January 06, 2026
Blog
Corporate
What does responsible AI look like
in health care?
Rene Rancourt
Vice President, Global Digital Business Transformation
The hype around artificial intelligence (AI) in healthcare is impossible to ignore. Every week brings new headlines about algorithms that can diagnose diseases or predict patient outcomes. But most of these headlines miss the point.
AI is a tool. And like any tool, it's only as good as the foundation it's built on and the people who use it. At Eisai Inc., we’ve had many cross-functional and cross-regional discussions about how to approach and implement AI responsibly in a pharmaceutical company. We realize that the key to our success in this area will be less to do with algorithms and more to do with learning, adapting, and improving for the benefit of human health.
The cross-functional perspective that my role as Vice President of Information Technology for Eisai’s Americas region and Global Data & Analytics lead has shaped how I think about AI. It's a strategic enabler that touches every part of our business, from drug discovery to omnichannel marketing.
Exploring the use of AI in the pharmaceutical industry has been an exciting journey, one of unexpected twists and surprises. In the spirit of collaboration, I want to share what we've learned, not only my perspective, but also that of two colleagues leading AI transformation in their areas of responsibility: Janna Hutz, Head of Neurology Discovery at Eisai and President of Eisai’s Center for Genetics Guided Dementia Discovery (G2D2), and Vijay Venugopal Iyengar, Executive Director who leads Digital Business Transformation for our US business.
Foundation First
When people talk about AI, they jump straight to the shiny stuff: the models, the predictions, the automation. But that's backwards.
Real work happens before you train a model. It happens in the unglamorous world of data infrastructure, governance frameworks, and organizational readiness. If you don't get that foundation right, your AI initiatives will fail.
When creating the building blocks of an AI program, it’s important to keep in mind centralized data storage, governance, harmonized standards, and FAIR principles: Findable, Accessible, Interoperable, Reusable to ensure consistency across clinical, manufacturing, and commercial domains. Without clean, well-governed data, even sophisticated algorithms will fail.
Here's something I tell my peers: data readiness isn't an IT issue. It's a business enabler.
The other foundational element is governance. In healthcare, we are responsible for sensitive information about patients and HCPs. The stakes are high. At Eisai, we formed an AI Governance Committee to assess risks around regulation compliance, privacy, security, and bias. When it comes to critical decisions, human oversight remains essential. A person should always review outputs for alignment with ethical standards and other factors, and have the final say. This partnership between machine intelligence and human expertise is what makes AI truly transformative.
AI in Action: Three Perspectives
Drug Discovery: Speed Meets Scientific Rigor
Scientists like Janna Hutz, President of G2D2, and head of our Neurology Discovery team, have high standards for AI, and they should. When you're developing molecules that could become medicines, you can't blindly trust a black box.
Recently, Janna's team collaborated with scientists across Eisai’s global discovery sites to use AI modeling to test one million potential new drugs, narrowing them down to a list of 296 hits in just weeks. A process that, when performed traditionally, would have taken months or years. But they didn't just trust the algorithm. They validated it.
As Janna puts it, "We're looking for opportunities to leverage AI, and as scientists, we are tough customers when it comes to technology claiming intelligence. We not only want, but need, to understand: is AI going to perform well for us and our research? Can we be sure it’s not going to miss the next breakthrough treatment? For that reason, we are most excited about applications of AI where we can quickly and definitively validate the models’ suggestions in the lab."
The compound screening example is just one model. Neurology drug discovery projects across Eisai are also benefiting from deployment of models to optimize chemical properties, automate image analysis, and predict cellular outcomes to treatment. It's about giving scientists better tools, not replacing their judgment.
Omnichannel Marketing: From Campaigns to Conversations
Vijay Venugopal Iyengar leads Global Digital Business Transformation in the US. His focus is different from Janna's, but the principles are the same: AI must serve a clear purpose, and that purpose must be person-centered.
His team’s incorporation of AI is anchored in two strategic imperatives: unlocking data-driven insights and achieving precision in reaching the right audience at the right moment.
As Vijay explains, "This is not just about technology. It's about our responsibility as a human health care (hhc) company to serve real people navigating diseases, delivering not only treatments, but also information to health care providers treating those patients, when and where that information will provide the most impact in the patient journey. At Eisai, we are focused on patients and their families, committed to increasing the benefits that health care provides to them."
AI helps to personalize touchpoints across the patient journey by sequencing and synchronizing messaging across platforms and turning fragmented touchpoints into coherent narratives. We call this the “Right Next Engagement.”
Vijay shared: "Return on investment in AI-driven marketing cannot be boiled down to a single number. We look at pivotal moments that alter the customer journey - because when insight turns into empathy, marketing becomes less about conversions and more about conversations.”
His team tracks engagement using AI and data analytics to measure the positive change, or increase, in how effectively a marketing campaign connects with its target audience compared to previous benchmarks. But the real measure is qualitative: an HCP responding and internalizing that information in a way that can benefit the patient and ease their journey.
“We plant the seed with AI insights, but success is watching it bloom.”
The Common Thread: Prove It, Then Scale It
Whether it's Janna's work in research or Vijay's work in the commercial organization, the pattern is the same. We pilot. We learn. We prove value. Then we scale. We shift the conversation from “look what we built” to “look who we helped.”
This phased approach reduces risk and builds organizational confidence. When teams see that AI works and may help advance research and our hhc mission, they become advocates. That cultural shift enables scaling.
AI in healthcare carries unique responsibility. We're serving patients and families navigating disease. At Eisai, our mission is human health care (hhc) and everything we do must align with that concept. That means using AI to accelerate development timelines and enhance operational efficiency. It also means never losing sight of the people on the other side of our decisions. That's the challenge we're working on every day at Eisai. It's the most exciting work I've done in my career.
US5748 © Eisai Inc. December 2025
