Anyone working in a large organization has likely asked this question: Why is it that I can seemingly find anything on the internet but I can’t seem to find anything inside my organization? It is counter-intuitive that it’s easier to organize the vast quantity of information on the public internet than it is to organize the smaller amount of information inside a single organization.

The reality is that enterprise knowledge management and search is very difficult. Data does not reside in easily organized forms. It is spread across systems which provide varying levels of access. Knowledge can be fleetingly exchanged in communication systems. And each individual person has their own access rights, creating a complex challenge.

These challenges may be amplified by large language models in the enterprise which seek to help people with analytical and creative tasks by tapping into an organization’s knowledge. How can these systems access enough enterprise data to develop a useful level of understanding? How can they provide the best answers to each individual that follows data access governance requirements?

To answer these questions, we talked with Arvind Jain, the CEO of Glean, which provides AI-powered workplace search. Glean searches across an organizations applications to build a trusted knowledge model that respects data access governance when presenting information to users. Glean’s knowledge models also provide a way for enterprises to introduce the power of generative AI while providing boundaries for its use that can be challenging to create.

Prior to founding Glean, Arvind co-founded Rubrik, one of the fastest growing companies in cloud data management. For more than a decade Arvind worked at Google, serving as a Distinguished Engineer, leading teams in Search, Maps, and YouTube.

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Arvind Jain: Glean, Enterprise Search, and Generative AI

Stay Human, from the Artificiality Institute