Health Tech: Sivasai Nadella Of Amgen On How Their Technology Can Make An Important Impact On Our Overall Wellness

In recent years, Big Tech has gotten a bad rep. But of course many tech companies are doing important work making monumental positive changes to society, health, and the environment. To highlight these, we started a new interview series about “Technology Making An Important Positive Social Impact”. We are interviewing leaders of tech companies who are creating or have created a tech product that is helping to make a positive change in people’s lives or the environment. As a part of this series, I had the pleasure of interviewing Sivasai Nadella.

Sivasai Nadella is an enterprise solution architect, technology strategist, and revenue systems leader, currently serving as a Specialist Software Engineer at Amgen. He drives digital transformation across global commercial operations, architecting Customer Relationship Management (CRM), Configure Pricing Quote (CPQ) Contract Life cycle management (CLM), Artificial intelligence/Machine Learning (AI/ML), event-driven, and cloud-based solutions that support more than 10 million patients worldwide. With over a decade of experience at Amgen, Atos, and Cognizant, he has delivered high-impact platforms that enhance compliance, accelerate revenue cycles, and bridge business strategy with modern technology.

Thank you so much for joining us in this interview series. Before we dive in, our readers would love to learn a bit more about you. Can you tell us a bit about your childhood backstory and how you grew up?

I am from a farming background family and grew up in a small town; my siblings and I were the first generation in our extended family to go to school. We didn’t have many resources, and technology simply wasn’t a part of our daily life — computers were something I only saw much later. At school, I dreamed of becoming a doctor, but financial constraints led me to explore other paths and sparked my interest in technology during high school. My parents always believed in the power of education and encouraged us to do well in school so we might have opportunities beyond our small town.

When I got to college, the fact that I had never used a computer made those first classes really intimidating. Still, I chose to study computer science because I could see how transformative technology was becoming and I wanted to be part of it. I spent a lot of late nights in the computer lab trying to catch up.

That effort eventually paid off: I landed a job that took me abroad, which was a huge step for someone from my background. Much of my career has involved working with pharmaceutical clients and a biotech company, allowing me to indirectly serve patients by supporting the systems and tools that drive medical innovation. Seeing new places and cultures broadened my horizons and it also showed others in my hometown what education could make possible.

Can you share the most interesting story that happened to you since you began your career?

At the start of my career, I was part of a large team supporting pre-clinical applications for a pharmaceutical client. As a junior developer, I was eager to contribute, but it took six months before I received my first development assignment — adjusting a calculation in a lab application used to determine drug dosage for rodents in early research studies. What seemed like a small task at the time actually had a big impact: it improved accuracy in dosage results, which supported more reliable experiments and, ultimately, better decision-making in the drug development process. That moment not only gave me visibility within the team but also showed me how even small pieces of code can contribute to something much larger — helping advance science and patient care. From there, I built on that momentum by driving automation, fine-tuning databases, and streamlining business processes, all with the same goal of saving time, reducing errors, and improving outcomes.

None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful towards who helped get you to where you are? Can you share a story about that?

I’m deeply grateful to the many people who have helped shape my journey, and I appreciate the chance to acknowledge them here. I’ve found that most of us never succeed entirely on our own; it usually takes a village of mentors, peers and loved ones who believe in us. In my case, it all starts with my parents and family, who instilled in me a sense of responsibility and a positive outlook. They taught me to see each challenge as an opportunity to learn and to give back, a mindset that has been my foundation ever since.

In my first job, a thoughtful team lead introduced me to the world of technology. We would spend hours discussing not just what we were doing, but why we were doing it; his mentorship sparked my curiosity and set me on a path of continuous learning. Later, when my career took me abroad, I found myself fortunate enough to work with three remarkable individuals who continue to influence me. My manager showed me how to think from a business perspective and encouraged me to share innovative solutions rather than keeping them to myself. A talented technical lead guided me through designing and architecting robust systems, teaching me how to approach problems methodically and creatively. And a client-side counterpart, with whom I collaborated closely, taught me the art of building lasting customer relationships. Together, we navigated evolving technologies and worked side by side to stay ahead of the curve.

Last but not least, I owe a great deal to my life partner, whose unwavering support has been a constant source of strength. She believes in my dreams as deeply as I do and encourages me to pursue them wholeheartedly. Whether I’m celebrating a success or facing a setback, she’s right there, reminding me why I started and helping me keep my aspirations in sight. It’s because of all these people, each playing a unique role, that I am where I am today.

Can you please give us your favorite “Life Lesson Quote”? Can you share how that was relevant to you in your life?

