Most enterprise AI conversations still start in the wrong place: the model. But the organisations that succeed with AI will be the ones that understand the sum of the parts beneath it.
Taxonomies make information findable. Ontologies make it meaningful. Knowledge graphs make it connected. Rules, metadata, provenance and governance make it reliable. AI-ready data platforms bring these capabilities together so that models, RAG systems and agents can work with context they can trust.
This session will explore how semantic foundations are becoming critical infrastructure for enterprise AI. As AI moves from content generation to retrieval, reasoning, automation and agentic action, businesses need more than prompts and vector search. They need governed context: the ability to connect language, meaning, relationships, permissions, policies and evidence across the enterprise.
The session will also examine the rising importance of neuro-symbolic AI: combining the strengths of neural models with symbolic structures such as taxonomies, ontologies, knowledge graphs, rules and decision logic. This combined approach offers a practical path toward AI that is not only powerful, but explainable, controllable and safe to scale.
Three takeaways for the submission form
- Attendees will learn how taxonomy, ontology and knowledge graphs each contribute to AI-ready data, and why their real value comes from working together.
- They will understand why neuro-symbolic AI is becoming increasingly important as organisations move from generative AI pilots to RAG, GraphRAG and agentic workflows.
- They will leave with a practical framework for building semantic foundations that improve grounding, explainability, governance and trust in enterprise AI.