When discussing AI systems, many people tend to focus on topics such as:
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Model accuracy
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Data volume
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Inference speed
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Number of parameters
However, in real-world operational systems, a slightly different problem exists.
That problem is the question:
“Why was this decision made?”
And this is not a problem unique to AI systems.
In fact, this issue has existed for many years in many business support systems as well.
What Is Truly Lost Is Not the Final Result
In many manufacturing companies, the following forms of management are already highly developed:
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Drawing management
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Document management
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Revision history tracking
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Approval workflows
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Quality traceability
In other words,
the management of final deliverables
is already very strong.
However, in practice, situations like the following often occur:
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No one knows why a particular proposal was adopted
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No one remembers what the alternative options were
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The concerns that were raised are not recorded
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It cannot be explained why it was judged acceptable at the time
In other words,
what has been lost is the decision-making process itself.
The Value of Design Lies in the Process, Not the Result
The value of design does not lie in the final drawing.
The real value lies in understanding:
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What options were considered
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What ideas were rejected
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Where risks were perceived
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At what point the decision process stopped
In other words, the key structure is the decision path:
Option → Concern → Decision
This decision path is an extremely valuable asset for an organization.
However, in most systems today, this information remains as tacit knowledge, scattered across places such as:
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Meeting discussions
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Emails
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Slack conversations
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Personal notes
As a result, it is never stored in a structured form.
The Original Purpose of the Decision Trace Model
The Decision Trace Model is not a technology designed only for AI systems.
Originally, it was designed as a framework to structure human decision-making processes.
For example, the basic structure of a Decision Trace Model in manufacturing is very simple.
1. Option (Candidate Design Choices)
Example:
Option A: SUS304
Option B: SUS316
2. Concern
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Corrosion risk
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Cost increase
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Procurement lead time
3. Risk Assessment
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Low / Medium / High
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Quantitative score
4. Rejected Reason
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Cost constraints
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Delivery schedule constraints
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Standardization priority
5. Stop Condition
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Test results not yet obtained
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Safety standards not verified
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Insufficient confidence level
By using this structure, it becomes possible to preserve the decision path itself.
How This Differs from RAG or Document Search
Recently, many companies have introduced technologies such as:
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Document search
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Knowledge bases
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Retrieval-Augmented Generation (RAG)
However, these systems essentially function as mechanisms to read documents.
In other words, they operate under a structure where:
Documents are retrieved, and an LLM summarizes them.
But in such systems, the following elements do not remain as structured information:
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Rejected reasons
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Concerns
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Decision stop conditions
Instead, the LLM simply interprets the information on the spot each time.
The Decision Trace Model is fundamentally different.
The unit of storage is not the document, but the decision itself.
How to Apply the Decision Trace Model to Business Support Systems
In practice, implementation proceeds in several stages.
Phase 1
Define a common Decision Trace schema.
Phase 2
Decompose existing data.
Existing materials such as:
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Drawings
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Meeting minutes
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Test results
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Standards documents
are decomposed into structured units.
Phase 3
AI-assisted schema integration.
Using technologies such as:
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Graph Neural Networks (GNN)
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Large Language Models (LLM)
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Machine learning
the system can perform tasks such as:
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Extracting candidate concerns
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Suggesting possible rejection reasons
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Searching for similar past cases
Phase 4
Cross-case analysis.
Once Decision Traces are accumulated, it becomes possible to search across them to identify patterns such as:
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Cases rejected for the same reasons
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Repeated concern patterns
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Risks that had previously been flagged
What Changes When This System Is Introduced?
When this framework is implemented, several major changes occur.
1. Stronger Prevention of Recurrence
When accidents occur, traditional explanations often end with:
“At the time, there appeared to be no problem.”
However, if Decision Trace records exist, it becomes possible to explain:
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Why the design was adopted
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Which concerns were ignored or accepted
2. Reuse of Expert Decision Paths
The real value of experienced engineers lies in the fact that they eliminate dangerous paths before they become problems.
However, these “eliminated paths” usually disappear.
Decision Trace allows organizations to capture and reuse these paths as knowledge assets.
3. Integration with AI
Only at this stage does AI truly become powerful.
Once Decision Traces exist, AI can support human decisions by:
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Searching for similar past decisions
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Identifying risk patterns
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Generating potential concerns
In other words,
AI becomes not a system that makes decisions, but a system that supports decision-making.
Decision Trace Model and AI Systems
When applied to AI systems, the Decision Trace Model forms the following structure:
Event
↓
Signal (AI prediction)
↓
Decision (Human or AI)
↓
Boundary (Stop condition)
↓
Ledger (Decision history)
The key point here is that:
Both AI decisions and human decisions are recorded using the same trace structure.
In other words,
the Decision Trace Model is not an AI-specific technology.
It is the foundation of a decision-making system where humans and AI coexist.
Conclusion
For many years, companies have successfully turned the following into assets:
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Drawings
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Documents
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Code
However,
decisions themselves — especially the reasoning process leading to them — have never been systematically assetized.
The Decision Trace Model provides a framework to:
structure decisions and turn them into organizational assets.
The value of design does not lie in the result.
It lies in the process.
The Decision Trace Model is a system designed to preserve that process.
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