When asked about a “life lesson” that guides me, I always think of something Dr. A.P.J. Abdul Kalam said: “All of us do not have equal talent. But all of us have an equal opportunity to develop our talents.” That line resonated because I’ve often felt like I wasn’t the most naturally gifted person in the room. Early on, that was discouraging, but over time I realized the real difference lies in how much effort you’re willing to invest in yourself.

My path into technology is a good example. I started out working with Microsoft tools, then branched into other technologies as new languages and systems sparked my curiosity. I never felt like a prodigy, so I leaned on discipline and consistency to close my gaps. Even with the rapid rise of AI today, I try to see emerging tools as chances to grow ethically and responsibly rather than things to fear.

One of the best habits I’ve developed is to avoid comparing myself to others. Instead, I look at who I was yesterday and ask whether I’ve improved and grown today. Focusing on my own progress instead of on other people has turned many weaknesses into strengths. Dr. Kalam’s words remind me that opportunity is the true equalizer; if I keep pushing myself to learn, there really is no limit to what I can achieve.

You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?

Looking back on my journey as a technologist and leader, I’ve found that a few core traits have consistently guided my success and shaped the way I approach challenges — curiosity, cross-disciplinary thinking, comfort with failure

A deep sense of curiosity: For me, everything starts with curiosity. I’ve always been the person who can’t help but ask “Why does this work like that?” or “What would happen if we tried it another way?” That habit has taken me on some interesting detours — like picking up a new programming language just to see how it handles multitasking, or spending an evening buried in a paper about machine-learning ethics because I wanted to apply those ideas to a healthcare project I was working on. Following those impulses doesn’t always produce immediate results, but it almost always pays off later when a seemingly random insight turns out to be exactly what I need.

cross-disciplinary thinking : has been a game-changer in my career. I’ve learned that big breakthroughs rarely come from working in isolation — they happen when ideas from different fields collide. Some of my proudest moments came from pulling together people who normally wouldn’t sit at the same table: like teaming up a data scientist with a field rep to design a predictive tool, or collaborating with user-experience researchers to make sure an algorithm actually worked for the people using it.

One example that stands out was a project where a team of data engineers was struggling to make sense of healthcare providers’ expense reports. We crunched the numbers every which way, but something just wasn’t adding up. It wasn’t until I brought in a compliance specialist that the picture became clear — she was able to connect the dots and interpret the data in a way that made sense within regulatory frameworks. That experience reinforced for me that no matter how skilled we are, we won’t always find the right answers until we bring in the right voices. True progress happens when you invite diverse expertise into the conversation.

Comfort with failure and iteration: is a trait I believe everyone needs, no matter the field. I’ve always looked at failure as a stepping stone to success — it’s not the end of the road, but a chance to pause, learn, and try again with more wisdom. Over the years, I’ve built projects that never made it past testing and written code that ended up in the recycle bin. At first, it’s easy to feel like all that effort was wasted, but I’ve come to see those moments differently. Every failed attempt shows me what doesn’t work, challenges the assumptions I made, and often uncovers problems I hadn’t even considered. That mindset — treating setbacks as lessons and staying willing to iterate — has been what pushes my projects forward. In fact, many of the breakthroughs I’m most proud of only happened because I was willing to fail, adapt, and keep going.

Ok super. Let’s now shift to the main part of our discussion about the technology or medical devices that you are helping to create that can make a positive impact on our wellness. To begin, which particular problems are you aiming to solve?

The problems I’m most focused on solving sit at the intersection of healthcare and technology. In my experience, patients, providers, and researchers all struggle with similar challenges: too much data, not enough clarity, and too many manual processes slowing things down. For patients, that might look like delayed access to treatments; for providers, it’s long hours spent on administrative tasks instead of direct care; and for researchers, it’s the inefficiency of piecing together insights from siloed systems.

One of the most striking examples is in drug discovery within biotech. Traditionally, it takes 10–15 years and billions of dollars to bring a new therapy from the lab to the patient. A huge part of that time is spent running experiments, analyzing clinical trial data, and testing countless variations of molecules to see which ones are safe and effective. It’s an incredibly resource-intensive process, and every delay means patients are left waiting — sometimes for treatments that could save or extend their lives.

This is where AI can make a real difference. Machine learning models can rapidly analyze molecular structures to predict which compounds are most promising before they even enter the lab. Natural language processing can sift through vast amounts of medical literature and trial data to uncover patterns that might take humans years to spot. Predictive analytics can help researchers design smarter trials — identifying the right patient populations, flagging risks earlier, and reducing the number of failed attempts. In short, AI doesn’t just speed up the process; it increases the odds of success by focusing resources where they’ll have the most impact.

