Unveiling AI in Knowledge Management: Lessons from Great Authors in the Digital Transformation Era

In this sequence of articles, I invite you to embark on a journey through the evolution of knowledge management over the past 30 years, exploring its transformation from traditional concepts to contemporary approaches, now including the growing relevance of Artificial Intelligence (AI).

AI, with its ability to process and analyze vast amounts of data, has become a crucial component in the evolution of knowledge management, offering new possibilities to transform information into knowledge more efficiently and effectively.

I've curated content and, through the perspectives of renowned thinkers I've selected, such as Nonaka and Takeuchi, Drucker, and others, I propose we examine how information metamorphoses into knowledge, establishing a solid conceptual base to delve more deeply into AI application.

Weekly, we will enhance our understanding of AI against the backdrop of knowledge management in light of the great thinkers who established theories and models on this topic at the end of the 20th and beginning of the 21st century, which remain relevant today.

Your contribution is very welcome! As we will see later, collaboration determines the generation of knowledge and shapes our legitimate stance towards AI 😉.

Nostalgia Section - Traditional Knowledge Management

Before we dive into the modern intersections of AI with knowledge management, it's essential to recall past practices. Knowledge management has been a constant concern for organizations throughout history. I remember discussing this topic back in the early 2000s. Before the era of artificial intelligence (AI), knowledge management methods largely depended on us, the employees, and were based on conventional approaches that, despite their good intentions, often faced significant challenges.

In this first part, we'll remember the knowledge management methods of the past, or for those new to knowledge management, we'll learn, highlighting employee dependency and the limitations of conventional approaches.

Employee Dependency: In the past, organizations heavily relied on their employees as the main repositories of critical knowledge. This knowledge often resided in the minds of experts and experienced collaborators, becoming vulnerable to loss when these employees retired, changed jobs, or faced other events that took them away from the organization. Excessive dependence on individual skills made companies susceptible to the so-called "brain drain".

Moreover, the transfer of knowledge between generations of employees was a challenge. Tacit knowledge (we'll discuss this term when we talk about Nonaka & Takeushi), which wasn't documented and was acquired only through practical experience, was especially difficult to share and transmit effectively. Organizations often struggled to capture this knowledge and turn it into tangible, absolute, and valuable assets.

Limitations of Conventional Approaches: Conventional approaches to knowledge management often relied on manual processes of collecting, storing, and disseminating information. Document management systems were used to organize physical documents, while communication between teams often depended on emails, face-to-face meetings, and printed documents.

These approaches had various limitations. The physical location of documents made it difficult to access knowledge quickly and globally. And even when they were stored digitally, finding them through search was a complex and time-consuming process. There was a lack of systems to properly manage the repository of produced documents and classify/label them with keywords. Contents were not accessible, only titles, and in raw format.

Furthermore, information often became outdated quickly, making it difficult to maintain relevant and accurate knowledge. Knowledge sharing largely depended on the employees within the organization, which could be inefficient and limited.

Challenges in Knowledge Retention and Sharing: The struggle of organizations to retain and share knowledge effectively was evident in various areas.

The main difficulties included:

• Critical Knowledge Loss: The retirement of experienced employees or their departure for other opportunities (the "brain drain") often resulted in the loss of valuable knowledge that had not been properly documented or transferred.

• Difficulty in Capturing Tacit Knowledge: Tacit knowledge, often the basis of practical experience, was difficult to document and share effectively.

• Limited Access: The lack of efficient document management systems and information sharing mechanisms made it challenging to access knowledge quickly and broadly within the organization.

• Rapid Obsolescence: Information often became outdated, making outdated knowledge a recurring problem.

This analysis of the limitations of traditional approaches to knowledge management highlights the urgent need for more effective solutions, even today, as there are organizations that still live in this context.

Next week, I'll delve into how Artificial Intelligence (AI) can revolutionize the field of knowledge management. Our exploration will begin with an in-depth look at the groundbreaking work of Nonaka & Takeuchi, whose theories have significantly shaped our understanding of knowledge dynamics.

Thank you for joining me on this insightful journey. Wishing you a wonderful weekend, and I look forward to continuing our exploration together!"