Introduction

In today's digital age, the intersection of AI and KM is becoming increasingly crucial for organizations seeking to thrive and benefit from their organizational knowledge and data assets. Understanding the fundamentals of AI and its applications in KM, and vice versa is essential for organizations looking to succeed and remain relevant or competitive.

In this class, we will explore the definitions, components, and applications of AI and KM, and how they interact together. As we navigate through this intersection, we will discuss leveraging KM foundations such as content governance, roles, taxonomy, ontology, and knowledge graphs. These, along with AI technologies, can enhance knowledge creation, sharing, and utilization, ultimately fostering innovation, efficiency, and competitive advantage within enterprises.

Who Should Attend?

• Data and Knowledge Managers who want to understand how KM and Enterprise AI work together to help their organizations improve content and data management processes, as well as the search experience.
• Knowledge and content engineers such as Taxonomists and those new to ontology who want to gain knowledge on real-world applications of Enterprise AI, leveraging the power of KM, taxonomies, ontologies, and knowledge graphs.
• People working in the public sector or in highly regulated industries who are interested in understanding the potential of KM and Enterprise AI to analyze large amounts of data, enhance decision-making, and support regulatory compliance.  
• IT Project Managers who want to advance their skills by gaining an understanding of how to plan for Enterprise AI projects.
• Anyone who practices KM in their organization and is interested in learning how targeted Enterprise AI use cases can significantly transform how work is done, providing them with the tools and insights they need to maintain a competitive edge.

Learning Objectives

• Understand the fundamentals of Enterprise AI and its application in KM.
• Educate your organization on the importance of KM foundations in the realm of AI.
• Gain a high-level understanding of the role of knowledge engineering in the design of Enterprise AI solutions.
• Understand the role of AI in KM, common case cases, applications, and their business value.
• Learn how to identify if an organization is ready for the design and implementation of an Enterprise AI solution and what recommended approaches should be taken into consideration.
• From a project management perspective, gain an understanding of the recommended roles, responsibilities, tasks, and measures to successfully plan for and manage an Enterprise AI project.
• Acquire insights into designing, planning, and implementing Enterprise AI solutions based on industry best practices and lessons learned.

Topics Covered

• Fundamental concepts, including Knowledge Management, Content Management, Data Management, Semantic Layer, Knowledge Graph, Enterprise AI, Machine Learning, Natural Language Processing (NLP), and Large Language Models (LLM).
• Top Enterprise AI categories, use cases, and applications
• Common Enterprise AI risks and limitations
• Enterprise AI case studies
• Recommended approaches to Semantic Solutions and KG Solutions
• Knowledge Modeling and Mapping Overview
• Enterprise AI Readiness
• Proven Approaches to KM and AI Implementations
• Preparing your content for AI
• Enterprise AI plan and roadmap (common Enterprise AI project roles, tasks, KPIs, etc.)
• Ethical AI and Regulation