Have you ever thought about the difference between tacit and explicit knowledge? Is it important to your work?
Tacit knowledge refers to the kind of knowledge that is deeply embedded in personal experience, skills, insights, and intuition—knowledge that is difficult to express, formalize, or communicate to others through words or written documents. It is “know-how” rather than “know-what,” often gained through practice, socialization, or hands-on involvement rather than formal instruction.
Key Characteristics of Tacit Knowledge:
- Personal and Context-Specific: Often unique to an individual’s background, culture, or experience.
- Difficult to Articulate: Hard to write down or transfer because it is not codified or easily verbalized.
- Learned by Doing: Transmitted through shared activities, mentorship, imitation, or apprenticeship.
- Embedded in Action: Includes things like problem-solving skills, creative abilities, social intelligence, intuition, and judgment.
“Tacit knowledge is personal, context-specific, and therefore hard to formalize and communicate. It is rooted in action, procedures, routines, commitment, ideals, values, and emotions” (Nonaka, 1994, p. 16)
Examples:
- A master craftsperson’s skill in working with wood.
- A nurse’s intuition about a patient’s health from subtle signs.
- An experienced teacher’s ability to manage a classroom fluidly.
- How to ride a bicycle or play a musical instrument.
Is it even possible to quantify tacit knowledge? If so, how?
Tacit knowledge conversion represents one of the most critical challenges in modern knowledge management systems. When organizations fail to capture the deeply embedded expertise, intuitions, and skills of their employees, they create knowledge silos that limit innovation and decision-making effectiveness.
The tacit knowledge conversion process transforms this implicit understanding into explicit, shareable formats that can be systematically stored, retrieved, and reused across the organization. While traditional approaches face significant barriers—including the subconscious nature of tacit knowledge and employee resistance to sharing—AI and NLP technologies offer breakthrough solutions for automating and enhancing this conversion process.
“Converting tacit knowledge into explicit forms enhances organizational effectiveness by making this knowledge accessible and reusable” (Zaoui Seghroucheni et al., 2025, p. 1).
Think of tacit knowledge like a master chef’s cooking secrets that exist only in their head – they know exactly how much salt to add by feel, but can’t easily teach it to others. Converting this to explicit knowledge is like writing down those secrets in a detailed recipe book. When organizations do this with their employees’ expertise that is implicitly stored in their data (in truth data is typically made from recorded communication, which is made from knowledge and experience, which are both types of information, and from this information discrete data are identified.)
What is Natural Language Processing and How Does it Help in the Conversion of Tacit Knowledge?
Natural language processing operates at the intersection of three critical disciplines: artificial intelligence provides the computational power, computer science contributes the algorithmic frameworks, and linguistics supplies the theoretical understanding of language structure and meaning.
Modern NLP systems must handle the inherent challenges of human language, including inconsistent representations and context-dependent meanings. Effective tacit knowledge conversion requires a sophisticated pipeline that progresses through distinct stages:
- Preprocessing cleans and standardizes textual data
- Text analysis extracts valuable information through techniques like named entity recognition and sentiment analysis
- Knowledge extraction derives actionable insights
- Formalization techniques transform these insights into structured, accessible formats.
The entire process incorporates validation loops and centralized storage to ensure accuracy and accessibility.
“NLP draws heavily from both fields: computer science provides the algorithms, data structures, and machine learning techniques necessary for processing and analyzing large volumes of language data, while linguistics offers the theories and frameworks needed to understand the structure, meaning, and context of language” (Zaoui Seghroucheni et al., 2025, p. 2).
Natural Language Processing (NLP) is like teaching computers to understand human language. It’s a team effort: computer science provides the “brain power” (the technical tools and methods to crunch through massive amounts of text), while linguistics provides the “language knowledge” (understanding how grammar, meaning, and context work). It’s like having both a powerful calculator and a grammar expert working together to understand what people are really saying.
“Through methods like sentiment analysis, topic modeling, and named entity recognition, the system transforms implicit knowledge into explicit, structured formats suitable for reuse in knowledge decision-making, and for fostering knowledge sharing across teams” (Zaoui Seghroucheni et al., 2025, p. 3).
