When Does Your AI Need a Knowledge Graph?
A practical decision guide for enterprise teams evaluating graph retrieval, shared business definitions and evidence-linked AI.
Start with the question, not the database
A knowledge graph can be useful when the workflow depends on explicit relationships: which supplier provides a component, which system supports a service, or which portfolio holds an instrument. First identify questions your current approach cannot answer reliably. A graph should address a demonstrated gap.
When simpler retrieval may be enough
If users primarily need passages from a small set of well-structured documents, conventional search or vector retrieval may be sufficient. Establish that baseline before adding modelling, entity resolution and graph operations. More architecture introduces more work to maintain.
Signals worth investigating
Consider a graph when identities vary across sources, answers require several relationship steps, shared definitions matter across teams, or provenance needs to remain attached to facts. These signals justify an evaluation; they do not guarantee the approach will improve results.
Decide with a bounded test
Choose representative questions and reserve examples for evaluation. Compare answer correctness, source support, retrieval relevance, latency and operating cost. Review unanswered and incorrect cases. Expand only if the evidence and ownership model justify the additional complexity.