Tacit Knowledge Guide

From Experience to AI-Ready Intelligence: A Guide to Tacit Knowledge Harvesting

Institutionalizing Expert Judgment in the GenAI Era

By David H. Deans

Standard manuals tell people what to do. Experienced practitioners also understand why, when, and under what circumstances something should be done.

That difference is the essence of tacit knowledge.

Tacit knowledge is the accumulated judgment, pattern recognition, contextual awareness, heuristics, exceptions, and decision logic that experienced professionals develop through years of practice. It often exists only in the minds of an organization's most knowledgeable people—and therefore becomes vulnerable to retirement, turnover, organizational change, and the loss of institutional memory.

Tacit Knowledge Harvesting provides a systematic way to make this expertise visible, inspectable, and reusable.

The objective is not simply to document what an expert knows. It is to capture the judgment behind the knowledge so that it can become a durable organizational intelligence asset.

The ExpertHarvest Approach

ExpertHarvest is an AI-assisted workspace for converting expert judgment into reusable intelligence for enterprise AI.

Rather than conducting a conventional expert interview, ExpertHarvest captures a Judgment Episode: a specific situation in which an experienced practitioner had to interpret signals, evaluate alternatives, make a decision, and act under real-world constraints.

The process captures:

Situation → Signals → Evidence → Alternatives → Discriminators → Judgment → Rationale → Action → Outcome → Rule → Exception

This creates a much richer representation of expertise than a conventional procedure, interview transcript, or knowledge-base article.

1. Define the Domain & AI Outcome

Begin by establishing the domain in which expertise is being harvested.

Identify:

• The business or technical domain

• The workflow or decision environment

• The type of expertise being captured

• The intended AI or organizational outcome

For example, the domain might be B2B SaaS product launches.

The objective might be to help an AI system evaluate launch readiness and identify risks that an inexperienced product team could overlook.

This step establishes the context for everything that follows.

2. Capture a Judgment Episode

The central question is:

Tell me about a difficult decision in this domain that a competent newcomer would be likely to get wrong.

This changes the nature of the knowledge-capture exercise.

Instead of asking an expert to describe everything they know, ExpertHarvest focuses attention on a real situation where experience mattered.

The expert describes:

• What was happening

• What was at stake

• What constraints existed

• What signals or observations were noticed

• What the expert actually did

• Why the decision was difficult

The result is a concrete Expert Judgment Episode rather than a generic interview.

3. Map the Decision Logic

Experienced professionals rarely make important decisions from a single piece of information.

They integrate multiple signals, consider alternatives, recognize patterns, and identify the evidence that matters most.

ExpertHarvest therefore makes the decision structure explicit:

Situation & Context → Signals & Evidence → Alternatives & Hypotheses → Discriminating Evidence → Judgment

The expert identifies:

Evidence Considered

What information, observations, experience, or data informed the decision?

Alternatives & Hypotheses

What other explanations or courses of action were plausible?

Discriminating Evidence

What ultimately separated the attractive alternatives from the judgment that was selected?

4. Surface Expert Reasoning

The goal is not to capture an expert's private internal monologue.

Instead, ExpertHarvest captures communicable decision rationale—the reasoning that another practitioner can inspect, challenge, learn from, and potentially encode into an AI system.

The expert explains:

• Why the judgment made sense

• What assumptions influenced it

• What trade-offs were considered

• What contextual factors mattered

• What experience-based patterns were recognized

• What evidence would have changed the decision

This distinction is important.

The objective is not to reproduce unrestricted chain-of-thought. It is to capture useful, inspectable reasoning and decision rationale.

5. Capture Rules, Heuristics & Exceptions

Years of experience often produce practical rules of thumb.

These may take forms such as:

When X occurs in Y circumstances, consider Z because A.

ExpertHarvest captures these heuristics while also asking an equally important question:

When does the rule fail?

Experienced judgment is rarely universal.

A useful knowledge object therefore includes:

• The rule or heuristic

• The conditions under which it applies

• Exceptions

• Boundary conditions

• Thresholds

• Dependencies

• The expert's confidence in its broader applicability

This prevents an organization from converting useful expertise into simplistic rules that fail outside their original context.

6. Validate & Harvest

The final step converts the captured episode into a structured AI-ready intelligence object.

Before harvesting, the expert validates that the codified representation accurately reflects:

• The situation

• The decision logic

• The rationale

• The assumptions

• The exceptions

• The intended use

The resulting knowledge object can potentially support multiple downstream applications.

Potential AI Uses

Training / Fine-Tuning Examples

Provide high-quality examples of domain-specific judgment.

Evaluation & Test Scenarios

Test whether an AI system recognizes situations that experienced practitioners handle correctly.

RAG / Knowledge Objects

Provide contextual expert knowledge to retrieval-augmented AI systems.

Agent Guardrails & Decision Rules

Encode important constraints, heuristics, and boundary conditions into AI-enabled workflows.

Human Knowledge Transfer

Use the same captured intelligence to accelerate learning and transfer expertise to less-experienced practitioners.

From Tacit Knowledge to Organizational Intelligence

The objective of Tacit Knowledge Harvesting is therefore not simply to create documentation.

It is to transform:

Individual Experience > Expert Judgment Episodes > Structured Decision Logic > Reusable Expert Intelligence > AI-Ready Knowledge.

This creates a potential bridge between human expertise and enterprise AI.

The most valuable organizational knowledge may not exist in a database, a manual, or a training document.

It may exist in the judgment of the people who have spent years learning what the data does not tell you.

ExpertHarvest provides a structured way to begin capturing that knowledge before it disappears.

The ExpertHarvest Knowledge Object

Each completed harvesting session produces a structured representation of an expert judgment episode containing:

• Domain & AI outcome

• Situation and context

• Signals and observations

• Expert account

• Evidence considered

• Alternatives and hypotheses

• Discriminating evidence

• Decision rationale

• What would change the judgment

• Outcome and learning

• Rules of thumb

• Exceptions and boundary conditions

• Confidence

• Potential AI applications

• Human validation

This structure is deliberately designed to remain useful beyond the initial interview or capture session.

It can become a reusable organizational knowledge asset.

Put Your Tacit Knowledge to Work

Tacit Knowledge Harvesting is a methodology for turning experience into something an organization can inspect, preserve, reuse, evaluate, and potentially operationalize through AI.

ExpertHarvest is the practical instrument for applying that methodology.

Explore ExpertHarvest

Use the ExpertHarvest Hub to capture an expert judgment episode and experience the methodology firsthand.

Need a broader strategy?

GeoActive Group provides advisory consulting focused on the intersection of enterprise technology, Applied-AI strategy, value realization, and organizational knowledge.

The goal is not to replace expert judgment with AI.

It is to make more of that judgment available to the organization—and ultimately to the AI systems the organization builds.

Call to Action: Let's put this process into practice.

Launch the ExpertHarvest application now