Adding AI to an existing .NET application should not mean creating an unmanaged side project. The AI feature should respect the same identity, authorization, data access, deployment, logging, and operational standards as the rest of the system.
Start with a practical use case. Good early candidates include document search, support assistance, internal knowledge lookup, summarization, and workflow drafting. Avoid starting with broad “AI everywhere” goals.
For RAG scenarios, define sources, permissions, citations, and evaluation before building the chat interface. For agent workflows, define approved actions, tool boundaries, and approval rules.
.NET teams can integrate Azure OpenAI, Azure AI Search, Semantic Kernel, and existing APIs without abandoning their platform. The key is to keep AI inside the architecture rather than letting it become a disconnected script.
AI adoption works best when it improves one workflow, proves value, and then expands through a controlled roadmap.