Lucidea’s Lens: Knowledge Management Thought Leaders Part 72 – Tom Davenport

Tom Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, a Fellow of the MIT Center for Digital Business, and an independent senior advisor to Deloitte Analytics.

An author and co-author of 20 books and more than 200 articles, he helps organizations to transform their management practices in digital business domains such as artificial intelligence, analytics, information and knowledge management, process management, and enterprise systems.

Tom pioneered the concept of “competing on analytics” with his best-selling 2006 Harvard Business Review article and 2007 book by the same name. Since then, he has continued to provide cutting-edge insights on how companies can use analytics and big data to their advantage, and then on artificial intelligence. Tom’s book Only Humans Need Apply: Winners and Losers in the Age of Smart Machines offers tangible tools for individuals who need to work with cognitive technologies. In The AI Advantage: How to Put the Artificial Intelligence Revolution to Work, he provides a guide to using artificial technologies in business. His latest book, All-in On AI: How Smart Companies Win Big with Artificial Intelligence provides leaders and their teams with the insights to help their own companies become AI-fueled.

Books

 

How to Train Generative AI Using Your Company’s Data

Leveraging a company’s proprietary knowledge is critical to its ability to compete and innovate, especially in today’s volatile environment. Organizational innovation is fueled through effective and agile creation, management, application, recombination, and deployment of knowledge assets and know-how. However, knowledge within organizations is typically generated and captured across various sources and forms, including individual minds, processes, policies, reports, operational transactions, discussion boards, and online chats and meetings. As such, a company’s comprehensive knowledge is often unaccounted for and difficult to organize and deploy where needed in an effective or efficient way.

Outline

• The Technology for Generative AI-Based Knowledge Management
  • Training an LLM from Scratch
  • Fine-Tuning an Existing LLM
  • Prompt-tuning an Existing LLM
• Content Curation and Governance
• Quality Assurance and Evaluation
• Legal and Governance Issues
• Shaping User Behavior
  • Knowledge of what types of content are available through the system
  • How to create effective prompts
  • What types of prompts and dialogues are allowed, and which ones are not
  • How to request additional knowledge content to be added to the system
  • How to use the system’s responses in dealing with customers and partners
  • How to create new content in a useful and effective manner
• Everything Is Moving Very Fast

Competing on Analytics: The New Science of Winning

 

Analytics at Work: How to Make Better Decisions and Get Better Results

Five levels and five factors for building analytical capability