From pioneering virtual product placement at Amazon to shaping AI-powered experiences that put the customer first, principal product manager Yash Chaturvedi lives and breathes machine learning, media, and product strategy. In this conversation, Yash talks about how to build scalable, ethical AI solutions, how to turn experimentation into industry standards, why relevance is better than format in advertising, and how to lead in a world full of hype.
Can you share how you first got involved with AI-driven product development? Which projects have been most pivotal in shaping your career?
My exposure to AI began during graduate school, where I was fascinated by how algorithms could drive real-world decision making. That curiosity took shape professionally at Staples, where I worked on early machine learning use cases for search and recommendation engines. Those early experiences taught me how to connect data science with tangible operational value.
But the real turning point came at Amazon, where I had the opportunity to lead the development of Virtual Product Placement for Prime Video. We used advanced computer vision and scene understanding to seamlessly integrate brands into content without interrupting the viewer experience. That project not only defined a new category in advertising but also shaped how I approach AI product development—starting with the customer and building with scalability and trust in mind.
What core principles guide you when building product strategy from the ground up for AI services?
I start with the customer experience and work backwards. AI isn’t the entire strategy, but rather it’s just a tool. Second, I define success in measurable terms that reflect both accuracy and business impact. And third, I build explainability and governance into the product from day one. It’s tempting to optimize solely for performance, but long-term trust and usability come from transparency and thoughtful constraints.
For instance, in the case of Virtual Product Placement at Amazon, the goal wasn’t just to insert ads but to create a seamless, brand-safe experience that respected both the story and the viewer. Our computer vision and scene-level understanding helped place products correctly and ensure their presence made sense. That meant we had to build in creative guidelines, metadata standards, and human review loops from the beginning because trust can’t be retrofitted. The product must have it in its DNA.
Rembrand, which recently raised $23 million, has built a business around Virtual Product Placement—a technology you first pioneered at Amazon. What is it like to see your innovation become an industry standard?
It’s incredibly rewarding. VPP started as a bold experiment to create monetization that enhances rather than disrupts storytelling. Seeing that spark turn into a market standard validates not just the technology, but also the vision behind it. I’m proud to have been part of the team that pushed those boundaries early on. Moreover, companies like Ryff, Rembrand and Mirriad have contributed to the evolution of VPP by creating software that enables dynamic ad insertions across platforms and audiences. It shows how the industry is moving toward more integrated, viewer-friendly advertising.
And frankly, it’s exciting to see new players building on that foundation. It means the idea has legs and that the category is just getting started.
In your view, how does this technology need to evolve in order to remain both effective and ethically responsible at scale?
The next chapter is about context and consent. It’s not enough for product placement to be technically seamless but it must also be semantically meaningful and brand-safe. We need better scene-level metadata, stricter content alignment, and tools that give creators and advertisers visibility and control. And we must ensure viewers are not unknowingly manipulated. Ethical AI in media requires both precision and restraint.
Imagine, for example, an AI system inserting a brand into a dramatic or emotional scene like a hospital setting or a breakup. The placement might be technically right, but it could feel tone deaf or exploitative. That’s why ethical AI requires more than just smart algorithms. Context awareness, alignment of values, and human oversight are all needed. To do that, you have to build systems that don’t just optimize engagement, but also respect the emotional integrity of your content.
What has been the most unexpected insight you’ve gained while working on AI-powered advertising products? Was there a moment when user behavior or data led you to rethink your strategy?
Although it’s fairly well-known in the industry, one insight still surprised me: customers care far more about relevance than format. I had been hyper-focused on minimizing ad duration, but what I learned through testing was that viewers were more tolerant of longer ads if they felt timely or integrated into the experience. That pivot changed how we approached personalization and even influenced the prioritization of a few of my products that I’ve built.
Many companies invest in AI but fall short of expected outcomes. What are some common mistakes you’ve seen in AI product implementation—and how can they be avoided?
The biggest mistake is treating AI like a magic wand rather than a capable discipline tool. Many companies either overhype what it can do or fail to define a clear, focused customer problem. Another common pitfall is building models without a strong feedback loop, which stifles learning and iteration. The fix? Anchor every model to a real business goal, integrate it closely with the customer experience, and ensure there’s a closed loop from prediction to outcome to improvement.
With over 11 years of leadership experience in launching AI products, what is the most important piece of advice you would give to product managers working in this space?
Lead with the problem, not the model. AI doesn’t magically create value, but it amplifies when paired with great product thinking. If you deeply understand your customer’s friction and can define what “better” looks like, then AI becomes a superpower. But without that grounding, you risk building something impressive that no one actually needs. Your development time is valuable, as is your customer’s. It is here that the real power of AI begins.
