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 […]

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.

2. At Mead Johnson, you’ve stressed going beyond “basic insights.” Can you share two or three examples where advanced analytics or AI directly created measurable business impact?
One example is our demand forecasting transformation. By combining advanced machine learning models with market signals, we reduced forecast error by over 30%, improving service levels and optimizing working capital.
Another area is digital commerce optimization. Using AI-driven performance analytics, we identified micro-trends in consumer purchase behavior, improving digital ROI by double digits in several markets.
Lastly, quality analytics in manufacturing helped predict deviations before they occurred, reducing waste and improving batch yield—translating data directly into operational efficiency and sustainability gains.

3. How do you balance enterprise-level data governance with the pressure to deliver quick wins in AI?
It’s a dual-speed strategy. Governance sets the long-term scaffolding—data quality, lineage, and compliance—while “quick win” pilots demonstrate business value fast. I often run both in parallel: a strong governance backbone for reliability, and lighthouse projects that prove impact. The wins build momentum and justify scaling governance further. It’s about showing that governance enables innovation—it doesn’t slow it down.

4. Where do you see the most immediate opportunities for generative AI to reshape consumer experience and engagement in CPG?
Generative AI has immense potential in personalization at scale. For example, creating dynamic product recommendations or tailored wellness guidance for parents using Mead Johnson products. It also accelerates content creation—translating complex scientific insights into engaging, parent-friendly narratives across markets. Finally, knowledge assistants for field teams and healthcare professionals can dramatically improve speed-to-knowledge, ensuring consumers get consistent, evidence-based information.

5. What measures do you use to define the success of AI initiatives—and how do you get business leaders to buy into those measures?
We focus on value realized, not models deployed. Success is defined in terms of growth, margin improvement, efficiency, or customer experience uplift. I work closely with business P&L owners from the start to co-define success metrics. When leaders see AI as a lever for achieving their business goals, not a side experiment, the buy-in becomes natural.

6. Having worked at both Fortune 100 giants and startups, what startup practices have you successfully embedded into large enterprise AI programs?
Two principles stand out: speed and experimentation. Startups test fast and learn faster. I brought that mindset into large enterprises through “AI sprints”—short, high-focus cycles that go from idea to prototype in weeks, not months. The other is a founder mindset: encouraging teams to own outcomes end-to-end. It fosters accountability and creative problem-solving within the structure of a large organization.

7. What strategies have helped you build trust and adoption of AI among non-technical stakeholders?
Trust comes from transparency and relevance. I focus on making AI explainable and relatable—showing how it works and why it matters. For example, when we deployed pricing optimization models, we visualized decision pathways so teams could see AI as a co-pilot, not a black box.
Also, empowering business users through data literacy programs has been key. When people understand data, they trust it—and that’s where adoption starts.

8. How do you approach mentoring and cultivating the next generation of AI and data leaders, and why is this important to you?
Mentorship is one of the most rewarding parts of my journey. I actively mentor through industry forums and within Reckitt, focusing on helping emerging leaders develop a balance of technical acumen, business understanding, and empathy.
AI leadership isn’t just about models—it’s about shaping mindsets that see possibilities and act responsibly. I always tell my mentees: the best AI leaders are translators between data and decisions.

9. What emerging trends in AI—whether in technology, governance, or business models—are you most excited about for the next five years?
I’m excited about AI democratization—where tools and platforms empower every employee to use AI responsibly. Also, AI governance frameworks are maturing, ensuring ethics and transparency stay central.
Technologically, multi-modal AI—combining text, vision, and sensory data—is opening new possibilities in product innovation and customer engagement. The next frontier is where AI augments human creativity, not replaces it.

10. If you had to give one piece of advice to organizations still stuck at “basic insights,” what should they do first to move toward an AI-driven enterprise?
Start small, but start with impact. Don’t begin with technology—begin with a business pain point that data can solve. Prove value fast, celebrate success, and scale from there.
Most organizations don’t fail from lack of AI talent—they fail from lack of alignment and conviction. Build belief first; the transformation follows.

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Bhasker Gupta
Bhasker Gupta is a seasoned technology leader and entrepreneur, recognized for building platforms and communities at the intersection of AI, data, and innovation. With over two decades of experience, he has consistently driven impactful initiatives empowering enterprises and tech ecosystems worldwide. Reach out to me at bhasker.gupta@aim.media
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