Skip to main content
Mobile
  • Finance, Accounting & Economics
  • Global Business Management
  • Management, Leadership & Organisation
  • Marketing & Sales
  • Strategy
  • Technology & Operations
HS Talks HS Talks
Subjects  
Search
  • Notifications
    Notifications

    No current notifications.

  • User
    Welcome Guest
    You have Limited Access The Business & Management Collection
    Login
    Get Assistance
    Login
    Forgot your password?
    Login via your organisation
    Login via Organisation
    Get Assistance
Finance, Accounting & Economics
Global Business Management
Management, Leadership & Organisation
Marketing & Sales
Strategy
Technology & Operations
You currently don't have access to this journal. Request access now.
Practice paper

What executives need to know about knowledge management, large language models and generative AI

Seth Earley
Applied Marketing Analytics: The Peer-Reviewed Journal, 9 (3), 215-229 (2023)
https://doi.org/10.69554/YQBV7690

Abstract

This paper discusses the opportunities and risks presented by large language models (LLMs), which power the popular and widely adopted Chat-GPT types of applications. The potential benefits include support for enhancing the customer journey and efficient management of an ever-increasing volume of information for employees. Risks include hallucinations (made up answers by generative AI that are not factually correct), exposure of corporate intellectual property (IP) to training models, lack of traceability and audit trails and misalignment with brand guidelines. The approach to handling risk described in this paper is retrieval-augmented generation (RAG), which references corporate knowledge and data sources in order to identify precise answers and retrieve exactly what users want. The paper also outlines the need for a knowledge architecture which enables enriched embeddings into vector databases which retain the context of intelligently componentised content. Using RAG requires knowledge hygiene and metadata models, and the paper discusses an experiment in which results were measured with and without the knowledge architecture. The improvement was significant: 53 per cent of questions were answered correctly without the model versus 83 per cent with the model. The use of RAG virtually eliminated hallucinations, secured corporate IP and provided traceability and an audit trail.

Keywords: RAG; retrieval augmented generation; generative AI; ChatGPT; LLMs; large language models; KM; knowledge management; LLM challenges; LLM solutions; knowledge models; metadata models; knowledge architecture

The full article is available to subscribers to the journal.

Already a subscriber? Login or review other options.

Author's Biography

Seth Earley is Founder and CEO of Earley Information Science, a professional services firm working with the world's leading brands. He has been working in the information management space for over 25 years. His firm solves challenging problems for global organisations with a data/information/knowledge architecture-first approach. Seth is author of the award-winning book ‘The AI-Powered Enterprise’, which outlines the knowledge and information architecture groundwork needed for enterprise grade generative AI. He coined the industry catchphrase, ‘There's No AI Without IA’ in 2016, referring to the critical value of information architecture for artificial intelligence applications.

Citation

Earley, Seth (2023, December 1). What executives need to know about knowledge management, large language models and generative AI. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 9, Issue 3. https://doi.org/10.69554/YQBV7690.

Options

  • Download PDF
  • Share this page
    Share This Article
    Messaging
    • Outlook
    • Gmail
    • Yahoo!
    • WhatsApp
    Social
    • Facebook
    • X
    • LinkedIn
    • VKontakte
    Permalink
cover image, Applied Marketing Analytics: The Peer-Reviewed Journal
Applied Marketing Analytics: The Peer-Reviewed Journal
Volume 9 / Issue 3
© Henry Stewart
Publications LLP

The Business & Management Collection

  • ISSN: 2059-7177
  • Contact Us
  • Request Free Trial
  • Recommend to Your Librarian
  • Subscription Information
  • Match Content
  • Share This Collection
  • Embed Options
  • View Quick Start Guide
  • Accessibility

Categories

  • Finance, Accounting & Economics
  • Global Business Management
  • Management, Leadership & Organisation
  • Marketing & Sales
  • Strategy
  • Technology & Operations

Librarian Information

  • General Information
  • MARC Records
  • Discovery Services
  • Onsite & Offsite Access
  • Federated (Shibboleth) Access
  • Usage Statistics
  • Promotional Materials
  • Testimonials

About Us

  • About HSTalks
  • Editors
  • Contact Information
  • About the Journals

HSTalks Home

Follow Us On:

HS Talks
  • Site Requirements
  • Copyright & Permissions
  • Terms
  • Privacy
  • Sitemap
© Copyright Henry Stewart Talks Ltd

Personal Account Required

To use this function, you need to be signed in with a personal account.

If you already have a personal account, please login here.

Otherwise you may sign up now for a personal account.

HS Talks

Cookies and Privacy

We use cookies, and similar tools, to improve the way this site functions, to track browsing patterns and enable marketing. For more information read our cookie policy and privacy policy.

Cookie Settings

How Cookies Are Used

Cookies are of the following types:

  • Essential to make the site function.
  • Used to analyse and improve visitor experience.

For more information see our Cookie Policy.

Some types of cookies can be disabled by you but doing so may adversely affect functionality. Please see below:

(always on)

If you block these cookies or set alerts in your browser parts of the website will not work.

Cookies that provide enhanced functionality and personalisation. If not allowed functionality may be impaired.

Cookies that count and track visits and on website activity enabling us to organise the website to optimise the experience of users. They may be blocked without immediate adverse effect.