We tend to preserve information because we believe someone might need it later.
We write documentation, record decisions, create knowledge repositories and organize information so that it can be retrieved.
But there’s a problem.
We don’t know what questions people will ask in the future.
And perhaps that should change how we think about preserving knowledge.
A Question Nobody Could Have Anticipated
Imagine AI had access to my complete photographic library.
In 2040, I might ask:
“Compare this photograph from 2009 with this one from 2030. How did my composition change?”
Nobody could have prepared that answer in 2009.
The question didn’t exist yet. Neither did half the evidence required to answer it.
But because the photographs were preserved, the question can now be investigated.
The photographs weren’t created to answer that particular question.
They became useful because someone could relate them to evidence that emerged much later.
Perhaps organizational knowledge works the same way.
Preserving Answers or Preserving Evidence?
Organizations accumulate enormous amounts of information.
Documents, presentations, requirements, architectural decisions, source code, test results, incident reports and project deliverables.
Much of this information is organized around the questions people were trying to answer when it was created.
What should we build? How should we implement it? Did it work? What went wrong?
But future questions may be entirely different.
A consultant might want to compare approaches used across several projects.
An architect might want to understand why a particular technology was selected.
A quality manager might want to investigate whether recurring defects originate in weaknesses in organizational capabilities.
Nobody necessarily anticipated these questions when the original information was created.
And no single document may contain the answer.
Understanding emerges from relating evidence that was created at different times, for different purposes.
Making Knowledge Interrogable
Recently, someone in a team chat asked whether colleagues had material that might help prepare a customer workshop.
People responded with suggestions, questions and references to previous work.
The knowledge existed.
But the consultant still had to locate the material, understand its context and combine it into something useful.
Then it struck me.
The question was already a prompt.
What if the consultant could ask the organization directly?
“What relevant experience do we have?”
“What approaches have we used?”
“What problems did we encounter?”
“Why is this project relevant?”
“Show me the evidence.”
AI could help make an organization’s accumulated knowledge interrogable.
Not merely retrieving documents, but relating information across sources and helping construct answers to questions nobody anticipated when those sources were created.
Of course, those answers must remain traceable to their sources.
AI can help construct an interpretation. It should not become the authority for whether that interpretation is correct.
Interrogating the Product
Consider a consultant maintaining a software product in 2030.
The original architect has retired.
The consultant wants to know:
“Why was this function built? Why Java? What tests cover it? What incidents has it caused? How is it actually used?”
The answers may be scattered across requirements, architectural decisions, source history, tests, incident records and operational data.
Perhaps nobody ever documented the complete explanation.
But the evidence may still exist.
And if it has been preserved, AI could help reconstruct the understanding.
This suggests that preserving organizational knowledge is not simply about documenting what we currently know.
It is also about preserving the possibility of understanding things we haven’t thought to ask about yet.
Tomorrow’s Questions Don’t Exist Yet
We cannot document every answer that future people might need.
We don’t know their questions, their circumstances or what additional evidence will become available.
But we can preserve the sources from which future understanding might emerge.
That doesn’t mean keeping everything indefinitely. Information still needs context, provenance, appropriate retention policies and protection against becoming misleading or inaccessible.
It means recognizing that the future value of information cannot always be determined by its immediate purpose.
Perhaps AI changes the economics of organizational memory.
Instead of requiring people to anticipate every future information need, we can increasingly preserve evidence and make it possible to interrogate that evidence when new questions arise.
The Body of Knowledge supplies the material.
AI helps relate it.
Humans ask, challenge and judge.
Because tomorrow’s questions don’t exist yet.
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