Chaitanya Garikapati is Head of Data and Analytics at Mars Petcare, where he leads enterprise data, analytics, and AI capabilities with a focus on driving measurable business outcomes.
He brings more than 20 years of experience across multiple companies and industries, building and scaling data-driven organizations in complex, global environments. Chaitanya began his career in hands-on analytical and technology roles, working closely with operations and commercial teams. These early experiences shaped a core belief that data only creates value when it is embedded into everyday decision-making.
As he progressed into leadership roles, Chaitanya led data and analytics initiatives across nearly every major business function, including Sales, Marketing, Supply Chain, Manufacturing, R&D, Finance, Sales and Operations Planning, and end-to-end planning. At Mars Petcare, he is responsible for transforming fragmented, function-specific reporting into an integrated, “digital-first” operating model. His work includes deploying advanced analytics and AI into factories, planning systems, customer execution, and innovation workflows.
A defining shift in Chaitanya’s perspective came from leading large-scale transformations rather than greenfield builds. Navigating legacy technology, organizational change, and global–local tensions sharpened his focus on adoption and sustainability. More recently, stepping into a global leadership role expanded his emphasis on scalability, governance, and responsible AI, particularly in areas such as generative AI and digital manufacturing.
Chaitanya’s leadership philosophy is grounded in the belief that success in data and AI depends as much on culture, trust, and strong business partnership as it does on technology.
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 requires a combination of technical credibility, business intimacy, and the ability to lead through change. In my experience, the most critical traits are business-first thinking, systems-level understanding, and the discipline to translate complexity into decisions that leaders can act on.
“Earlier in my career, I led teams embedded within traditional IT functions. These roles built strong foundations in architecture, scalability, and delivery rigor. However, the most influential shift came in my current role at Mars Petcare, when data and analytics moved into the business as part of the CFO organization.
“That change was a game changer. Sitting within the business enabled much closer partnership with finance, supply chain, and commercial leaders to co-create what truly matters – first defining the right problems to solve, grounded in business outcomes, and only then focusing on building the solutions right with technology. This sequencing has driven far stronger adoption and impact.
“Other essential skills include influencing without authority, navigating ambiguity (especially with GenAI), and building trust through data quality and governance.
“In our organization, business proximity combined with execution discipline has had the greatest impact, turning data and AI into capabilities leaders rely on to run the business.”
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?
“Stop trying to prove how smart your solutions are and start proving that you understand the business better than most people in the room.
“Early in my career, I believed credibility came from technical depth and elegant architecture. Over time, especially as I moved closer to the business, I learned that executive trust is built differently. Leaders don’t promote you because you built the most sophisticated model; they promote you because you consistently help them make better decisions under pressure.
“This means spending time where data leaders don’t always gravitate in operating reviews, factory floors, finance conversations, and moments where trade-offs are uncomfortable.
“Learn how the business wins and loses money. Be willing to simplify and say, ‘this is good enough to act’ and to take accountability for outcomes, not just insights.
“What ultimately differentiates C-suite data leaders is not technical mastery, but judgment of knowing when to push innovation, when to slow down for trust and governance, and how to translate complexity into clarity. If you can be seen as someone who helps the business choose wisely, not just analyze deeply, the C-suite becomes a natural next step.”