I’ve had the chance to see this potential up close in one of my roles, where I led AI/ML proof-of-concepts focused on predictive analytics for revenue and contract systems. On the surface, those projects were about commercial operations — things like forecasting risks and helping leaders make smarter decisions — but what struck me was how transferable the principles are to research and development. At the core, it’s about using data responsibly to guide better, faster decisions.

What keeps me inspired is the human side of that impact. If AI can shave even a few years off the drug discovery process, it could mean a cancer treatment reaching a patient in time to change the course of their illness. It could mean a life-saving therapy for a rare disease finally becoming available to families who have been waiting and hoping for answers. That’s the true promise of AI in healthcare — not just efficiency, but giving people a real chance when time matters most.

How do you think your technology can address this?

I believe technology, especially AI, can help address these challenges by turning mountains of complex data into clear, actionable insights. In healthcare and biotech, the information we need is often there — it’s just buried in clinical trial results, medical records, or research papers that no one human could possibly keep up with. AI can process that data at incredible speed, highlight patterns, and suggest the next best steps in a way that helps researchers, providers, and patients alike.

For example, in drug discovery, AI can screen millions of molecular structures in weeks instead of years, predicting which ones are most likely to succeed in trials. In clinical development, it can identify the right patient populations faster and flag potential risks earlier, which not only shortens timelines but also makes studies safer and more inclusive. And in everyday patient care, AI-driven tools can help doctors make quicker, more accurate decisions by presenting the right data at the right time.

Can you tell us the backstory about what inspired you to originally feel passionate about this cause?

My passion for this cause goes all the way back to my roots. I grew up in a small farming town where access to healthcare was limited and resources were scarce. As a child, I often saw families in my community travel long distances just to see a doctor or wait weeks for even the most basic test results. Sadly, even today there are still many places where reaching basic healthcare takes far too long. What stayed with me was the realization that people weren’t suffering because treatments didn’t exist — they were suffering because the systems to deliver those treatments were too slow or too far out of reach.

When I began my career working with pharmaceutical and biotech companies, I saw the same problem play out on a much larger scale. Even in advanced healthcare systems, it can take more than a decade and billions of dollars to bring a new drug from the lab to the patient. Seeing those two worlds — my hometown’s struggles and the global challenges of drug development — sparked a passion in me. It made me determined to use technology to help close those gaps, shorten timelines, and make sure patients don’t have to wait years for something that could change, or even save, their lives.

How do you think this might change the world?

I believe this has the power to change the world by making healthcare more timely, equitable, and humane. Imagine if instead of waiting 10–15 years for a new drug to be developed, patients could access breakthrough therapies in half that time — or less. That kind of shift doesn’t just save money; it saves lives. A cancer patient might get the right treatment in time to change the course of their illness. Families facing rare diseases, who often feel forgotten, could finally see therapies become available in their lifetime.

Beyond patients, this kind of transformation could ripple out into society. Faster, more efficient drug discovery could lower healthcare costs, reduce the strain on providers, and make cutting-edge treatments available not just in the wealthiest hospitals but in rural clinics and underserved communities around the world. To me, that’s the real promise: using technology not just to advance medicine, but to level the playing field — so that where you live, or how much money you have, doesn’t decide the quality of care you receive.

Keeping “Black Mirror” and the “Law of Unintended Consequences” in mind, can you see any potential drawbacks about this technology that people should think more deeply about?

I think it’s important to acknowledge that with any powerful technology, especially AI in healthcare, there are potential drawbacks we need to keep front of mind. One concern is bias in data — if the information we train AI models on doesn’t represent all populations fairly, we risk creating tools that work better for some groups of patients than others. That could unintentionally widen the very gaps we’re trying to close.

There are also privacy and trust issues. Healthcare data is deeply personal, and if patients don’t feel confident their information is being handled securely, they may be hesitant to participate in research or use AI-enabled tools. On the provider side, there’s a risk of over-reliance on algorithms. AI should support doctors and researchers, not replace their judgment. We need to ensure there’s transparency in how these systems make decisions so that clinicians can trust — and challenge — them when needed.

Finally, there’s the “law of unintended consequences.” For example, if AI speeds up drug discovery dramatically, it could shift pressure onto regulatory systems or create new bottlenecks we aren’t yet prepared for. Or if AI tools make predictions too well, they could be misused commercially in ways that prioritize profits over patients.

