Hi, my name is Harry Prima. Thanks for joining this session. I'm here to talk about reshaping the very foundation of retail artificial intelligence for store assistance as a senior data engineer and architect with over 18 years of experience in this field. I've seen how data algorithms and smart systems are transforming businesses. AI doesn't just support backend analytics. It becomes a frontline tool for engaging customers, optimizing operations, and driving strategic decision making decision. Today's session is designed to unpack this transformation. I will guide you through how AI and machine learning are integrated into retail environments, both online and in physical stores, and how these technologies are helping businesses to meet modern consumer expectations. This is not, it's. It is about using data and intelligence to craft better, more efficient, and more personal shopping journeys. Whether you are a tech guy or a retail executive, or simply curious to know about how AI is impacting in retail stores, this session will help you. To walk away with some valuable insights today. Let's dive in and explore what the future of smart retail really looks like. Here is what we'll cover over this session. First, I'll give a quick introduction to the state of AI in retail to provide context to it. Then we'll see some statistics about growth and investment in the space. We'll see some specific applications like virtual try ons, cashier list checkouts, and AI powered restocking systems. Then we'll dive into future trends, especially hyper-personalization, dynamic pricing and digitalization of and stores. From there, we'll go deep into core applications of AI in. Inventory management, chat bots, recommendation systems, and more. We'll learn about ML models and tools empowering these applications. Next, we'll break down the most important frameworks and technologies. TensorFlow, PyTorch, Azure, ai, and Maker. Then we. We'll also cover a real case study to make things tangible and show how it benefits. Let's begin with the big picture is undergoing a profound transformation and AI at the heart of it. In just the past five years, we have seen become far more digital and automated. AI and retail is projected to grow from 26.9 billion in 2023 to over 60 billion by 2030. That's a compound annual growth rate of 31 percentage. This is not marginal. It's exceptional growth. Why? Because AI addresses two core challenges in retail efficiency and personal. Customers expect more faster service. Personalized offers, seamless experiences. At the same time, businesses face pressure to reduce operational cost and make smarter decisions in real time. AI is uniquely positioned to solve both the challenges. Think about your last shopping experience. Did you get. Did HR Port assisted you? Was your checkout fast and personalized? If so, there is a good chance AI played a role behind the scenes. Retailers are using AI for dynamic inventory management, ensuring shelves are stocked, not just based on past data, but on predictive insights. They using it. Demand spikes based on weather or local events, or even to create hyper-personalized promotions based on processing behavior. As we continue, I'll show you how different types of ai, national language processing, computer vision, predictive analytics, are coming together to create intelligent, responsive and customer first retail ecosystems. Many still rely on manual processes, gut failing or outdated systems, but the raise of cloud AI platforms and pre-trained models, even small businesses, can implement chatbots, customer segmentation models, or pricing optimization tools with minimal cost and high returns. The future of AI in retail is incredibly exciting and can be summarized in three key trends, hyper-personalization, real-time dynamic pricing, and AI optimized physical stores. Let's start with hyper-personalization. This goes beyond showing how you products based on your browsing history. It's about using data from multiple touch points, social media, purchase history, realtime location, even biometric signals to create a tailored experience. Imagine walking into your store where screen welcomes you by name and offers deals based on your preferences. That's where we. Second real time dynamic pricing. A models can now adjust prices based on current inventory, competitive pricing, market demand, and even better, or even data. Airlines and hotels have used this for years. Now it's coming to retail. For instance, a grocery store can rise or lower the prices based on protect expiration timelines or foot traffic trends. Maximizing profits while minimizing waste. Third brick and motor optimization four years. Physical stores have lacked behind e-commerce in innovation, but that's changing fast. We now have AI power systems for tech prevention, footfall analytics, and automated checkouts stores can analyze in-store movement patterns to improve shelf layouts, deploy virtual mirrors, or use computer vision to track them interactions and. Takeaway here is that AI is no longer just enhancing what we do in retail. It's redefining how we do it. It's creating new standard where convenience, speed, and