Megha Hooli
Senior Program Manager at Amazon
Seattle, Washington, United States
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Megha Hooli is a Senior Program Manager at Amazon and a technology leader with 10+ years of experience building and scaling products and programs across AI, retail, healthcare, and enterprise technology.
Her work focuses on turning emerging technologies into practical, measurable outcomes—from AI-driven automation and intelligent customer experiences to enterprise AI adoption and Agentic AI. At Amazon, Megha leads global initiatives within Worldwide Customer Service, partnering across product, engineering, analytics, finance, and business teams to solve complex customer experience challenges at scale.
Before Amazon, Megha held product and technology roles across Cognizant, ACCELQ, Emerson Digital, and Accenture, working on healthcare platforms, AI-powered test automation, industrial systems, and digital transformation.
Beyond her industry work, Megha is an active speaker, author, and technology community contributor. She speaks about Agentic AI, responsible AI, AI-powered product strategy, cybersecurity, personalization, and the practical realities of taking AI from experimentation to enterprise-scale adoption.
Megha holds an M.S. in Business Analytics from The University of Texas at Dallas and a B.E. in Telecommunication Engineering.
Area of Expertise
Topics
Fabric-Powered Retail AI: From Data Lakes to Dollar Signs in 80 Characters
The retail industry is experiencing an unprecedented AI revolution, with academic research publications exploding from just 12 articles in 2000 to over 847 articles in 2023—a staggering 21.3% compound annual growth rate that signals the transformative power of intelligent systems integrated through unified data platforms.
This presentation reveals how leading retailers are leveraging Microsoft Fabric's unified analytics platform to process 150+ customer attributes simultaneously (versus 10-15 in traditional systems), achieving hyperpersonalization that drives 20-30% conversion rate increases and 34% higher click-through rates through context-aware recommendations. By combining Fabric's data lakehouse architecture with deep learning-based recommendation systems—which now constitute 68% of all retail AI research—transformer-based architectures like BERT4Rec deliver 23-31% performance improvements over traditional methods.
Drawing from comprehensive analysis across 29 industries and 112 countries, we'll explore why only 10% of companies achieve significant AI ROI—and what the successful ones do differently with unified data platforms. The presentation will showcase real-world case studies demonstrating how Fabric's integrated environment enables inventory optimization with 30-50% forecast accuracy improvements through generative AI models processing 20-50 feature variables, operational excellence with logistics firms achieving 28% average productivity gains, innovation acceleration with 37% average increase in patent applications post-AI adoption, and customer experience enhancement with 75% prediction accuracy enabling real-time personalization across the data estate.
However, success requires more than technology—it demands the right data foundation. Research reveals that 70% of AI success stems from organizational factors, with AI leaders investing 73% more in human-AI collaboration training compared to laggards (23%). We'll decode the five critical organizational learning practices that distinguish AI winners from losers, all supported by Fabric's comprehensive analytics capabilities.
This data-driven session combines cutting-edge research insights with practical implementation strategies using Microsoft Fabric, addressing both the $billion opportunities and the ethical considerations reshaping retail's future through unified data and AI platforms.
Fabric-Powered Retail AI: From Data Lakes to $Billion ROI & Personalization
The retail industry is experiencing an unprecedented AI revolution, with academic research publications exploding from just 12 articles in 2000 to over 847 articles in 2023—a staggering 21.3% compound annual growth rate that signals the transformative power of intelligent systems built on modern data platforms.
This presentation reveals how leading retailers are leveraging Microsoft Fabric's unified analytics platform to process 150+ customer attributes simultaneously (versus 10-15 in traditional systems), achieving hyperpersonalization that drives 20-30% conversion rate increases and 34% higher click-through rates through context-aware recommendations. Deep learning-based recommendation systems now constitute 68% of retail AI research, with transformer-based architectures like BERT4Rec delivering 23-31% performance improvements when powered by scalable data lakehouse architectures.
Drawing from comprehensive analysis across 29 industries and 112 countries, we'll explore why only 10% of companies achieve significant AI ROI—and how Microsoft Fabric's integrated data platform accelerates success for the winners. The presentation will showcase real-world case studies demonstrating inventory optimization with 30-50% forecast accuracy improvements through generative AI models processing 20-50 feature variables in Fabric's lakehouse, operational excellence with logistics firms achieving 28% average productivity gains through real-time analytics, innovation acceleration with 37% average increase in patent applications post-AI adoption, and customer experience enhancement with 75% prediction accuracy enabling real-time personalization across unified data estates.
