How generative AI is changing the knowledge paradigm for companies

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With the increasing complexity and distributed nature of organizations—remote teams, remote work, and a multitude of knowledge systems—trailing data across an entire enterprise knowledge ecosystem is difficult, and workers are feeling the cost.


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This knowledge access challenge “results in a loss of productivity and frustration that we’re starting to see, leading to a decline in our employee engagement,” says Phu Nguyen, head of digital workplace at Pure Storage during the recent VB Spotlight, “The Impact of Generative AI on Enterprise Search: A Game Changer for Enterprises.”

He was joined by Jean-Claude Monney, Digital Workplace, Technology and Knowledge Management Consultant, and Eddie Zhou, Founding Engineer, Intelligence at Glean to discuss the emergence of the evolutionary leap in workplace-specific search tools. , powered by generative AI, giving employees full access to the knowledge they need, and its context, anywhere in the organization.

Traditional enterprise search cannot reach all of an organization’s knowledge, which is distributed across multiple systems. You can mine structured knowledge, such as the data found in Jira, Confluence, intranets, and sales portals, but unstructured knowledge—information communicated via IM, Teams, Slack, and email—has been uncharted, difficult territory. to corner in any useful context. way, adds Nguyen.

“The knowledge management paradigm has changed significantly,” he says. “How can you have a system that can analyze structured and unstructured data and give you the answers you’re ultimately looking for? Not the information you need, but the answer you’re looking for.”

Solutions that integrate with multiple systems and use generative AI can address these challenges and help employees find the information they need to do their jobs effectively, no matter where that knowledge resides.

“Companies are now building searches specifically for the workplace, built for internal searches that work across their entire internal system,” Nguyen explains. “More importantly, they are based on a knowledge graph that returns a search that is most relevant to your employees. This is all very exciting for us because we think of this as part of our employee information hub strategy. Previously it was just an intranet and our support portal, but now we have this workplace search that can connect information across multiple systems within our organization.”

How organizations can take advantage of generative AI

There are three main ways that companies can take advantage of generative AI, and they are revolutionary, Monney says. First, she says, there are the benefits that an NLP interface brings.

“Time to know is a new business currency,” Monney says. “What we’ve seen with generative AI is this quantum leap in user experience. ChatGPT has democratized the ways of talking to a system and getting very short answers”.

At home, users have grown accustomed to the ease and convenience of natural language interfaces like Alexa and Siri; Generative AI brings that user experience to the workplace, giving workers not just an enterprise search tool, but also a digital knowledge assistant, he adds. It enables employees to find not only information, but also accurate answers quickly, increasing productivity and efficiency, especially in complex decision-making scenarios. Generative AI also has the potential to go beyond answering individual questions and assist in more complex decision processes, providing users with relevant, synthesized information without the need for explicit queries.

Generative AI can also automate repetitive tasks and streamline workflows; For example, chatbots powered by generative AI can handle customer service inquiries, product recommendations, or simply help with booking appointments. That frees up time for more complex tasks and greatly increases productivity.

Lastly, these generative AI solutions can be fine-tuned for specific industry and case use. Companies can add their own corpus of knowledge to the large language models used by generative AI, to improve relevance and time-to-knowledge.

Bringing generative AI to the workplace

“Bringing this technology to the workplace is not easy,” Zhou cautions. It requires a knowledge model, which is made up of three pillars. The first is the knowledge and context of the company. A ready-to-use model or system, without being correctly connected with the correct knowledge and the correct data, will not be functional, correct or relevant.

“You need to incorporate generative AI into a system that has the knowledge and context of the business,” he explains. “That allows this model of trustworthy knowledge to form from the combination of these things. Search is one of those methods that can give this company insight and context, along with generative AI. But it’s one of many.”

The second pillar of the trusted knowledge model is permissioning and data governance, or being aware, when a user interacts with a product and a system, what information they should and should not have access to.

“We talk about knowledge in the company as if it were a free-flowing currency, but the reality is that different users and different employees in a company have access to different pieces of knowledge,” he says. “That is objective and clear when it comes to documents. It might be part of a group alias that has access to a shared drive, but there are so many other things that a given person shouldn’t have access to, and in a generative setup it’s incredibly important to get it right.”

The third and last one is the referenciabilidad. As the product interface has evolved, users must build trust in the system and be able to verify where the system is pulling information from.

“Without that kind of provenance, trust is hard to build and can lead to hallucinations and runaway factual errors,” he says, especially in an enterprise system where each user is responsible for their decisions.

The emerging possibilities of generative AI

Generative AI means moving from questions to decisions, Zhou says, reducing the time to insight. Basic enterprise search can display a series of documents to read, leaving the user to search for the information they need. With the answer-first augmented enterprise search, the user doesn’t ask those questions individually; instead, they can express the underlying journey, the general decisions that need to be made, and the LLM agent brings it all together.

“This generative technology, when we combine it with search, and not just individual searches, gives us the ability to say, ‘I’m going on a business trip to X. Tell me everything I need to know,’” he says. . “An LLM agent can go and find out all the information that he might need and repeatedly run different searches, collect that information, synthesize it for me, and send it to me.”

To learn more about the ways generative AI and great language models can transform the way knowledge is accessed and used in enterprises, types of use cases, and more, don’t miss this VB Spotlight! !

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  • Understanding the present and future of AI in enterprise search
  • Unleash the full potential of data in enterprise environments with generative AI
  • Recognize the importance of a trusted knowledge model for generative AI
  • Facilitate information access and discovery to improve employee productivity
  • Create smarter, more personalized and effective experiences


  • Phu NguyenHead of Digital Workplace, Pure Storage
  • jean claude monneyDigital Workplace, Technology and Knowledge Management Advisor
  • eddie zhouFounding Engineer, Intelligence, Glean
  • cole artModerator, VentureBeat

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