personalization are no longer pers. They are expectations. Let's talk about how are reshaping operations. And perhaps most impactful area is inventory management. Traditional stock control is reactive and manual, but with ai, stores now predict buying trends weeks in advance, algorithm analysis, past sales, seasonal patterns, and even events like holidays, weather forecast. This predictive approach not only awards stock orders, but also minimizes overstock, which saves money and reduces waste. Next is demand forecasting by leveraging machine learning models. Often time series based retailers can fine tune recruitment schedules, for example, a. Stock more raincoats in advance of we season predicted by AI models trained on historical and meteorological data. Then we have personalized product recommendations. This is one of the most visible AI applications to customers. Think about streaming our online video companies. You see solutions tailored just for you. Retailers are now doing the same, whether it's suggesting a new skincare product based on the previous purchase or bundling offers according to buying behavior. These algorithms drive both customer satisfaction and higher revenue per transaction. One of. Is chatbots and virtual assistance. These are not the clunky boards of early two thousands. Today's AI assistance use NLP to hold real conversations. They assist with FAQs, offer style suggestions, event process returns all 24 by seven and often in multiple languages. Let's not forget AI enabled fraud detection, loyalty, program optimization, and customer sentiment analysis from online reviews and social media feeds, AI turns noise, internal knowledge. What's most powerful here is. These use cases are interconnected. A chat bot can collect customer intent data, which fits into demand forecasting, which influences inventory, restocking the result, a synchronized ecosystem where data flows intelligently between functions and reducing silos and maximizing efficiency. Now that we have seen what AI can do, let's explore core technologies making it possible. First up is natural processing, natural language processing, NLP. This is the engine behind smart chat bots and voice assistance. NLP models, analyze customer queries, understand intent, and generate human-like responses. Next is computer vision. This is where AI analyzes visual data, images, videos, realtime camera feeds. In retail computer vision powers, automated checkout systems, virtual try-on apps, and even in-store thrift detection. For example, cameras can detect when an item is removed from a shelf and not scanned it. Checkout instantly flagging potential loss. Then comes predictive analytics. This is where machine learning meets business strategy. Predictive models analyze past behavior. We want to know which customer is likely to churn or which product category will spike next week. Predictive analytics gives the edge. Another underrated but crucial technology is recommendation engines. This systems blend collaborative filtering with the behavioral data to generate real time tailored product solutions. They're constantly learning and evolving as customer behavior shifts. Each of these technologies is supported by specific types of data. NLP uses text and speech. Computer vision requires image, video inputs, and predictive analytics works best with structured sales and transaction data. Together, these technologies from. To make all these technologies accessible. We have AI frameworks and platforms that development deployment and scaling. Let's begin by, its flexible. For building deep learning models in retail, it's often used for time series demand forecasting, image based inventory tracking, and recommendation engines. Retailers use tensor flow to forecast food traffic, or detect low stock products through shelf imagin. Next is PyTorch. A framework allowed by researchers and developers community for its ease of use and dynamic computation. PyTorch powers, NLP Drive chatbots, dynamic pricing models and sentiment analysis engines. A small business can use PyTorch to create a simple chatbot that lands customer preferences over, or monitors brand on social media. Moving on Microsoft Azure ai. This is a cloud-based suite of tools that helps to deploy AI models at scale. Azure supports things like fraud detection through anomaly detection models wise enabled shopping assistance, and automated product tagging. It's fantastic option for businesses that wanna rapid deployment without worrying about server infrastructure. Then we have Amazon SageMaker, a fully managed ML platform. It's perfect for hyper-personalization, supply chain optimization, and even real-time search ranking on e-commerce site retailers use SageMaker to fine tune product discovery a and detect patterns in customer. Each of these tools support a wide variety of data types from structured tables and transactional logs to image, video, audio, and text. Choosing the right framework depends on your goals. Are you optimizing logistic? Then go with TensorFlow or SageMaker. Building an intelligent chatbot. PyTorch and NLP are the best friends. The key takeaway here is you don't need to reinvent the wheel. The