However, success requires more than technology. Research reveals that 70% of AI success stems from organizational factors, with AI leaders investing 73% more in human-AI collaboration training compared to laggards (23%). We'll decode the five critical organizational learning practices that distinguish AI winners from losers, and how Fabric's collaborative workspace enables these practices at scale.
This data-driven session combines cutting-edge research insights with practical Microsoft Fabric implementation strategies, addressing both the $billion opportunities and the ethical considerations reshaping retail's future through unified analytics platforms.
Building AI-Driven Retail: Dev Tools, APIs & Implementation Strategies
The retail AI development landscape has exploded, with research publications growing from 12 articles in 2000 to over 847 in 2023—a 21.3% compound annual growth rate that reflects the urgent need for robust development tools and frameworks.
This session explores the developer toolkit powering retail's AI transformation, where modern systems process 150+ customer attributes simultaneously through sophisticated APIs and microservices architectures. We'll examine how deep learning frameworks and recommendation system libraries—now comprising 68% of retail AI research—enable developers to build transformer-based solutions like BERT4Rec that deliver 23-31% performance improvements over traditional implementations.
Drawing from analysis across 29 industries and 112 countries, we'll dissect why only 10% of AI implementations achieve significant ROI from a developer perspective. The presentation covers practical development approaches including generative AI model integration for inventory systems processing 20-50 feature variables, real-time personalization APIs achieving 75% prediction accuracy, and scalable architectures supporting the 30-50% forecast accuracy improvements that leading retailers demand.
Key technical focuses include MLOps pipelines for retail AI, API design patterns for recommendation engines, data preprocessing tools for customer attribute management, model deployment strategies that enable real-time personalization, and monitoring frameworks for production AI systems. We'll examine specific development tools, libraries, and platforms that successful teams use to build these systems.
However, technical implementation is only half the battle. Research shows that 70% of AI project success depends on organizational factors, with successful teams investing 73% more in developer-business collaboration compared to struggling implementations. We'll decode the development practices and team structures that distinguish successful AI projects.
This technical session combines cutting-edge development insights with practical implementation strategies, addressing both the billion-dollar opportunities and the engineering challenges facing retail AI developers today.
AI-Powered Personalization: Building Intelligent Shopping Experiences at Scale
This session explores how modern React Native applications are evolving from static interfaces into intelligent, adaptive systems powered by AI. Drawing from real-world experience in building large-scale retail systems, the talk will walk through how to integrate personalization, recommendation engines, and predictive intelligence into React Native apps.
Attendees will learn how to design mobile architectures that support AI-driven features such as personalized product recommendations, size prediction, and real-time user adaptation while maintaining performance and scalability.
AI-Driven Retail Revolution: From 847% Research Growth to Billion-Dollar ROI
The retail industry is experiencing an unprecedented AI revolution, with academic research publications exploding from just 12 articles in 2000 to over 847 articles in 2023—a staggering 21.3% compound annual growth rate that signals the transformative power of intelligent systems in the global data landscape.
This presentation reveals how leading retailers are leveraging AI to process 150+ customer attributes simultaneously (versus 10-15 in traditional systems), achieving hyperpersonalization that drives 20-30% conversion rate increases and 34% higher click-through rates through context-aware recommendations. Deep learning-based recommendation systems now constitute 68% of all retail AI research, with transformer-based architectures like BERT4Rec delivering 23-31% performance improvements over traditional methods.
Drawing from comprehensive data analysis across 29 industries and 112 countries, we'll explore why only 10% of companies achieve significant AI ROI—and what the successful ones do differently in their data and AI strategies. The presentation will showcase real-world case studies demonstrating inventory optimization with 30-50% forecast accuracy improvements through generative AI models processing 20-50 feature variables, operational excellence with Chinese logistics firms achieving 28% average productivity gains, innovation acceleration with 37% average increase in patent applications post-AI adoption, and customer experience enhancement with 75% prediction accuracy enabling real-time personalization.
However, success requires more than technology. Research reveals that 70% of AI success stems from organizational factors, with AI leaders investing 73% more in human-AI collaboration training compared to laggards (23%). We'll decode the five critical organizational learning practices that distinguish AI winners from losers in the global data economy.
This data-driven session combines cutting-edge research insights with practical implementation strategies, addressing both the billion-dollar opportunities and the ethical considerations reshaping retail's future in our interconnected digital world.
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Megha Hooli
Senior Program Manager at Amazon
Seattle, Washington, United States
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