Ameya Gokhale

Autonomous AI Agents for Scalable Personalisation and Reinforcement Learning–Driven Programmatic Media Optimisation in Online Retail

Abstract:

The global AI in retail market, valued at USD 4.84 billion in 2021, is projected to grow at a 30.5% CAGR through 2030, reaching USD 55.5 billion, reflecting large-scale adoption of machine learning systems in digital commerce. This talk presents a systems-level view of autonomous AI agents as applied machine learning architectures integrating reinforcement learning, large language models (LLMs), computer vision, and big data analytics to optimise retail ecosystems.

From a personalisation perspective, AI-driven recommendation engines influence 35% of Amazon purchases, while AI-based personalisation yields a 15% increase in conversion rates and up to 20% higher average order values. CRM systems powered by predictive ML models have demonstrated 40% increases in qualified leads and 21% improvements in customer retention, highlighting measurable downstream impact.

In programmatic advertising, reinforcement learning based bidding systems reduce cost per acquisition by 44%, increase conversion rates by 59%, and reduce wasted ad spend by up to 76% through real-time policy optimization. AI-generated creatives further improve click-through rates by 41%, while personalized ad segmentation drives 72% higher engagement rates. AI-powered campaign systems operate at millisecond decision speeds, aligning with modern distributed and high-performance computing environments.

For seller ecosystems, applied ML models enable 25% profit improvements via dynamic pricing, 76% catalog time savings, 65% policy violation reductions, and 30% higher annual growth rates for AI-enabled businesses. This session frames autonomous AI agents as scalable, reinforcement learning–driven decision systems operating across cloud-based infrastructures, demonstrating how machine learning algorithms translate into quantifiable performance gains across consumer modelling, media optimisation, and intelligent commerce platforms.

Profile:

Ameya Gokhale is an AI and machine learning leader with over nine years of experience designing, developing, and monetizing large-scale AI/ML products across retail advertising, e-commerce analytics, smart city infrastructure, and digital marketing. He currently serves as a Staff Product Manager at Walmart Connect, where he leads the development of Walmart’s AI-driven optimized bidding platform for Sponsored Products, focusing on advertiser growth and profitability through advanced machine learning systems.

At Walmart Connect, Ameya spearheaded the launch of Walmart’s first AI-powered Dynamic Bidding Solution and Target ROAS AI product, leveraging deep neural networks, ensemble models, XGBoost, and reinforcement learning. These initiatives optimized real-time bidding and campaign performance for hundreds of thousands of advertisers, improved sales and ROI for small businesses, automated manual processes, and generated significant incremental revenue for the platform.

Previously, Ameya held leadership roles at Stackline Inc., where he served as Director of Data Science and Machine Learning. In this role, he led teams building and deploying large language models and other AI systems embedded across Stackline’s product suite, driving improved personalization, faster inference, and reduced operational overhead. Earlier at Stackline, he developed transformer-based product matching systems, automated product classification pipelines operating at tens of millions of SKUs per week, and sentiment analysis frameworks that enabled brands to extract actionable insights from consumer reviews.

Earlier in his career, Ameya worked as a Data Scientist at Pernix Inc. in Adelaide, Australia, where he led AI-driven network optimization initiatives for the city’s 10 Gigabit network. His work supported large-scale smart city infrastructure by improving network performance, enabling real-time monitoring, and contributing to economic growth through enhanced digital connectivity. He began his professional journey at Viral Mint in Pune as a Marketing and Advertising Manager, where he led data-driven performance marketing strategies for multinational organizations.

Ameya holds a Master’s degree in Information Systems Management from Carnegie Mellon University and a Bachelor’s degree in Computer Science Engineering. He has received multiple professional certifications in machine learning and deep learning and was recognized with the Outstanding Speaker Award at the IDEAS Conference in 2019.