Insights from KM and AI Webinar

Aug 23, 2024

Mindmap

Notes on KM and Enterprise AI Webinar

Introduction

  • Thank you for joining; participants from multiple countries.
  • Facilitators introduced: Tatiana, Fernando, Elliot, and Sarah May.
  • Focus: The intersection of Knowledge Management (KM) and Enterprise AI.
  • Goal: Equip knowledge managers on how to succeed using AI methods.

Importance of KM and Enterprise AI

  • The KM landscape is rapidly evolving, with AI becoming crucial.
  • Webinar objectives:
    • Understand course content on KM and Enterprise AI.
    • Learn for personal and organizational growth.
    • Open for questions at the end of the session.

Facilitator Backgrounds

  • Tatiana Vaki: Senior Knowledge Management Consultant and Taxonomist with 20 years in KM.
  • Fernando Agil: Data Analytics and Machine Learning Specialist with 10 years of experience.
  • Elliot R.: Technical Consultant specializing in semantic AI solutions.
  • Sarah May: Senior Semantic Engineering Consultant focusing on ontologies and KM strategy.

Webinar Agenda

  1. Overview of the class.
  2. Detailed exploration of four modules:
    • Introduction to KM and Enterprise AI.
    • Common KM challenges and AI solutions.
    • Approaches to Enterprise AI solutions.
    • Enterprise AI implementation and roadmapping.
  3. Q&A session at the end.

Class Overview

  • Duration: Approximately 12 hours over two days.
  • Day 1:
    • Introduction to KM and Enterprise AI.
    • Discussing top AI use cases in industry.
  • Day 2:
    • Hands-on conceptual model design.
    • Approaches to Enterprise AI solutions.
    • Implementation roadmap development.

Learning Objectives

  • Understand fundamental concepts of KM and Enterprise AI.
  • Identify organizational readiness for AI solutions.
  • Explore industry best practices and tools for implementing Enterprise AI.

Module 1: Introduction to KM and Enterprise AI

  • Define KM using five pillars: people, processes, content, culture, enabling technologies.
  • Define Enterprise AI: leveraging machine capabilities for knowledge and data discovery.
  • Discuss importance of utilizing AI in KM:
    • 85% of organizational content is unstructured.
    • AI predicted to contribute 45% of economic gains by 2030.
  • Overview of common Enterprise AI categories.

Module 2: KM Challenges with AI Solutions

  • Common KM challenges:
    • Application overload: many information systems complicate data retrieval.
    • Content explosion: untagged content hampers processing.
    • Knowledge structure and capture: unstructured information needs structuring.
    • Collaboration silos: lack of knowledge sharing across teams.
    • Resource crunch: increasing productivity with fewer resources.
    • Institutional knowledge drain: capturing knowledge from retiring workforce.
  • Categories of AI solutions:
    • Insight, diagnostic, predictive, prescriptive use cases.
    • Action track: allowing models to act based on inputs.
  • AI readiness considerations:
    • Intentionality in AI implementation is crucial.
    • Multiple AI technologies exist (not just generative AI).
    • Subject matter expertise is essential.

Approaches to Enterprise AI Solutions

  • Emphasis on user-centered design and agile approaches.
  • Start with pilot implementations for practical insights.
  • Operationalization and scaling solutions based on user needs.
  • Ensure quality content for effective AI solutions.
  • Importance of knowledge models (taxonomies, ontologies) in AI readiness.

Enterprise AI Implementation Plan

  • Assess organizational AI goals to align with business objectives.
  • Evaluate data quality and establish strong governance practices.
  • Identify necessary skill sets and training for AI initiatives.
  • Foster a supportive culture for AI adoption.

KPIs and Success Measures

  • Metrics help define success and align business and technical teams.
  • KPIs provide visibility and quantifiable impact of AI initiatives.
  • Ongoing optimization through defined KPIs ensures continuous improvement.

Future Trends in Enterprise AI

  • Increase in human and AI collaboration is expected.
  • AI-driven KM: enabling personalized experiences and addressing misinformation.
  • Emphasis on ethical and responsible AI to ensure compliance.

Q&A Highlights

  • Definition of KM includes information, content, and knowledge.
  • AI readiness assessments can apply to both large enterprises and smaller units.
  • Concept mapping vs. knowledge mapping discussed.
  • Training relevant even for those without immediate use cases.
  • Interest in developing a KM culture in organizations without a formal structure.

Class Registration

  • Upcoming class dates: November 5th and 6th.
  • Details available and participants encouraged to register.