Episode
Enterprise Wide Search 37: Lulit T, Joe H & Zach W - Why AI Needs Enterprise Semantics
- Podcast
- Enterprise Wide Search
- Published
- Jul 10, 2026
- Duration seconds
- 1860
- Processing state
not_requested
Actions
POST https://stenobird.com/v1/public/podcasts/enterprise-wide-search-7039824/episodes/enterprise-wide-search-37-lulit-t-joe-h-zach-w-why-ai-needs-enterprise-semantics/transcription-requests
Idempotently request low-priority transcript generation for this episode.GET https://stenobird.com/podcast/enterprise-wide-search-7039824/enterprise-wide-search-37-lulit-t-joe-h-zach-w-why-ai-needs-enterprise-semantics.md
Read the agent-friendly Markdown representation of this episode resource.
Summary
In this episode, Ole Olesen-Bagneux sits down with Lulit Tesfaye, Joseph Hilger, and Zach Wahl from Enterprise Knowledge to explore why the future of enterprise AI depends on connecting knowledge, data, and semantics. Drawing from their new book, Bridging Knowledge, Data, and AI, they explain why organizations can no longer treat structured data, unstructured content, taxonomies, ontologies, glossaries, and knowledge graphs as separate disciplines. As AI pushes enterprises to make better use of context, meaning, and organizational knowledge, the need for a true semantic layer becomes more urgent than ever. Together, they explore: Why Enterprise Knowledge was created, and how its mission has evolved What a semantic layer really is, beyond vendor diagrams and buzzwords Why knowledge assets include much more than structured data How taxonomies, ontologies, business glossaries, and knowledge graphs work together Why AI is forcing data, IT, and knowledge teams to collaborate in new ways The risks of competing semantic layer initiatives inside the same organization 🎧 Tune in for a conversation on semantics, enterprise knowledge, AI readiness, and why the next phase of data work depends on bridging the silos that organizations have built for decades. Hosted on Ausha. See ausha.co/privacy-policy for more information.