To me, the answer isn’t to slow down innovation, but to build guardrails early: diverse and ethical data practices, strong governance, clear accountability, and a constant reminder that people — not algorithms — should remain at the center of healthcare.

Based on your experience and success, can you please share “Five things you need to know to successfully create technology that can make a positive social impact”?

Creating technology with a positive social impact is very different from just building something new. Over the years, working in biotech, healthcare, and enterprise systems, I’ve learned that the real breakthroughs don’t come from the technology itself — they come from how thoughtfully we design, test, and apply it. Along the way, I’ve made mistakes, learned from mentors, and seen projects succeed or fail depending on whether we kept people at the center.

Based on those experiences, here are five lessons I believe are essential for anyone who wants to create technology that truly makes a difference — each one shaped by real stories from my own journey.

1 . Start with the real problem, not the technology.

It’s easy to get caught up in the excitement of the latest tool, but true impact begins with understanding the actual need. In one project, our team thought automation would be the answer to improving a healthcare workflow. But when we spent time with field reps, we discovered their real challenge wasn’t automation at all — it was correctly mapping customers to the right territories. We redesigned the integration so customers were aligned properly, and suddenly their work became much easier. Field reps could focus on serving and prioritizing customers instead of fighting the system, which created far more value than the fancy solution we originally imagined.

2 . Build with diverse perspectives.

Great solutions don’t come from a single discipline. Some of my biggest breakthroughs came when I brought unlikely people together — like pairing a data scientist with a compliance specialist who was able to explain why the numbers weren’t adding up in a provider expense report. It was her perspective that connected the dots and unlocked the solution. Without that mix of expertise, we might never have solved it.

3 . Make ethics and trust non-negotiable.

Especially in healthcare, data is personal. In a proof-of-concept I led around AI/ML contract analytics, we built encryption and audit features into the architecture from the start rather than bolting them on later. It slowed us down a little at first, but it built trust with stakeholders and meant the solution could actually scale in a highly regulated biotech environment.

4 . Embrace iteration — and don’t fear failure.

I’ve built projects that never made it past testing and written code that went straight into the trash bin. Early on that felt like wasted effort, but I learned that every failure teaches something: what doesn’t work, what assumptions were wrong, or where the real challenge lies. One prototype for revenue forecasting failed its first round of testing, but after multiple iterations and feedback loops, it evolved into a model leaders could actually use to make confident decisions.

5 . Plan for access and sustainability.

If technology only works in perfect conditions, it’s not really making an impact. I’ve seen solutions built for big hospitals with high-end systems, while smaller clinics or rural areas were left out. That doesn’t feel like progress to me. The real test is whether a tool can run on simpler devices, work even with spotty internet, and be easy enough for anyone to use without weeks of training. When you design with those realities in mind, the technology doesn’t just help a few — it reaches the people and places that need it most, and that’s what makes the impact truly sustainable.

If you could tell other young people one thing about why they should consider making a positive impact on our environment or society, like you, what would you tell them?

If I could tell young people one thing, it would be this: don’t underestimate the impact of small steps. You don’t need to have all the answers or build the next world-changing invention overnight. Start by looking around you — at your community, your workplace, or your school — and ask, “What problem here could I help make a little better?” For me, that mindset grew from watching people in my hometown struggle with access to basic healthcare, and later seeing how much inefficiency slowed down biotech on a global scale. Each small effort I made to solve those challenges eventually added up to bigger impact.

Making a positive difference isn’t just about technology or resources — it’s about intention. If you stay curious, keep learning, and use your skills to serve others, you’ll find that even your smallest contributions can ripple out in ways you might never expect. And in the process, you’ll discover a purpose that makes your own journey more meaningful.

Is there a person in the world, or in the US with whom you would like to have a private breakfast or lunch, and why? He or she might just see this, especially if we tag them. :-)

If I had the chance to have a private breakfast or lunch with someone, it would be Bill Gates. Beyond his role in shaping the modern technology industry, I deeply admire how he’s devoted his life to solving some of the world’s hardest problems — global health, vaccines, climate change, and equitable access to resources. He has a unique ability to think both like a technologist and a humanitarian, bridging innovation with impact.

For me, it would be an incredible opportunity to learn how he prioritizes which challenges to tackle, how he measures real impact, and how he stays optimistic in the face of such massive global issues. I’d love to share my own journey in healthcare technology and hear his perspective on how AI and biotech can accelerate progress in ways that truly change lives.

How can our readers further follow your work online?

The best way to follow my work is through my LinkedIn profile and reach me directly at email

Thank you so much for joining us. This was very inspirational, and we wish you continued success in your important work.