The Tacit Knowledge Bottleneck
Engine Room Article 7: Expert Knowledge Resists Capture
The Articulation Gap
Ask experts how they make decisions and you’ll typically get an incomplete picture. They’ll describe the factors they consciously consider. What they often can’t articulate: the pattern recognition that happens below conscious awareness.
This is tacit knowledge - knowledge demonstrated in practice but not fully expressible. It’s often the most valuable thing experts have.
Experts know things they can’t fully articulate. That tacit knowledge is often their most valuable contribution - and the hardest to capture.
The AI Training Problem
AI systems learn from data. If knowledge isn’t captured in data, models can’t learn it. Tacit knowledge, by definition, isn’t in the data - it’s in the experts who create and interpret the data.
Working on systems that needed domain expertise surfaced a recurring pattern: the most valuable knowledge was often the hardest to capture. Experts could perform tasks effortlessly but struggled to explain exactly how they did it.
More Promising Approaches
Decision logging: Capture not just outcomes but the specific inputs that led to them.
Structured disagreement: When experts disagree, exploring why often surfaces tacit criteria neither would articulate unprompted.
Schema co-creation: Building data models with experts forces articulation of what entities and relationships matter.
Building data models with experts forces articulation. The modeling process itself surfaces knowledge that wouldn’t emerge through interviews alone.
Tacit knowledge is real and valuable. Capturing it requires methods designed for knowledge that resists articulation.
Related: 07-source—engine-room-series