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At the enterprise level, keeping track of internal data and information has become a huge challenge. At this VB Spotlight event, learn how new generative AI environments are unlocking the full potential of data in enterprise environments and reducing time-to-learning.
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With the increasing complexity and distributed nature of organizations – distributed teams, remote work and a multitude of knowledge systems, it is difficult to track data across the entire enterprise knowledge ecosystem and employees are taking their toll.
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This knowledge access challenge “causes a loss of productivity and frustration that we are starting to see, leading to less engagement with our employees,” says Phu Nguyen, Head of Generative AI Impact on Enterprise Search: A Breakthrough for Businesses.
He was joined by Jean-Claude Monney, Digital Workplace, Technology and Knowledge Management Advisor, and Eddie Zhou, Founding Engineer, interviewed at Glean to discuss the emergence of an evolutionary leap in workplace-specific search tools powered by generative AI that gives employees full access to the knowledge they need and its context anywhere in the organization.
The Evolution of Enterprise Search
Traditional enterprise search cannot reach all the knowledge in an organization that is distributed across multiple systems. It can extract structured knowledge, such as data found in Jira, Confluence, intranets and sales portals, but unstructured knowledge, information communicated via instant messaging, Teams, Slack and email, was uncharted territory, difficult to collect in any helpful context way, adds Nguyen.
“The knowledge management paradigm has changed significantly,” he says. “How do you create a system that can view both structured and unstructured data and provide the answers you are 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 job effectively, no matter where that knowledge resides.
“Companies are now building searches specifically for the workplace, built for internal search that works across the entire internal system,” explains Nguyen. “Most importantly, they are built around a knowledge graph that returns a search that is more relevant to your employees. This is all very exciting for us as we see it as part of our employee helpdesk strategy. Previously, it was just an intranet and our support portal, but now we have this job search engine that can combine information from multiple systems in our organization.”
How organizations can leverage generative AI
There are three main ways companies can use generative AI, and they are groundbreaking, says Monney. The first, he says, is the benefits of an NLP interface.
“Knowledge time is the new business currency,” says Monney. “What we’ve seen with generative AI is a quantum leap in user experience. ChatGPT has democratized how to communicate with the system and get very concise answers.”
At home, users have become accustomed to the ease and convenience of natural language interfaces such as Alexa and Siri; adds that generative AI brings this user experience to the workplace, giving employees not only an enterprise search tool but also a digital knowledge assistant. It enables employees to quickly find not only information, but also precise answers, increasing productivity and efficiency, especially in complex decision-making scenarios. Generative AI can also go beyond answering individual questions and assist in more complex decision-making processes by providing users with synthesized and relevant information without the need for direct queries.
Generative AI can also automate repetitive tasks and streamline workflows — for example, generative AI-powered chatbots can handle customer service queries, product recommendations, or simply help book appointments. This frees up time for more complex tasks and significantly increases productivity.
Finally, these generative AI solutions can be fine-tuned for industry-specific and case-specific applications. Companies can add their own knowledge base to large language models used by generative AI to improve relevance and time to knowledge acquisition.
Bringing generative AI to the workplace
“Bringing this technology into the workplace is not easy,” warns Zhou. It requires a knowledge model that consists of three pillars. The first is the knowledge and context of the company. A ready-made model or system, without the right connection with the right knowledge and the right data, will not be functional, correct or appropriate.
“You need to build generative AI into a system that has the knowledge and context of the business,” he explains. “This allows this trusted model of knowledge to arise from the combination of these things. Search is one method that can provide this company with knowledge and context, coupled with generative AI. But it’s one of several.
The second pillar of the trusted knowledge model is authorization and data management, i.e. knowing, as a user interface with a product and system, what information it should and should not have access to.
“We talk about knowledge in the company as if it were a free-floating currency, but in reality different users and different employees in the company have access to different parts of the knowledge,” he says. “It’s objective and clear when it comes to documents. You may be part of a group alias that has access to a shared drive, but there are many other things that a person shouldn’t be able to access, and in a generative environment it’s extremely important to get it right.”
The third and last is referentiality. As the product interface has evolved, users need to build trust in the system and be able to verify where the system is getting information from.
“Without that kind of background, it’s hard to build trust, and that can lead to uncontrollable factual errors and hallucinations,” he says, especially in a corporate system where each user is accountable for their own decisions.
Emerging possibilities of generative artificial intelligence
Generative AI means moving from questions to decisions, says Zhou, reducing the time to knowledge. A basic corporate search may result in a series of documents to read, leaving the user to search for the information they need. With enhanced corporate search where you must answer first, you don’t ask these questions individually; instead, they can express the basic journey, the general decisions that need to be made, and the LLM agent brings it all together.
“This generative technology, when combined with searches, 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. “The LLM agent can find all the information I might need and run various searches over and over again, collect that information, synthesize it for me, and deliver it to me.”
For more on how generative AI and large language models can change 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
- Unlocking the full potential of data in enterprise environments with generative AI
- Recognition of the importance of a trusted knowledge model for generative AI
- Making information easier to access and discover to increase employee productivity
- Creating more intelligent, personalized and effective experiences
- Phu Nguyenhead of the digital workplace, Pure Storage
- Jean-Claude MonneyDigital Workplace, Technology and Knowledge Management Advisor
- Eddie ZhouFounder, Intelligence, Glean
- Art ColeModerator, VentureBeat
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