Imagine giving AI access to everything an organization knows.
Every document, presentation, architectural decision, assessment report, project deliverable and incident record.
Would the AI understand the organization?
Not necessarily.
It might retrieve relevant information, summarize documents and identify recurring themes.
But would it understand what the information refers to, how different subjects relate, and which evidence supports which conclusions?
Perhaps making organizational knowledge interrogable requires more than access to information.
Perhaps it also requires context.
A Body of Knowledge Is Not a Model
Organizations accumulate enormous Bodies of Knowledge.
They contain observations, decisions, experiences, explanations and evidence gathered over time.
But much of this information was created for particular purposes, by different people, using different terminology.
A project report describes a problem. An assessment identifies a capability weakness. An incident record describes a failure. An architectural decision explains a technical choice.
These artifacts may concern related aspects of the same organizational reality.
Yet their relationships are rarely explicit.
An AI assistant might retrieve all four documents without necessarily understanding how they relate.
The information exists.
The structure needed to interpret it may not.
Could Reference Models Provide the Context?
A reference model provides a conceptual structure for understanding a particular kind of subject.
A capability reference model describes capabilities and their relationships.
A product reference model describes concepts such as purpose, characteristics, architecture, decisions and evidence.
These models are generic. They do not describe any particular organization or product.
But they can be contextualized.
A generic capability model can be related to an organization’s actual capabilities, responsibilities and operating environment.
A product reference model can be applied to a particular product, connecting its purpose, realization, characteristics and supporting evidence.
This suggests an interesting possibility.
Could contextualized reference models serve as context layers between an organization’s Body of Knowledge and the questions people want to ask?
The Body of Knowledge would provide the information and evidence.
The context layers would help establish what that information concerns and how it relates.
AI could use both to investigate questions that were never anticipated when the information was created.
Asking Better Questions
Consider a consultant asking:
Why are we experiencing recurring defects in Product X?
Without an explicit context model, AI might retrieve incident reports, defect statistics and retrospective findings.
That could already be useful.
But suppose it also had access to contextualized models describing Product X, its expected characteristics, the organizational capabilities involved in developing it and the relationships between them.
The investigation could become more structured.
Which product characteristics are affected?
Which capabilities contribute to producing those characteristics?
What evidence exists about those capabilities?
Have previous interventions attempted to address the problem?
What happened afterward?
The context layer would not provide the answer.
Nor would it establish that a particular capability caused the defects.
But it could help identify relevant relationships, locate evidence and expose gaps in our understanding.
The model would help us determine what to investigate. The evidence would help us determine what we can reasonably conclude.
But Who Says the Model Is Correct?
There is a complication.
A contextualized reference model is itself a representation of organizational reality.
And representations can be incomplete, outdated or wrong.
Perhaps the model assigns responsibility to a team that no longer performs the work.
Perhaps it describes a dependency that has disappeared.
Perhaps it assumes that a capability operates in a particular way, while the available evidence suggests something different.
If AI treats the context layer as unquestionable truth, it may produce convincing answers based on incorrect assumptions.
That would make the organization easier to interrogate without necessarily making its answers more reliable.
So perhaps the relationship must work in both directions.
The model helps interpret evidence.
But evidence must also be capable of challenging the model.
When observations contradict the contextualized understanding, that contradiction should become visible.
And when the evidence warrants it, the model should change.
An Interrogable Organization
This raises another possibility.
Perhaps an interrogable organization should not merely allow people to ask questions about its accumulated knowledge.
It should also allow them to question its understanding of itself.
Why do we believe this capability depends on that one?
What evidence supports this assessment?
When was this relationship last validated?
Which assumptions are no longer supported?
What has changed since this model was established?
AI might even help identify discrepancies between the models and the evidence accumulated through everyday work.
But identifying a discrepancy is not the same as resolving it.
Someone still needs to evaluate the evidence, interpret its significance and decide whether the model should change.
The context layer would therefore need to evolve alongside the organization.
Not simply as documentation maintained periodically, but as a representation that can be challenged and refined through continued observation.
How Much Context Must We Model?
There is still an open question.
How much organizational context needs to be explicitly represented?
Modern AI can already infer relationships from documents and other information.
Perhaps some contextual understanding can be reconstructed when a question is asked.
But relying entirely on inference introduces uncertainty.
Different questions might produce different interpretations of the same organization. Important relationships may be overlooked. Assumptions may remain invisible.
Explicit models could provide greater consistency and make assumptions easier to examine.
Yet maintaining comprehensive models of an organization could become an enormous undertaking.
Perhaps the answer lies somewhere between the two.
Reference models provide relatively stable conceptual structures.
Contextualized models represent selected aspects of organizational reality.
AI helps connect those models to available evidence, infer missing relationships and identify inconsistencies.
Humans evaluate what should be accepted, challenged or changed.
The models need not contain everything.
But they may provide enough structure to make the organization’s knowledge more useful.
From Information to Understanding
Perhaps the real opportunity is not simply to create better search facilities.
It is to connect three things that organizations often manage separately:
Their accumulated knowledge.
Their models of how the organization works.
And the evidence through which those models can be evaluated.
An interrogable Body of Knowledge could make information accessible.
Context layers could make relationships understandable.
And continued interrogation could help expose where the organization’s understanding no longer agrees with reality.
That suggests a potentially different role for reference models.
Not merely as instruments for assessment or standardization, but as part of the infrastructure through which an organization understands itself.
Perhaps an interrogable organization needs more than access to what it knows.
It also needs a way to question what it believes it knows.
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