Add overview of AI use cases in retail

This document outlines various AI use cases in the retail sector, including shopping assistants, customer behavior analysis, product suggestions, sentiment analysis, and personalized content.

Author: brown9804

0_Azure/_industry-specific/1_Retail/0_Art_of_the_possible.md

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+# AI Use cases: Art of the possible - Overview 
+
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+[brown9804](https://github.com/brown9804)
+
+Last updated: 2025-10-23
+
+----------
+
+1. AI Shopping assistant: `my own copilot`, `want to chat with my data` [GPT-RAG Solution Accelerator](https://github.com/Azure/GPT-RAG). RAG-based chatbot to interact with manuals, guides, and product data for better search and personalization.
+
+      | [Public Architecture](#basic-architecture)  | [Private Architecture](./1_PrivateArchitecture.md) |  [Standard Zero-Trust Architecture](https://github.com/Azure/GPT-RAG) |
+      | --- | --- | --- | 
+      |  <img width="800" alt="image" src="https://github.com/user-attachments/assets/5444e87c-32af-44e8-aa86-22fe4082c4f4" />    |   <img width="800" alt="image" src="https://github.com/user-attachments/assets/af835493-2d76-4216-8df6-3e258c9db949"> | <img width="800" alt="image" src="https://github.com/user-attachments/assets/67e7d9eb-f757-4fe9-85f4-d5bb08b9e55f" /> |
+      
+2. Analyze customer behavior and feedback for trend discovery. [Demo: How to configure the Microsoft Fabric (Power BI) MCP connector in Copilot Studio](https://github.com/MicrosoftCloudEssentials-LearningHub/Fabric-MCP-Agent2Agent)
+
+  > Low code approach (Copilot Studio Agents):
+  
+  <img width="1141" height="669" alt="MCP-Fabric-Agent2Agent-CopilotStudio-databricks+data-agents+copilot-Studio drawio" src="https://github.com/user-attachments/assets/76be59e3-f48e-4c01-aa58-464463658d92" />
+  
+  > Full code approach (AI Foundry Agents):
+  
+  <img width="1141" height="668" alt="MCP-Fabric-Agent2Agent-CopilotStudio-data agent Fabric + AI Foundry drawio" src="https://github.com/user-attachments/assets/5742dc3a-427d-4ab5-862d-ba291a31fc35" />
+
+3. Enable AI-driven suggestions for complementary products
+4. Real-time sentiment analysis and campaign triggers based on trends.
+5. Improve data taxonomy and metadata
+6. Enhance user experience with personalized content blocks.
+
+
+   
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