271. Pavi Gupta, VP Insights & Analytics at Chobani: Data that Matters

·59 min·5 clips

A J&J leader called Pavi Gupta's insights work 'okay,' pushing him to learn coding and embrace digital exhaust.

The episode begins with hosts Daniel Torres and Efrain Rosario sharing summer travel anecdotes before introducing the topic of insights in FMCG. They welcome guest Pavi Gupta, VP of Insights & Analytics at Chobani, who connects from Chicago. Gupta outlines his career, starting in 1996 at Research International (Kantar) in India with pen-and-paper surveys, then moving to Coca-Cola where he worked in India, Thailand, and Atlanta, gaining exposure to qualitative research and neuromarketing. He describes his transition to J&J and SC Johnson, where he embraced data analytics and AI, upskilling through courses at Columbia, MIT, and Kellogg. Gupta explains his 'power of and' philosophy, advocating for layering new technologies onto traditional insights to build a richer consumer understanding. He recounts a pivotal moment at J&J when a leader critiqued his 'old school' approach, prompting him to explore 'digital exhaust' from social media. Gupta discusses AI's role, identifying the 'validation space'—known and intuitive insights—as where AI will have the biggest impact by automating tasks, freeing humans for curiosity and empathy. He addresses managing team resistance to AI, emphasizing that human superpowers like curiosity remain critical. Gupta shares an example of an insights project at J&J that failed due to data ecosystem limitations, comparing it to electric cars in the 1890s being ahead of their time. The conversation turns to elevating the insights function, with Gupta mentioning the Insights Lighthouse initiative by Dr. Luboff, which hosts events at Davos, Cannes, and CES. He advises his younger self to enjoy the career journey, not just the destination. Hosts summarize key takeaways, including embracing AI as a tidal wave, focusing on curiosity, and learning from the 'digital exhaust' concept.

As heard by us

A steady look at AI's history, generative tools, and the skills now expected around insights work.

This single-episode review takes up a practical concern for anyone working near the insights function: what skills matter as AI changes the surrounding work. Its central argument is pragmatic rather than alarmist.

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Why you'd press play

Want a calm way to think about AI in insights work, with a Copilot poem as the surprisingly human example?

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