Data integration combines and coordinates information from multiple sources so an AI system can process it consistently. In established products, data may be scattered across databases, stored in incompatible formats, or need substantial cleanup before producing value. Integration therefore includes practical work on governance and infrastructure, not just connecting systems. section 2.1.4
For language-model applications, integration connects custom organizational knowledge—such as wikis, knowledge bases, and internal documents—to generated responses. Retrieval-augmented generation retrieves relevant, current information and supplies it as context, reducing generic or outdated output and helping users trust the result. section 7.0
From scattered knowledge to useful answers
An analytics product may need to combine information across existing repositories before adding AI. A language-model application can then retrieve relevant material from wikis and internal documents instead of relying only on prompts, making responses more specific to the organization. section 2.1.4 section 7.0
Use the scenario map to distinguish how integration work changes between established products, new AI efforts, and other organizational contexts. section 2.1.4
