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Dipin Oberoi is a social listening expert who has had stints at Walgreens, Ava Labs, and Microsoft and who recently joined JPMorganChase as senior associate, social intelligence and insights. He is set to speak at Marketing Brew’s upcoming event, The Art and Science of AI in Marketing, on February 25.
Ahead of the event, we caught up with him to hear how he and his teams use AI for social listening, which you can hear more about live at the event.
How are you and your teams using AI today?
We’re mainly using it to process the massive amount of social listening data we handle. Instead of manually combing through millions of consumer conversations, AI helps us spot trends and sentiment shifts quickly. We also use it to pull together competitive intelligence and turn complex analytics into reports that merchants and executives can actually use. It’s really about getting from insight to action faster, not replacing strategic thinking.
What’s the best real-life application of AI you’ve seen in marketing?
Honestly, predictive trend spotting for retail. We can now catch what consumers are talking about four to six weeks before it shows up in search or sales data. We saw certain beauty ingredients gaining traction in conversations way before they became mainstream requests. That head start is huge for planning what actually ends up on shelves when people want it.
Which AI applications are most promising? Which ones are least promising?
Most promising is anything that cuts through information overload. Tools that can take consumer reviews, social chatter, and research reports and actually tell you what matters. Personalization at scale is exciting, too.
Least promising? AI-generated creative that tries to replace human storytelling. We’ve tested it, and it still misses the cultural nuance and emotional connection. And I’m skeptical of anything claiming it can replace strategy. AI informs decisions, but the judgment still needs human context.
What role should the advertising industry play in AI risks, ethics, and regulations?
We can’t just wait around for regulations. We need our own standards, especially around transparency in how we use AI for targeting and insights. The bigger issue is making sure we’re not baking existing biases into our recommendations, particularly when they affect diverse communities.
For us, that means being upfront about when AI is involved, keeping humans in the loop on big decisions, and setting our own guardrails now. The brands that do this right will earn more consumer trust, which honestly is just good business.
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