tools are measured, scalable, and readily available. Let's explore the fuel behind all this AI engines. The data retail AI doesn't work without large volumes of diverse high quality data, and it. It's about having the right kind of data. Let's break it down by time. First, we have time series data. This is historical sales records, foot traffic counts, seasonal trends, and other metrics measured over time. A models like LSTM or Arima variance can forecast demand or identify supply chain in efficiencies using this data. Then we have transaction data that records of individual purchases. This includes SKUs, payment methods, discounts, store IDs, customer IDs, and more. It's the backbone for pricing strategies, inventory, movement, and customer lifetime value analysis. Text data is crucial for understanding customers reviews. Descriptions and social media posts all contain valuable sentiment and intent. NLP models pass this unstructured text to extract keywords, classify emotions, or generate response suggestions. Another measure input is visual data. Images and videos feeds. Cameras are used in computer vision models for tasks like object, ization, theft detection, facial analysis, and product categorization. Audio data is becoming more common as voice commerce rises, voice assistance convert, voice to text integrated and trigger backend systems. And finally we have clickstream and behavioral data, which captures how users interact with apps or websites. Every click, scroll, or page record, uh, page exit is recorded. This data is used to improve search algorithms, personalized product, and reduce collectively. These data enable. AI to operate in a truly only FA omnichannel fashion. Bridging online and instore experiences that matters most is the data integration and quality, clean, well level, and regularly updated data sets, produced better models, performance and deeper insights. The right data foundation. The most powerful AI tools are not much useful and effective. To make this real, let's look at a practical case study that uses open source retail data to drive business decisions. I worked with the data set that include store sales history, customer demographics, and product transaction details. Using machine learning, especially recreation models and clustering, have identified actionable insights that could improve both revenue and. First, I segmented customers based on demographics and BA behavior. This allowed for the creation of targeted marketing campaigns that significantly increased conversion rates. Second, the recreation analysis revealed interesting trends. For instance, certain product categories performed in specific seasons and geographic areas. This information was used to fine tune product placement and advertising timing. Third, I observed inefficiencies in inventory stocking. Some stores constantly overstocked items with low demand using predictive models have recommended for optimal stock levels based on forecasted demand, which resulted in a 12% reduction in. I also applied a sentiment analysis to customer reviews. This helped the business to prioritize quality control in products that receive frequent negative feedback while amplifying visibility for the products with strong reviews. Lastly, I evaluated promotional impact using flipped modeling by comparing customer who. Future promotion strategies, ensuring that discounts were targeted where they have produced the highest return. All of these insights came from analyzing, structured and unstructured data through ai. What's more powerful is that these methods are scalable, whether you are a national chain or a small retailer, these same techniques can be applied to any business using open tools. Python and cloud services like Azure ml. As I conclude, AI is no longer optional In retail industry. It's essential, whether it's through smarter inventory control, highly targeted marketing, marketing, or seamless customer experiences. AI delivers real value. What's most exciting is that we are still at the beginning. Emerging technologies like reinforcement learning, generative AI, and H AI are just started to enter the retail world. Imagine autonomous stores that continuously optimize layout based on live customer behavior or generative models that create hyper product descriptions on the fly. We are also seeing more emphasis on ethical ai, ensuring fairness, transparency, and accountability in AI systems. This includes preventing a securing personal data and maintaining customer trust for developers and engineers. Focus on interdisciplinary collaboration. Partner with marketing largest finance team to solve real business problems using right tools. AI in retail is here to stay and it's evolving fast. Whether you a small startup, a global enterprise, embracing AI means embracing, continuous, improve. Here are the some screenshots from my article. You can find more details and findings in my articles, which is published with the title, artificial Intelligence for Store Assistance Exploring the integration of AI and ML in retail environments. My journals, articles are available on research and Google Scholar. Thank you so much for. Thank you. One second.