Interviews * Session Previews

Maximizing Business Value with Generative AI-Powered Knowledge Base

Amjad Almonzer

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In today’s digital age, the quality and accessibility of knowledge assets are paramount. Aramco recognizes this and has established a comprehensive corporate knowledge base that consolidates validated knowledge assets across various disciplines. But how do we ensure the quality of these assets and prevent the tedious cycle of reviewer rejections and resubmissions?

Amjad will unveil Aramco’s approach to maximize knowledge value unsing an in-house developed virtual assistant that not only provides users with guidelines and templates but also harnesses the power of generative AI to review the content and structure of knowledge assets, ensuring they meet a predefined criteria specific to each knowledge type. Moreover, it enhances the writing quality, creates abstracts, references, etc. making it more readable and reusable, thus amplifying its business value.

By participating in this session, attendees will:

  • Gain insights into Aramco’s AI-Empowered Knowledge Base.
  • Understand the role of AI in enhancing knowledge asset quality.
  • Learn how to leverage AI capabilities to optimize content for maximum business impact.
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Unlock Business Value through Applications of Large Language Models

Michelle Li

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Paychex manages a diverse range of textual data, including phone call transcripts, emails, chat logs, and survey feedback. A primary opportunity is to leverage this data effectively for actionable insights to improve the customer experience, and help small businesses succeed. The Paychex Data Science team employs a suite of large language models (LLM), covering traditional methods like word embeddings and phrase matching, to cutting-edge technology such as generative AI. Through these methods, we identify clients at retention risk, address economic distress signals, and categorize communications for product improvement. Our advanced LLM technology promises continuous enhancement to deliver tangible business value.

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