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Capture Knowledge

Preserving institutional expertise to enable a whole new league of AI-driven use cases.

In every enterprise, the most valuable asset is often the most fragile: Institutional Knowledge. This knowledge exists in two forms: Explicit Knowledge (scattered across PDFs, diagrams, and emails) and Tacit Knowledge (the specialized "how-how," rules of thumb, and method choices residing in the heads of your Subject Matter Experts (SMEs)).

The Use Case Tree Method uses the Enterprise Knowledge Graph (EKG) as the definitive technology stack to capture, formalize, and activate this knowledge before it "walks out the door."

The Institutional Knowledge Problem

Traditionally, when an SME leaves or retires, their expertise leaves with them. Documentation is often a "write-once, read-never" graveyard of information that AI cannot reliably use without context.

This creates a bottleneck:

  • Productivity Drain: SMEs spend 30-50% of their time answering the same questions or hunting for data.
  • AI Hallucinations: Generic AI models lack the "ground truth" of your specific business logic, leading to unreliable outputs.
  • Lost Rationale: We know what was decided in the past, but we've lost the why—the expert logic that drove those decisions.

How EKG Captures Expertise

An EKG doesn't just store data; it captures meaning and relationships. By using the Use Case Tree, organizations can systematically extract knowledge from SMEs and turn it into executable models.

  1. Structured SME Logic: SMEs define the Concepts, Outcomes, and Workflows that drive the business. This transforms "head-knowledge" into machine-readable Ontologies.
  2. Contextual Mapping: Every data point is linked to the business capability it supports. You aren't just capturing "what" the data is, but the constraints and dependencies that define its use.
  3. Durable Intellectual Property: The knowledge becomes part of the enterprise's software fabric—reusable, versioned, and protected from employee turnover.

AI Acceleration: The Ground Truth Foundation

The rise of Large Language Models (LLMs) has changed the game. AI is no longer just a tool; it is a productivity accelerator. However, AI is only as good as the knowledge it is grounded in.

EKG is the "Fact-Checking Guardrail" for AI.

  • GraphRAG (Retrieval-Augmented Generation): Instead of letting an AI guess, you feed it structured facts from your EKG. This ensures the AI's output is accurate, traceable, and compliant with enterprise standards.
  • Neuro-symbolic Harmony: We combine the probabilistic power of GenAI (extracting knowledge from millions of documents) with the symbolic precision of EKGs (the validated rules provided by your SMEs).
  • Accelerating SMEs: Instead of doing the "grunt work" of data retrieval, your experts use AI to turn interviews and past debriefs into structured knowledge linked to evidence, which is then instantly validated against the EKG.

!!! success "The Result: A New League of Use Cases" By capturing knowledge in an EKG, you move from "Data Management" to "Intelligence Management." You enable strategic use cases—like Complex Construction Quotations—that were previously impossible due to the sheer volume of expert knowledge required.

Why it Matters Now

As AI begins to take over routine cognitive tasks, the primary role of the human expert shifts from doing the task to defining the logic that drives the AI.

Capturing knowledge in an EKG ensures that your organization owns the "brain" of its AI systems. It isn't just about efficiency; it's about Agility—the ability to reuse expert knowledge across the entire organization to build new capabilities at the speed of thought.

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Author: Jacobus Geluk