Knowledge Graphs Improve GenAI — Validating Results Builds Trust for Organizations

This presentation is based on an article co-authored by me Joakim Nilsson together with the CTO of Capgemini Sweden Magnus Carlsson and Tomaz Bratanic from Neo4j.

Generative AI has the potential to revolutionize decision-making, but trust in its outputs is critical. One challenge is the unstructured nature of the large language models (LLMs) that GenAI relies on, making it difficult to understand how it arrives at specific conclusions. Knowledge graphs can address this by providing structured, interconnected data, improving the accuracy and reliability of GenAI's results.

Knowledge graphs act as a bridge between user queries and the data, allowing large language models to use tailored query templates for specific tasks like supply chain analysis or business intelligence. This approach ensures that insights generated are more relevant and deeply informed by underlying data structures. By using multiple, dynamic templates based on context, GenAI can solve complex business problems more effectively.

Additionally, knowledge graphs democratize access to AI by allowing non-experts to inspect how an AI reached its conclusions. This transparency builds trust, as the results are explainable, repeatable, and verifiable. In industries like manufacturing, life sciences, and telecommunications, this approach can enhance decision-making and free experts to focus on more specialized tasks.

As GenAI continues to evolve, the integration of knowledge graphs will play a vital role in ensuring that its outputs remain accurate and trustworthy, empowering businesses to embrace AI with confidence.