A.I. and Knowledge Management - Intro to 3-Part Series

Introduction

The relationship between artificial intelligence (AI) and Knowledge Management (KM) is rapidly evolving, heralding the potential for unprecedented organizational transformation. This article kicks off a three-part video series that offers insights into the dynamic interplay between AI and KM.

To appreciate the burgeoning relationship, we must first explore the differences between old and modern Knowledge Management. Traditionally, dating back to approximately 1995, KM was largely document-centric, focusing on organizing structured explicit knowledge. During this time, KM was often managed within IT departments, as it was perceived mainly as a system, rather than an organic practice.

This approach proved to be a significant misstep, one that hampered the effectiveness of KM for nearly a decade. Old KM faced numerous challenges, including issues of outdated content, the "multiple versions of the truth" dilemma, and inefficient search capabilities. Employees reportedly spent up to 33% of their workdays searching for information, with only 10% of the discoverable data being utilized confidently. Notably, even knowledge managers struggled to define the distinctions between data, information, and knowledge.

In contrast, contemporary KM acknowledges these differences clearly. Furthermore, one of the most significant revelations—previously a "dark secret"—is the tacit vs. explicit knowledge dilemma, which recognizes that about 80% of employee knowledge exists in their heads, while only about 20% is codified. This understanding is a pivotal shift from the document-centric views of the past.

Today, modern KM emphasizes a people-centric approach, recognizing the importance of human capital and the value derived from tacit knowledge. Over the course of this series, we will explore three levels where AI will impact KM:

1. Level One: Addressing the challenges of traditional KM.
2. Level Two: Tackling the complexities of converting tacit knowledge into more accessible forms and addressing unstructured explicit knowledge, such as emails and chat communications.
3. Level Three: Enabling transformational change as AI extracts, collates, and analyzes tacit knowledge in near real-time. This process aims to create a connected, continuous learning organization.

This raises an intriguing question: What role will the knowledge manager play in a landscape radically altered by AI? While the arrival of AI will undoubtedly change the responsibilities of knowledge managers, it also opens a wealth of opportunities. As the adage goes: "AI will not take your job, but the person who knows how to leverage AI might."

Join us in the ensuing videos as we delve deeper into this exciting journey.

Keywords

• Artificial Intelligence
• Knowledge Management
• Document-Centric
• People-Centric
• Explicit Knowledge
• Tacit Knowledge
• Transformation
• Continuous Learning
• Knowledge Manager

FAQ

Q1: What is the difference between old and modern Knowledge Management?
A1: Old KM was document-centric and IT-driven, whereas modern KM is people-centric, valuing human capital and tacit knowledge.

Q2: What were some challenges faced by traditional Knowledge Management?
A2: Challenges included outdated content, multiple versions of the truth, inefficient searching, and inadequate use of available information.

Q3: What percentage of knowledge is tacit?
A3: Generally, it's accepted that about 80% of an employee's knowledge is tacit and only around 20% is codified.

Q4: How can AI impact Knowledge Management?
A4: AI can help address traditional KM problems, convert tacit knowledge into explicit forms, and create a continuous learning environment by analyzing knowledge in real-time.

Q5: What will be the role of the knowledge manager in an AI-driven environment?
A5: The role of the knowledge manager will evolve, but there will be numerous opportunities, especially for those who can effectively utilize AI tools.