For more on building AI products and careers, along with early course announcement and special pricing, subscribe to the AI Career Boost mailing list at https://aicareerboost.com/interested THE GUEST Shampa Banerjee, PhD, is a powerhouse product leader who has scaled global digital platforms from scrappy startups into billion-dollar growth engines. As EVP and Chief Product Officer of Pluto TV at Paramount, she helped drive the platform from 20 million to over 50 million monthly active users while accelerating revenue to $1 billion ahead of schedule, expanding the service across Latin America and Europe. Prior to that, Shampa led the growth of Eros Now to 150 million users across 135 countries, delivering an extraordinary 240% year-over-year subscription growth. Over the course of her career, she’s served as both CPO and CTO across venture-backed startups and Fortune 500 companies, building world-class product, engineering, and data teams from the ground up. Trained as a physicist, Shampa brings a rare ability to recognize patterns in complex systems—pairing bold product vision with disciplined experimentation. Today, she advises growth-stage startups and global enterprises on scaling impact through AI, product strategy, and global go-to-market leadership. And in this episode, we’re diving into what it takes to scale digital platforms globally, the leadership behind building billion-dollar product ecosystems, and how AI and data are shaping the future of product strategy. THE SUMMARY AI adoption is failing in many companies — and nobody wants to talk about it: Many organisations are blindly adding AI because leadership mandates it, not because it solves a real problem. The smarter approach is to first ask whether AI is even the right solution. If it doesn’t improve growth, retention, or economics, it’s just expensive hype layered on top of existing systems. Understanding how AI works matters more than just using it: Treating AI like a magical tool leads to frustration when it produces imperfect results. Modern AI systems are probabilistic by nature — meaning uncertainty is built into how they work. Leaders who understand this design guardrails, human-in-the-loop processes, and better prompts instead of dismissing the technology as “wrong.” AI should drive growth — not just cost cutting: Too many companies frame AI purely as a way to reduce headcount or operational costs. The real opportunity is using it to expand capabilities, unlock new products, and scale output. Businesses that only focus on savings risk shrinking themselves instead of multiplying their impact. The AI transformation requires three shifts: People, Product, and Process: The biggest challenge isn’t the technology — it’s organisational change. Teams must get comfortable with uncertainty, rethink what products they should build, and redesign processes that were inherited from the manufacturing era. Companies that only upgrade tools without updating culture and workflows will stall. The AI revolution mirrors the early internet — but at a much faster speed: Just like the dot-com era, many experiments will fail, but the underlying ideas will eventually reshape industries. Concepts like Webvan looked wrong in the early 2000s, yet later became the foundation for companies like Instacart. Today’s AI experiments may look messy, but they are laying the groundwork for tomorrow’s dominant platforms. Technical credibility still matters for leaders: Leaders who understand the mechanics behind technology gain trust with engineering teams and make better strategic decisions. Getting hands-on — even building a small prototype — helps leaders translate between executives and technical teams and prevents unrealistic expectations about what technology can actually do. Hands-on experimentation is the fastest way to understand AI
