On February 15, 2024, WIRED published an article sponsored by ThoughtWorks, entitled “Three tech micro-trends businesses need to know“. It discusses upcoming trends in 2024 that companies should have on their radars.

The article specifically mentions three main trends — Cryptography, Chatbots for internal know-how, and Instant account-to-account (A2A) payments. Although these trends are of great interest, I will concentrate here only on chatbots for internal know-how.

My interest in reading this article and concentrating on chatbots came from Tom Davenport’s statement that “chatbots for internal know-how are also known as generative AI for knowledge management”! This, in some way, avoidance of using the classic term — knowledge management (KM) — immediately raised a red flag for me. For many of us with memories going back just about 40 or so years, this avoidance of using KM and coming up with a new replacement term undoubtedly resembles a move from “records management” to “document management” or “information resource management”, and even somewhat later, the move of replacing library and documentation centers with “knowledge centers”. So, for a moment, it seemed to me that all knowledge managers will be soon called “internal chatbot managers”!

However, Tom Davenport, a well-known and widely respected Distinguished Professor of IT and Management at Babson College in Massachusetts, and a research fellow at the MIT Center for Digital Business, quoted several times in the article, made some valid points.

He developed his main line of thought by emphasizing three different development phases.

First phase: KM Enthusiasm — During this phase, a company’s intellectual property was valuable, but it was dispersed throughout several files and systems. It has long been understood how important it is to compile all of that knowledge in a way that both preserves and facilitates searching. Employing “knowledge managers” to perform this task manually was a popular trend in the 1990s and early 2000s, but it required a lot of effort. “So the knowledge management movement largely died,” concluded Davenport.

Second phase: AI Enthusiasm — This is the phase where Large Language Models (LLMs) took most of the spotlight. They have been trained on the colossal information corpus of the internet, but it was realized that they can also be trained on corporate documentation. This enabled the model to respond to natural language questions about anything organization-specific, from financial strategy to project management. Some larger companies provided their staff with localized chatbots, therefore, internalizing the company’s collective thinking.

Third phase: New KM — Referring to the previous phase, Davenport noted that even with chatbots “Unfortunately, it has not gotten any easier to curate the knowledge in the first place, and you have to be quite careful with what you put in if you want the answers to be relevant and have fewer errors.” The current phase forces companies to go back to having “good old knowledge managers” who will need a different set of capabilities and who understand all the new technologies that didn’t exist previously.

Conclusion There are some very pertinent and important messages that the article sends to all of us dealing with KM today. KM is still relevant, and it might be even more relevant than previously. However, KM needs to undergo some major transformation from its old concepts, procedures, and activities to the new ones. It needs to adapt to new economic realities, align itself with the company’s business goals, and fully start using and benefiting from new AI and other IT tools available. To achieve all this and meet modern challenges, knowledge managers need to work on constantly improving their knowledge and skills. For all knowledge managers, continuous learning, self-improvement, and development are the only ways forward.

Dr. Dobrica Savić

DOI: http://dx.doi.org/10.13140/RG.2.2.26203.75047

*The image was created using Microsoft Copilot.