Tacit vs Explicit Knowledge

The Distinction

Explicit Knowledge: Codified, articulated, transmissible. Documents, databases, formulas, procedures. Can be stored, searched, and processed computationally.

Tacit Knowledge: Embodied, intuitive, experiential. Skills, intuitions, judgment. Transmitted through practice, demonstration, apprenticeship.

The Iceberg Metaphor

Explicit knowledge is the visible tip; tacit knowledge is the vast mass below the surface. Organizations often overestimate what they’ve captured explicitly and underestimate their dependence on tacit expertise.

Conversion Dynamics (SECI)

Nonaka and Takeuchi’s SECI model describes how organizations create value by converting between these forms:

• Socialization: Tacit → Tacit (apprenticeship, observation)
• Externalization: Tacit → Explicit (documentation, modeling)
• Combination: Explicit → Explicit (synthesis, systematization)
• Internalization: Explicit → Tacit (learning by doing)

AI Implications

LLMs work with explicit knowledge. They can process, combine, and generate text that was externalized by humans. But they cannot:

• Access tacit knowledge directly
• Develop tacit knowledge through practice
• Know when their explicit patterns fail to capture tacit expertise

This boundary defines where AI augments versus where it cannot substitute for human expertise.

Related: 06-atom—tacit-knowledge, 00-source—nonaka-1995-knowledge, 06-molecule—seci-framework, 07-organism—why-ai-cant-create-knowledge