Lulit Tesfaye: Semantic Architectures for the AI Era – Episode 52

Lulit Tesfaye

Generative AI has prompted a flurry of experimentation in enterprises, resulting in a parade of failed pilots, unproven PoCs, and unrealized return on investment.

Lulit Tesfaye helps companies improve and optimize their AI capabilities by adding semantics to their enterprise architectures. This puts their precious knowledge assets in a context that machines can actually work with. A crucial part of that context is the interoperability that standards like RDF enable.

We talked about:

her work at Enterprise Knowledge as Partner & Vice President, Knowledge, Data, and AI Solutions

the publication of Bridging Knowledge, Data, and AI: Harnessing the Semantic Layer Framework to Drive Intelligence, which she co-authored with colleagues at EK

her professional path from software engineering and applications development to her current role

how generative AI has increased data teams' workloads, leading to numerous pilots and PoCs, which has exposed the need in enterprises for semantic context

her definition of a semantic layer

"knowledge assets," a foundational concept that she uses to talk about data, content, information, expertise, and any other assets to which can append metadata

how she and colleagues see the distinction between the two types of semantic layers: data and analytics layers and semantically modeled layers, and the benefits of the latter

the framework she uses to convey the benefits of a semantic layer to executives