…It used to be experimental; now it’s integral. Companies no longer ask if AI works; they ask how fast and how effectively we can deploy it. That shift has created opportunities to solve bigger, more meaningful problems. Today, every pharma client wants to use AI to enhance commercial strategy, identify at-risk patients earlier, and optimize therapy pathways. AI is embedded in the decision-making fabric now…

Artificial Intelligence is rapidly redefining the future of healthcare, bringing transformative shifts from diagnosis to treatment and beyond. As AI becomes more embedded in healthcare systems, its potential to personalize patient journeys, enhance diagnostics, and streamline commercial strategies has never been more crucial. Shantanu Seth, Senior Director at Axtria, is at the forefront of this transformation, pioneering AI-powered analytics that are revolutionizing life sciences. With over a decade of leadership experience and a track record of delivering over $500M in impact, Seth offers an insider’s perspective on how AI is not just theoretical — it’s reshaping healthcare in real time.

Shantanu Seth brings deep expertise from his work across Axtria, TheMathCompany, and Mu Sigma, where he led AI-driven solutions across therapeutic areas such as cardiomyopathy, HIV, and oncology. His work operationalizes AI within marketing mix modeling, patient journey orchestration, and rare disease identification, integrating technologies like machine learning, deep learning, and NLP into everyday clinical and commercial practices. A recipient of the Titan Award and Hackathon Raptors Fellowship, he continues to shape the next generation of AI talent through mentorship and thought leadership.

Can you take us back to the beginning? What sparked your interest in AI and led you to this field?

I was fortunate to be in the right place at the right time. When I graduated, data science wasn’t widely recognized. I started at Mu Sigma, one of the first pure-play analytics firms, and my time there was foundational. It was all very new, both for me and for the industry. Data science was emerging, and I had the chance to set up new business operations in the Middle East, Egypt, Poland, Russia, and Latin America driving regional P&L from $200K to $6M. From there, I moved into leadership roles, eventually joining Axtria. My mission now is to make patients invest in their therapies as consumers are in lifestyle brands. If you’re buying a Patagonia jacket,
you deeply care about the value and story behind it. I want patients to be invested in their medications and therapies.

How has your role evolved over time, and what leadership lessons have you learned along the way?

In the early days, data science was still experimental. I had to wear many hats, from setting up legal entities to serving clients directly. At MathCo., I helped scale U.S. operations from under $1M to over $60M. Now at Axtria, I manage teams across the Midwest and West Coast, responsible for around $5M in business. Leading in this field has taught me two things: you can never know enough, and always focus on outcomes, not just the means. A statistically accurate model is meaningless if it doesn’t drive real value.

I also make a conscious effort to stay hands-on. I still code in Python and SQL, build neural networks, and stay current with new tools like PySpark and deep learning frameworks used in patient flow and market mix modeling. It helps me connect better with my team and keeps me grounded in the actual work.

What do you look for when hiring talent for your team?

I evaluate talent using a Venn diagram of math, technology, and business. But because healthcare in the U.S. is so complex, domain knowledge is critical. Understanding nuances like the difference in patient access or the implications of the Inflation Reduction Act is key. That kind of knowledge can’t easily be outsourced or taught overnight.

Can you give a concrete example of how these nuances affect your work?

Absolutely. In the U.S., accessing a cardiologist often requires going through a primary care provider and dealing with insurance. In India, you can walk in directly. These systemic differences affect how we model patient journeys. For biologics, which can cost $100,000 annually, we need to ensure all logistical and financial handshakes are predicted and managed so patients get timely access. That level of detail is what drives outcomes.

We also have to consider market access challenges, like 340B drug pricing programs and insurance coverage variances across states. These factors deeply impact how we build our predictive models.

How has ChatGPT and generative AI changed how your team operates?

It’s been transformative. We use it to build decks, storyboard strategies, and even design data roadmaps. It’s made us faster and more efficient. But I’m not concerned about it replacing us. ChatGPT can only solve problems that have already been solved. We focus on first-of-their-kind challenges, like identifying patients for rare conditions such as SCD, PKD, CAH, Rett Syndrome, Thalassemia, and ESR1 mutations using early warning systems. That kind of work requires human insight and domain expertise.

We also use generative AI to simulate patient journeys and model market scenarios. It’s become a co-pilot, helping us brainstorm and iterate faster. But it’s still up to us to validate and contextualize the output.

What are the biggest challenges in leading a team in such a rapidly evolving field?

The biggest is staying current. Technologies change daily. When I started, we used SPSS, then SAAS, and now Python. I still code myself because I believe a good leader in this space needs to be a practitioner. It keeps me grounded and credible. The other challenge is making sure the team doesn’t get hung up on the tool but remains focused on delivering value.

I also coach my team to always prioritize the end goal over the methodology. A great model that doesn’t move the needle for the client is just academic. We must tie our outputs to real, measurable business or clinical impact.

Have you seen a shift in how companies view the role of AI in their business?

Absolutely. It used to be experimental; now it’s integral. Companies no longer ask if AI works; they ask how fast and how effectively we can deploy it. That shift has created opportunities to solve bigger, more meaningful problems.

Today, every pharma client wants to use AI to enhance commercial strategy, identify at-risk patients earlier, and optimize therapy pathways. AI is embedded in the decision-making fabric now.

What keeps you motivated in this field?

Knowing that our work affects real patients. We’ve built models that reduce diagnostic delays by 30% especially in rare diseases like ATTR-CM and SCD as well as improve therapy adherence by over 20% through predictive interventions. That’s not just data — that’s someone getting diagnosed sooner or sticking with a treatment that could save their life.

And I love solving new problems. We’re often the first to tackle certain challenges, like finding patients for a brand-new rare disease therapy. It keeps the work meaningful.

How do you see the future of AI in healthcare evolving over the next few years?

I think we’ll see a move toward more proactive care — predicting health issues before they manifest, customizing therapies at the individual level, and automating many of the inefficiencies in the system. But success will still hinge on context. Algorithms alone won’t solve healthcare. They need to be coupled with a deep understanding of the ecosystem.

We’re also going to see more regulatory frameworks around AI, especially in clinical settings. Ethical AI, data privacy, and explainability are going to be front and center.

If you could give one piece of advice to aspiring AI professionals, what would it be?

Stay curious and stay humble. You’ll never know it all, and that’s okay. Focus on solving real problems, and always keep the human impact in mind. Also, don’t get too attached to any one tool or technology. The landscape will keep changing. Adaptability is your biggest strength.

About the Interviewer: Chad Silverstein is a seasoned entrepreneur with 25+ years of experience as a Founder and CEO. While attending Ohio State University, he launched his first company, Choice Recovery, Inc., a nationally recognized healthcare collection agency — twice ranked the #1 workplace in Ohio. In 2013, he founded [re]start, helping thousands of people find meaningful career opportunities. After selling both companies, Chad shifted his focus to his true passion — leadership. Today, he coaches founders and CEOs at Built to Lead and advises Authority Magazine’s Thought Leader Incubator.

A.I. in Action: Shantanu Seth’s Vision for a Data-Driven Healthcare Future was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.