AI’s Impact on Data Silos and Knowledge Hubs

A Single Source of Truth

Although organizations must do a fair amount of work to architect solutions on the back end to unite access to information across knowledge hubs, language models are oftentimes the interface for engaging with that content. This facet of LLMs shouldn’t be overlooked as a means of bridging the gaps among data silos. In this regard, LLMs serve as an intermediary for users of different aptitudes and departments.

The classic conversational AI approach exemplified by today’s language models “really helps in terms of getting more access to data,” Pava said. “If I’m working here, and I need to interact with this other group, and I need other information that this other group knows, with conversational AI, I can ask across it here, and I don’t have to have the domain knowledge to interpret what I’m asking.” 

Scratching the Surface

Although language models simplify the requirements for interacting with enterprise content across knowledge hubs, the onus is still on the enterprise to unify access to them while minimizing the effects of silos. Various forms of search, including retrieval-augmented generation (RAG), GraphRAG, other knowledge graph applications, agent-based architectures, and more assist with these endeavors.

“I think we’re just scratching the surface,” Bixby opined. “This is going to explode into more and more innovation— and more access to data—in the same way that the models are moving to the point now where they can legitimately do the work of a good software developer.”

The impact of AI on both data silos and knowledge hubs is proving to be powerful in solving some age-old problems, including curtailing the effects of siloed information while increasing the value of knowledge hubs. It can only become more impactful going forward.