Microsoft have released a technical guide titled "Accelerating Generative AI Innovation with Cloud Migration" outlining four enterprise use cases where generative AI in the cloud drives business impact for IT and digital transformation leaders.
The guide emphasises that organisations using legacy or on-premises infrastructure face an inflection point where migration becomes "a business imperative for realising generative AI at scale." Without cloud flexibility, companies encounter higher costs, slower innovation cycles, and limited access to data that AI models require for meaningful results.
The first use case focuses on Retrieval-augmented generation (RAG) for real-time data access. RAG makes generative AI more accurate by pulling current information from SQL databases, APIs, and internal documents, reducing errors and hallucinations. Companies using RAG can automate live data retrieval, make informed decisions with latest domain-specific information, boost accuracy in interactive applications, lower operational costs, and tap proprietary data for competitive advantages. Azure AI Search, Azure OpenAI Service, and Azure Machine Learning provide necessary tools for responsive and secure RAG applications.
The second use case addresses embedding generative AI into enterprise workflows including ERP software, CRM, and content management platforms. This integration enables teams to optimise operations by analysing supply chain data, enrich customer experiences with personalised recommendations, and automate routine tasks like data entry and report generation. Azure OpenAI Service, Azure Logic Apps, and Azure API Management facilitate seamless integration with minimal disruption.
The third use case covers generative search for contextually aware responses as enterprise data grows. This approach combines hybrid search with advanced AI models to deliver context-aware responses based on real-time data. Organisations can improve customer support with relevant responses, surface critical insights from unstructured data, and summarise dense documents efficiently. Azure AI Search combines vector and keyword search while Azure OpenAI Service leverages models like GPT-4 for summaries and recommendations.
The fourth use case examines generative AI agents that autonomously perform tasks and adapt to user interactions. These agents help automate routine tasks, cut operational costs through reduced manual effort, scale without additional headcount, and improve service delivery with consistent customer experiences. Azure AI Foundry Agent Service simplifies agent deployment while Azure OpenAI Service powers content generation and data analysis.
The guide targets industries with rapidly fluctuating demands including e-commerce, financial services, manufacturing, communications, professional services, and healthcare. Migration to cloud-based AI enables access to distributed data sources in real-time while maintaining enterprise-grade security and advanced data integration capabilities.
Cloud migration represents a strategic transformation rather than technical upgrade for companies seeking AI leadership. The comprehensive Azure ecosystem provides infrastructure, compute capabilities, and integration tools necessary for moving from AI experimentation to driving real business value across enterprise workflows.