Anand Shenoy is Global Chief Data Officer at Mars, where he leads enterprise data, analytics, and AI strategy across global business functions and regions. With more than two decades of experience, Anand has built and scaled data capabilities within global corporations and Fortune 50 enterprises, focusing on driving enterprise value through data-driven transformation.
Anand began his career in engineering and systems roles, developing a strong foundation in data accuracy, process discipline, and the operational underpinnings of digital transformation. He later expanded his perspective through consulting roles at Deloitte and EY, where he worked closely with senior executives across industries to design enterprise data strategies, modernize technology ecosystems, and lead complex transformation programs.
Moving into industry leadership, Anand went on to build and scale data and analytics functions for major global organizations. In these roles, he established governed data domains, modernized cloud-based analytics platforms, and led enterprise-wide data governance initiatives with direct CEO and board-level sponsorship. These experiences reinforced the importance of trust, security, and disciplined operating models in enabling AI at scale.
At Mars, Anand is focused on unifying fragmented data operating models, advancing multi-cloud platforms, and embedding responsible AI into decision-making processes. His leadership reflects a belief that successful data and AI transformation depends not only on technology, but on trust, cultural alignment, and sustained business impact.
As a data and AI leader, which traits and skills do you think matter most, and which of those have been most influential for you in your current position?
“Effective data and AI leadership hinges on a few traits that consistently separate transformational leaders from technical operators.
- Strategic clarity and enterprise thinking. The ability to translate AI into business model change, productivity gains, and P&L impact is the single most important differentiator. At Mars, this has been critical to securing C‑suite alignment and prioritizing AI investments that matter.
- Operating‑model design and execution discipline. AI only scales when data, governance, underlying platform ecosystem and delivery are unified. Redesigning our enterprise AI Delivery engine required re‑architecting workflows across functions and markets; this trait has had the biggest impact on adoption and speed.
- Influence across the C‑suite. AI touches every part of the business. The ability to align functional and regional CIOs, and business function leaders around shared outcomes has been essential to driving enterprise‑level change without owning every execution team.
“A rigorous approach to responsible AI. Embedding trust, security, and risk controls into the operating model has been a differentiator in a global, consumer‑facing business.
“These traits have been most influential because they enable AI to move from experimentation to enterprise‑wide scale and value creation.”
Reflecting on your career, what is one non-traditional piece of advice (outside of technical skills) you would give to an aspiring data or AI leader aiming for the C-suite?
“One non‑traditional piece of advice I give aspiring data and AI leaders is this: learn to read the organization, not just the data. The leaders who reach the C‑suite aren’t the ones with the deepest technical expertise; they’re the ones who understand power, incentives, and how decisions get made.
“In my experience, this has been far more influential than any technical skill. At Mars, the ability to sense where the organization is ready to move, where it will resist, and where influence, not authority, will unlock progress has been essential to transforming and scaling data and AI across global functions, regions and markets. AI transformation is rarely blocked by technology; it’s blocked by misaligned incentives, unclear ownership, and cultural inertia.
“The leaders who succeed learn to navigate these dynamics with precision: shaping narratives, building coalitions, and sequencing change in a way the business can absorb. They know when to push, when to pause, and when to reframe the conversation entirely.
“In short, the most underrated skill in data and AI leadership is organizational intelligence—the ability to understand people, power, and timing. It’s the difference between having a strategy and actually delivering enterprise‑level impact.”