Inside the Mind of the AI Leader Driving Business Impact at Mead Johnson

It’s rare to come across a leader who has not only navigated the full arc of data and AI transformation but done so across industries as complex and varied as healthcare, consumer goods, retail, insurance, and finance. In this interview, I spoke with Ganesh Sivakumar, who currently leads data and AI at Mead Johnson (part of Reckitt), to understand how he translates technical capabilities into tangible business impact—and how he’s scaling those efforts responsibly.

Mead Johnson Nutrition (a Reckitt company) is increasingly integrating AI and data science into its core operations. For example, during its migration to SAP S/4HANA, the company modernized its data pipelines—moving 140+ SAP tables into Databricks via Azure Data Factory and SAP DataSphere—to enable near‑real‑time analytics and future AI/ML use cases. In Hong Kong, Mead Johnson also launched a Mom AI Intelligence Network, using generative AI and big data to analyze mother‑to‑mother conversations across social media, WhatsApp, forums, and more, turning conversational insights into trends that trigger automated content / messaging optimizations.

What stood out was his clarity on balancing governance and speed, his approach to making AI relatable for business teams, and his focus on mentorship. Our conversation was rich with actionable insights for anyone navigating the intersection of data, AI, and business change in large enterprises.

1. You’ve led data and AI programs across healthcare, consumer goods, retail, insurance, and finance. What common playbook do you rely on when driving transformation in such varied industries?
While the industries differ, the fundamentals of data transformation remain consistent: start with business outcomes, not technology. My playbook always begins by asking, “What problem are we solving, and how will we measure impact?” Once the value hypothesis is clear, I focus on building the right data foundation—governance, literacy, and access—so analytics can scale responsibly.
The other constant is change management. No transformation succeeds without business adoption. I’ve found that simplifying AI for non-technical leaders, using storytelling and real use cases, drives far more engagement than technical depth. Whether in healthcare or CPG, the aim is the same: convert insights into action and measurable results.