Arvind Balasundaram, Executive Director, Commercial Insights and Analytics at Regeneron, speaks with Clyde Gillard, North American AI GTM Leader, HPE, in a video interview about his role, turning raw data into actionable data, building AI readiness, and the four critical dimensions of AI readiness.

Regeneron Pharmaceuticals, Inc., is an American biotechnology company headquartered in Westchester County, New York. 

Balasundaram leads the Commercial Insights and Analytics Center of Excellence teams at Regeneron. His role, as he explains, centers on navigating and guiding a rapidly evolving analytics landscape.

Next, Balasundaram highlights the core areas of focus, particularly on the commercial side of the business, including maturing data platforms, becoming AI-ready, agile analytic implementation, and deepening insights.

Insights, integration, and the evolving role of analytics

Moving forward, he reflects on what excites him most about his role and how the landscape of data, analytics, and AI is rapidly transforming. Balasundaram emphasizes the significance of the term “insights” in his title, distinguishing it from mere “findings.” “Insights are a different piece than a finding, and they are very difficult to obtain, especially when you want to deliver business impact,” he adds.

Drawing a parallel to detective work, he describes his passion for unearthing insights. “I’m a big consumer of Sherlock Holmes Mysteries, and to me, digging for insights is just like a detective game.”

Balasundaram points out the challenges that come with the overwhelming scale of data in today’s world — both structured and unstructured — and the importance of integrating it meaningfully. He also highlights the second element of analytics in his title and elaborates on how historically only 20% of information was available in structured form, with 80% being unstructured. But the advent of AI is allowing organizations to finally engage with that untapped data.

“With AI in the mix, we are now able to attack that. It’s still in the nascent phase, but we are very optimistic that we will get our hands on that.”

Beyond data: The funnel to enterprise wisdom

While acknowledging the importance of high-quality data, Balasundaram cautions against becoming overly fixated on it.

“If you don’t have a good data product, you don’t have good AI, but data is just data, right?” He connects working with data to his undergraduate art history course, where he learned a foundational lesson: “Looking at something is different from what you see in it.”

For Balasundaram, data marks the beginning of a journey — a funnel through which raw data must be processed, refined, and translated into action. He outlines the critical steps that follow:

• Ask the right question to avoid solving the wrong puzzle.
• Curate the data, because more data does not mean useful data.
• Integrate information because the what and the whys have to come together.
• Deliver insight in a format that enables action, even for those not immersed in data complexities.
• Move beyond reactive measures to strategic questioning in resource-constrained environments.

“Impact and value today go down to the quality of the data and how you’re able to convert it to information and to enterprise wisdom and knowledge,” he says.

Building true AI readiness

While many organizations rush to join the AI race, Balasundaram stresses that long-term success in AI demands far more than enthusiasm; it requires deliberate readiness across people, processes, and platforms.

“I think a lot of people dive into the AI ball game because you have to be in it to win it, but you first have to have the infrastructure and the environment that kind of delivers value in AI.”

Balasundaram further breaks down AI readiness into four critical dimensions:

1. Organizational readiness

Everything starts with the people and the culture. “Even before I go into data technology stacks and things of that nature, you have to have an organization that’s ready both bottom-down, laterally, and upstairs.”

Organizations need a culture of innovation, prudent risk-taking, stakeholders open to diverse modalities of information, and teams willing to learn new ways of thinking.

2. Technical readiness

Furthermore, Balasundaram highlights the importance of robust and flexible technical infrastructure to support AI deployment at scale. This includes:

• Cloud platforms that scale
• Robust data pipelines to manage vast volumes and velocity of data
• Unified data ecosystems
• Interoperable APIs to enhance usability and flexibility

“You really want data that’s ready for AI. You need metadata that is rigorous so you can govern that data appropriately by tracking lineage and things of that nature,” he adds.

Balasundaram then stresses the need for modular, scalable architecture that supports transfer learning and avoids one-time-use design traps. “You don’t want a one-time design that restricts you in the application because then you’re just going to stay in the mode of pilot and never go to production.”

3. Real-world readiness

On top of these, Balasundaram states that being technically ready today is not enough, and organizations must prepare for continuous evolution. As an example, he cites the shift from classic vector-based retrieval methods to graph-based data exploration.

4. Process readiness

Ultimately, Balasundaram addresses the need for practical frameworks to measure return on AI investments.

“Nothing can be done unless there’s an investment behind it, which means you have to be ready on day one to measure some form of ROI.”

While he acknowledges that traditional ROI measures can be tricky with AI, especially in early-stage areas like deep learning and unstructured data, organizations must still establish clear success metrics.

Wrapping up, Balasundaram states that at Regeneron, as in many forward-thinking companies, the approach to AI is intentional and holistic.

“We’ve kind of been approaching it in a very systematic way. Because we want to be ready not only for the short term, we want to be ready for the long haul and we want an evolving mindset towards innovation with AI,” he concludes.

CDO Magazine appreciates Arvind Balasundaram for sharing his insights with our global community.