Nupur Prasmit — Product Manager

I spend a lot of time thinking about where AI makes teams faster — but not necessarily better. In many organizations, AI adoption quickly improves metrics. Dashboards turn green. Response times drop. Output increases. And yet, outcomes don’t always move in the same direction. That gap — between what AI improves and what teams believe it improves — is what I’m most interested in right now. In my role as Assistant Vice President of Growth Product at Angel One, I’ve worked at the intersection of product strategy, growth systems, and applied AI at scale — contributing to ₹700Cr+ in revenue impact and platform growth to 20M+ users. My work has involved building and scaling: 1. Product-led growth systems and experimentation frameworks 2. AI/ML use cases across scoring, recommendations, and automation 3. Data-driven decision loops using SQL, Python, and large-scale analytics Across these initiatives, one pattern shows up repeatedly: AI changes workflows before it changes outcomes — and metrics don’t always capture that shift well. I’ve led and mentored cross-functional teams across product, analytics, and engineering, working closely with senior stakeholders to balance speed, quality, and long-term impact. Lately, I’m increasingly focused on: -->How AI distorts or reshapes success metrics -->Where “clean” dashboards hide second-order effects -->How teams can build better judgement around AI adoption, not just faster execution I write and think openly about these questions. If you’re responsible for AI-driven products or growth and have ever felt uneasy about metrics that look too good — you’re not alone.

Stackforce AI infers this person is a Fintech Product Leader with expertise in AI-driven growth strategies.

Location: Bengaluru, Karnataka, India

Experience: 7 yrs 10 mos