Scalable GenAI Systems for Enterprise Decision Intelligence: Architecture and Adoption Strategies

Abstract

Introduction: In the hustle and bustle of today's business landscape, scalable Generative AI systems are stepping up by blending cutting-edge AI, solid data frameworks, and flexible designs to elevate strategic decision-making. With the power of Azure OpenAI's GPT-4o and GPT-4o mini models, these systems allow for natural language queries across a variety of enterprise databases, delivering real-time, precise insights. When integrated with current data warehouses and business intelligence tools, this setup enhances speed, consistency, and compliance, revolutionizing Decision Intelligence in sectors like manufacturing and telecommunications.

Objectives: In today's world, businesses are juggling a lot of complex data, and trying to pull information from different databases can be a real hassle—it's often slow and requires a lot of manual effort. That's where GenAI-driven text-to-SQL systems come in. They allow for quick, secure insights across various databases, which really helps improve agility and supports better decision-making.

Methods: The proposed architecture takes advantage of Azure OpenAI’s GPT-4o and GPT-4o mini models for Text-to-SQL, allowing users to query various enterprise databases like Snowflake, Databricks, and Oracle using natural language. It features a unified semantic layer, modular adapters, and federated query orchestration through Azure Synapse, all while ensuring strong security with Azure AD and RBAC. The user experience is enhanced with self-service UX/UI, API-driven

prompts, and ongoing monitoring to improve accuracy and usability. To facilitate a smooth transition, a change management plan based on Kotter’s 8-Step Model is in place, focusing on stakeholder engagement, training, and a phased rollout, ultimately enhancing enterprise Decision Intelligence with real-time, actionable insights.

Results: The Azure-based GenAI Text-to-SQL architecture has truly transformed enterprise Decision Intelligence. It slashed query times by an impressive 70%, brought API latency down to under 200 ms, and empowered non-technical users to craft precise SQL queries using natural language, which means less dependence on IT. The ability to seamlessly query across databases like Snowflake, Databricks, and Oracle has really sharpened decision-making. Plus, with Azure AD and RBAC in place, security compliance is a solid 100%. Thanks to Azure services, deployment is scalable and boasts a reliability rate of 99.9%. A case study in the supply chain sector revealed that query times plummeted from days to mere minutes, leading to a 50% boost in analyst productivity and an 80% drop in errors, all of which enhances strategic agility.

Conclusions: A scalable GenAI Text-to-SQL setup that leverages Azure OpenAI, FastAPI, and various Azure services is transforming the way enterprises approach Decision Intelligence. It allows users to make natural language queries, gain insights across different databases, and maintain strong governance. This not only lessens the dependency on IT but also enhances agility through effective change management.