuracy, greater explainability, and reduced hallucinations—key elements for meeting FDA requirements.
These tailored customizations are vital for addressing sensitive use cases, from optimizing commercial strategies to ensuring compliance with strict industry standards.
Strategies for Real-World AI Success
When implementing AI within an organization, Ratan underscored that having a clear strategy is essential. Ujjwal emphasized the importance of starting with well-defined business objectives and fostering a culture of experimentation. Equally critical is ensuring that teams remain informed and involved throughout the process.
He highlighted the need to establish strong governance frameworks and approach AI as a tool to complement, not replace, human expertise. Ratan also reminded the audience that the AI journey is still in its early stages, encouraging flexibility, adaptability, and a commitment to continuous learning as key drivers for long-term success.
The Model N Advantage
Ratan’s keynote underscored why now is the time for pharma to fully embrace generative AI—and why choosing experienced partners matters.
At Model N, we’re embedding AI across our product portfolio and operations to drive transformative results. Our investments in GenAI have revolutionized R&D, enabling smarter code generation, automated testing, enhanced support, and more. These AI advancements are unlocking R&D productivity at scale. We’re focusing on integrating agentic AI capabilities to intelligently automate common multi-touch workflows for our life sciences customers.
Equally important is the power of trusted, data-driven insights. With solutions like Data nSights, we’re turning revenue management data into actionable intelligence, empowering customers to make informed decisions and maximize value.
With Model N’s compliance-driven solutions and deep life sciences expertise, organizations can accelerate innovation, enhance productivity, and meet global regulatory demands.
Contact Model N today to learn more.
