Arvind Balasundaram, Executive Director, Commercial Insights and Analytics at Regeneron, speaks with Clyde Gillard, North American AI GTM Leader at HPE, in a video interview about the challenge of pilotitis, strategic use cases, the need for a shift in mindset, the new era of immersive analytics, and AI ambition and its distinct layers.

At the outset, Balasundaram highlights the growing challenge of “pilotitis,” a state where companies, especially in healthcare, are stuck in perpetual pilot programs without ever scaling up.

He attributes this trend to two key reasons:

1. Chasing trends without strategic alignment

Balasundaram stresses that organizations must clearly understand why they are pursuing a particular use case and how it integrates with their overarching business strategy.

“If it is not part of the business strategy and is not integrated into a business plan, it is not going to get the buy-in needed for it to be productionized,” he adds.

Further, he emphasizes that a well-designed use case should:

• Be unique and relevant to the specific business context.
• Include considerations for scalability.
• Address enterprise-level needs, not just isolated functions.

2. Lack of proper metrics and measurement

Next, Balasundaram mentions the importance of having a robust framework for evaluating success. While he acknowledges the need for governance and data quality, Balasundaram focuses on the lack of concrete metrics that tie directly to business outcomes.

“If you don’t do A/B testing, if you’re not comparing it to the ways you would do it. Traditionally, there is no real financial basis to justify an investment because there is no marker of value.”

He warns that faster answers or seemingly new insights are insufficient if they don’t result in real decision-making impact, efficiency, or improved user engagement.

A shift in mindset: Think production from the start

Moving ahead, Balasundaram advocates for a mindset shift — treating use cases as if they are already in production. “I think people need to put more time into the use case as if it were already in production.”

This approach, he argues, leads to more thoughtful planning, broader cross-functional collaboration, and ultimately, better outcomes.

“By the time it goes into a use case, you have thought about everything that prevents you from scaling it. And it’s also easy to justify it,” he notes.

Immersive, intuitive, immediate: The new era of analytics

Delving into the evolving nature of analytics, Balasundaram draws an everyday analogy to describe the shift in how organizations and individuals engage with data. He says, “Every one of us, first of all, has to know where we are going before we get in the car.”

Balasundaram emphasizes that analytics today is no longer a detached technical process — it is immersive and intuitive, much like gaming.

He notes that even users without deep technical expertise can now engage meaningfully with analytics. “I just need to know what question I’m asking. Because of AI and some of these tools that are there — you can now get the answer not only in numeric outputs and graphs but also in natural language.”

This transformation, he explains, leads to a deeper involvement from end users, who now actively guide the analysis process instead of depending on an analyst.

The decline of dashboards

Looking to the future, Balasundaram predicts a shift away from traditional dashboards: “Dashboards to me are like stills, they become obsolete the minute they’re generated because it happened yesterday.”

This evolving mindset leads to what Balasundaram calls “knowledge on the go.”

He says, “I’m talking about an expectation of always being on in terms of being successful in the business, always driving with your eyes through the windshield.”

Balasundaram reflects on how the COVID-19 pandemic amplified the need for real-time insight and preparedness. “The unknown has staggering effects if we are not ready for it. And I think that’s changed the psychology of data and analytics.”

For him, what will define success in this new data era is people who can interpret data and information to guide the organization with a forward mentality.

AI ambition: “You have to know where you’re going”

Speaking on AI and its place in responsible innovation, Balasundaram emphasizes the importance of aligning AI with core organizational values and a clear long-term vision. For him, the ethical foundation of AI is non-negotiable, particularly in the healthcare industry.

“As a business, we are very committed to patients. If it’s not good for patients, we don’t want to be in it. And that’s true of AI.”

In continuation, Balasundaram stresses that organizations must adopt guiding principles that reflect their values before moving forward with AI deployment. He analogizes the journey of adopting AI to career planning: “People come to you and say, ‘Hey, I think you know what career I need to have. And we all know that’s often not the case. You have to get there yourself.”

He adds, “AI-forward companies have developed an AI ambition.”

Referencing a framework from Gartner, he breaks AI ambition into three distinct layers:

1. Everyday AI

“This is your ChatGPT report summarization. These are tasks that are not going to change the game, but are going to add significant relief to your bottom line.”

Balasundaram stresses that everyday AI is well-established and offers immediate ROI, especially in operational efficiency. If a company is not adopting these tools, he suggests it is likely due to hesitation or distrust.

2. Game-changing AI

On the far end of the spectrum lies “game-changing” AI with capabilities that fundamentally alter how businesses operate. To elaborate, Balasundaram offers the example of synthetic data:

“Synthetic data is a game-changing idea. You can take data and generate new data that has nothing to do with the original dataset but retains the context and the relationships of that source dataset.”

However, he is clear that such innovations must pass regulatory and ethical scrutiny. Still, companies need to be ready for when the environment becomes favorable.

3. Mid-level capabilities

Between operational tools and transformational tech sits a middle ground, says Balasundaram. These capabilities enhance speed and decision-making without redefining the entire business model.

“That’s an example of query tools. That could potentially get you top line. It won’t change your game, but the decision could come back in a far faster way.”

Tools like next-best-action measurements also fall into this category, helping companies react in real time and stay competitive.

Ultimately, Balasundaram urges organizations to have a clear, deliberate approach to their AI journey. “You want, as an organization, to have clarity, and you have to take your leadership through what that ambition is.”

Without a defined direction, he warns, companies risk aimlessness. “Because if you don’t know where you’re going. That’s like getting into a car and following the road without knowing where you’re going. None of us wants to do that,” Balasundaram concludes.

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