How do you turn raw data into AI products that drive over $1B in GMV? In this webinar hosted by Nisarg Desai, Amazon Head of AI Product & Data for Pricing & Promotions Dilip Patel will be speaking on building revenue-generating AI systems in marketplace and eCommerce environments. Drawing on a decade of experience at Amazon, Fractal.ai, and more, Dilip shares proven frameworks for scaling LLMs, structuring high-performance teams, and optimizing the KPIs that matter most.

Join us for new conversations with leading product executives every week. Roll through the highlights of this week’s event below, then head on over to our Events page to see which product leaders will be joining us next week.


Show Notes:

  1. Products That Count: Takeaways from Dilip Patel’s Webinar on Scaling AI Products
  2. Always start with customer problems, not technology solutions
  3. Prove value quickly by using readily available AI models before massive investments
  4. Unique proprietary data is the most significant competitive advantage in AI
  5. Implement human-in-the-loop systems to build trust and improve AI performance
  6. Integrate AI with analytics to enhance business decision-making
  7. Focus on long-term customer value, not just short-term metrics
  8. The role of product managers is changing – now includes training AI algorithms
  9. Use the McKinsey Horizons framework to balance innovation and current problem-solving
  10. Design experiments with holdout groups to accurately measure AI impact
  11. Build explainability and safeguards into AI systems to increase user trust
  12. Optimize AI for key business KPIs, not just technical performance metrics
  13. Create custom AI assistants using tools like Notebook LLM for knowledge retention
  14. Consider the multi-sided ecosystem dynamics when developing AI products
  15. Develop objective function thinking instead of just feature-based development
  16. Incrementally learn and adjust AI systems based on real-world performance
  17. Coach product teams to understand model trade-offs and evaluation frameworks
  18. Balance AI investment across current products, near-term growth, and future innovation
  19. Capture both explicit and implicit user feedback to improve AI systems
  20. Recognize that AI should augment human intelligence, not replace it
  21. Stay connected with the AI product management community to continuously learn and evolve
About the speaker
Dilip Patel Amazon, Head of AI Product & Data for Pricing & Promotions Member

Dilip is a data scientist turned product leader with 10+ years of experience in scaling AI-driven marketplace / eCommerce products. At Amazon, I lead AI Pricing and Promotions optimization product area with team of 8+ PMs and Data analysts as part of eCommerce business for the past 5 years, driving over $1 billion in GMV and $500 million in FCF. Prior to Amazon, he led Search, Discovery, Growth, and Ad-tech products at several BigTech (Salesforce) and SmallTech (Groupon, WeWork, Fractal.ai) companies in the SF Bay Area. Outside of work, Dilip is a lifelong learner (MBA -UC Berkeley Haas, CS -Mumbai University, and CFA) and enjoys outdoor activities (skiing, hiking, and kayaking) with family and friends in the SF Bay Area. Additionally, Dilip advises AI startup founders and coaches PMs on scaling AI products.

About the host
Nisarg Desai Lacework, Director of Product

Nisarg is currently the Director of Product at cloud security unicorn Lacework, and has a decade of experience in Product Management with the entirety of his focus on securing enterprises from cyberattacks. He has built both 0-1 and 1-100 products in cybersecurity. He is passionate about pushing forward the field of product management by helping a diverse field of entrants grow into successful PMs. He lives in Boston, MA.

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