Responsible enterprise decisions with knowledge-enriched generative AI | Deloitte Netherlands


Responsible enterprise decisions with knowledge-enriched generative AI

Why is it essential for enterprise-level generative AI to incorporate knowledge graphs?

By harnessing the combined power of knowledge graphs and generative AI, enterprises can unlock significant potential for knowledge-driven decision-making, innovation, and operational efficiency. Knowledge graphs, with their structured representation of a domain, enhance the performance of generative AI by providing context, validating outputs, and reducing biases, thereby ensuring alignment with strategic business objectives. Conversely, generative AI enriches knowledge graphs by filling knowledge gaps and predicting future states, thereby increasing the utility, accuracy, and relevance of these graphs. The synergy between knowledge graphs and generative AI serves as a game-changer for businesses, driving transformative impacts across various organisational functions.

As we navigate the dynamic field of artificial intelligence, the integration of knowledge graphs with generative AI stands out as a transformative approach to enable trustworthy and responsible enterprise decisions. Knowledge graphs are characterised by their structured representation of knowledge, where entities (often denoting concepts or objects) and the relationships between them are explicitly defined. They provide a reliable and actionable map of knowledge to not only capture explicit facts but also enable semantic understanding, allowing for the deduction of implicit knowledge.

On the other hand, generative AI is characterised by its ability to produce new content, be it text, images, music, or other forms of data, that mirrors or emulates the patterns seen in its training data.

Merging these two powerful realms into a knowledge-enriched AI ecosystem facilitates the production of outputs that are not only precise but imbued with contextual richness.

Why you need to read this whitepaper

This whitepaper is the start of our journey into knowledge-enriched AI where we touch upon the following topics:

Exploring the world of generative AI and its diverse use cases

  • What is generative AI?
  • A selection of high-impact use cases of generative AI

Potential pitfalls: navigating the risks of generative AI

  • How does generative AI work?
  • Technical risks of current generative AI models 
  • Regulatory risks of current generative AI models 

Harnessing knowledge graphs to mitigate the risks of generative AI

    • What is a knowledge graph?
    • Logical inference and reasoning
    • How do knowledge graphs help to mitigate the risks of generative AI?
    • An example scenario: a technical assistant chatbot

      Empowering organisations with knowledge-enriched generative AI

        • Navigating innovation & knowledge management leveraging knowledge-enriched generative AI

        A comprehensive approach to construct knowledge-enriched generative AI

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