Natural Language Processing uses several “detective tools” to find hidden knowledge in text. Sentiment analysis figures out if someone is happy or frustrated (like reading between the lines of an email). Topic modeling finds what the main subjects are (like automatically sorting emails into folders). Named entity recognition spots important names, dates, and places (like highlighting key information with a marker). Together, these tools take messy, unorganized information and turn it into meaningful knowledge by grouping it topically, categorically, etc.
What Algorithm Does this?
“With the wide variety of NLP algorithms available, selecting the most suitable one for a specific task can be challenging. To address this, we will conduct a comparative analysis of several NLP algorithms based on their advantages and limitations” (Zaoui Seghroucheni et al., 2025, p. 5).
Algorithm selection for tacit knowledge conversion demands careful consideration of trade-offs between accuracy, speed, and resource requirements across multiple processing tasks.
- Text cleaning algorithms range from fast regex patterns for simple tasks to computationally expensive spelling correction for accuracy-critical applications.
- Tokenization approaches must balance processing speed with handling complexity—whitespace tokenization offers efficiency but struggles with punctuation, while subword tokenization provides multilingual flexibility at higher computational cost.
- Normalization techniques face similar trade-offs: lowercasing provides consistency but may lose important case-sensitive information
- Stemming offers speed but can produce meaningless roots
- While lemmatization ensures linguistic accuracy but demands significant computational resources.
- Deep learning models consistently deliver superior accuracy across tasks from named entity recognition to sentiment analysis, but require substantial computational investment.
SBERT emerges as particularly powerful for semantic analysis, utilizing Siamese twin networks and contrastive learning to generate high-quality sentence embeddings that capture semantic meaning essential for tacit knowledge conversion.
There are many different tools for processing language, like having a toolbox full of hammers, screwdrivers, and wrenches. Each tool is good for certain jobs but not others. The challenge is knowing which tool to use for which job – you wouldn’t use a hammer to tighten a screw!
“Unlike traditional models, SBERT utilizes Siamese twin networks and a contrastive learning approach, which optimizes the distance between embeddings of similar examples while maximizing it for dissimilar ones” (Zaoui Seghroucheni et al., 2025, p. 11).
SBERT is like a very smart filing system. Traditional systems might file documents randomly, but SBERT uses “twin networks” (imagine two identical librarians working together) to understand meaning. It learns by comparing things: it puts similar documents closer together on the shelf and pushes different documents farther apart. It’s like organizing your music library so that all rock songs are grouped together, all classical music is together, but rock and classical are kept separate – making it much easier to find what you’re looking for.
Comparative Analysis of NLP Techniques
“The comparative analysis of various NLP techniques, including text mining, information extraction, sentiment analysis, and recommendation systems, has highlighted their effectiveness in extracting knowledge from semi-structured and document-oriented sources” (Zaoui Seghroucheni et al., 2025, p. 14).
After testing many different “knowledge-finding” techniques (like text mining, which digs through documents for useful information, and sentiment analysis, which reads emotions in text), the researchers found that these tools are really good at finding valuable knowledge hidden in company documents, reports, and partially organized files. It’s like having a team of expert treasure hunters who are particularly skilled at finding gold in messy attics and old filing cabinets – they know exactly where to look and what tools to use.
Does your organization need help in applying this type of analysis to its reservoirs of structured and unstructured information?
Are there costly losses of tacit knowledge every time a key member of a team moves on?
Reach out to me if you want to convert this information into knowledge for your organization. We will find the best way to steward the knowledge of your organization.
This article was made with the help of the SBERT website, essays, and AI resources referenced below:
Claude Sonnet 4. (2025, June 27). Analysis and explanation of “Using AI and NLP for Tacit Knowledge Conversion in Knowledge Management Systems: A Comparative Analysis.” Anthropic. https://claude.ai
OpenAI. (2025). ChatGPT (June 2025 version) [Large language model]. https://chat.openai.com/
Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT‑Networks (arXiv:1908.10084) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1908.10084
Zaoui Seghroucheni, O., Lazaar, M., & Al Achhab, M. (2025). Using AI and NLP for tacit knowledge conversion in knowledge management systems: A comparative analysis. Technologies, 13(2), 87. https://doi.org/10.3390/technologies13020087