Amazon Head of Product on Scaling AI-Driven Marketplace and eCommerce Products

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.

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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