The Philosophical Roots of InContextable
How a lifetime of watching information misbehave revealed the real reason work keeps breaking.
People sometimes ask where InContextable came from. They assume it started with a management framework, or a book, or a single aha moment.
But the truth is simpler:
It came from noticing, again and again, that our digital systems treat information as if it were data…while humans treat information as meaning.
And those two things do not behave the same way.
This is the real philosophical root of InContextable:
we keep trying to fix organizational problems by fixing IT systems — even though IT systems were never built to hold the kind of information people actually use.
Everything below are the thinkers, experiences, and moments that made that clear.
⭐ 1. My Dad: The First Clue
Long before systems thinking or epistemology entered my life, my dad told me something startlingly profound:
“The job of management is to decide what’s true in their organization.”
Not eternal truth.
Not mathematical truth.
Just shared truth — the kind people can act on.
Years later, I would understand why that sentence matters:
Because digital systems don’t decide what’s true. They only decide what’s stored. Meaning — the thing humans act on — gets lost.
⭐ 2. Thomas Sowell: Context Creates Interpretation
Sowell’s A Conflict of Visions taught me that people act from different underlying visions — different assumptions, different constraints, different contextual beliefs.
Two people can look at the same information and see entirely different realities.
IT systems assume information behaves the same in every head.
Humans don’t.
This was the earliest crack in the classical information model.
⭐ 3. Womack, Goldratt, Reinertsen: Systems Don’t Break Randomly — They Break at Points of Missing Meaning
Lean, the Theory of Constraints, and flow-based product development all reveal the same truth:
Work slows where interpretations diverge
Delays form where intent is unclear
Bottlenecks are often informational before they are operational
Processes drift because meaning drifts
These thinkers helped me see that the real constraint in most organizations isn’t workflow.
It’s interpretation.
And our systems have no place to store interpretation.
⭐ 4. Clay Christensen: We Keep Solving the Wrong Problem
Christensen taught me that organizations often succeed at executing the wrong answer because they are asking the wrong question.
The wrong question comes from:
the wrong frame
the wrong assumptions
the wrong interpretation of customer context
This pointed to a deeper truth:
If the system holds the wrong context, all its answers will be elegantly wrong.
Digital systems memorialize the data of the past
but almost never the meaning behind it.
⭐ 5. IT Work: Where the Mismatch Becomes Painfully Obvious
Years inside IT and MSP environments made something impossible to ignore:
Human information does not behave like database information.
Human meaning is:
tacit
contextual
narrative
fluid
ambiguous in important places
dependent on who is listening
But digital systems want:
structured fields
explicit definitions
stable categories
atomic units
a single “source of truth”
This creates the insanity loop:
“Work is unclear → buy a tool → fit work into the tool → clarity gets worse → buy a new tool.”
We keep treating incompleteness as a software probleminstead of an epistemological problem.
That’s the real break.
⭐ 6. Taleb: Beware Simple Narratives About Complex Reality
Taleb’s Fooled by Randomness sharpened my skepticism.
He made it clear that humans misinterpret not because they’re irrational,
but because reality is bigger than the stories we try to compress it into.
Digital systems compress aggressively.
Meaning leaks out.
After Taleb, I understood why dashboards feel so confident and so often wrong:
They’re compressing a world that doesn’t want to be flattened.
⭐ 7. Karl Popper & David Deutsch: Explanations Drive Action
Popper taught me that knowledge is adaptive explanation, not data storage.
Deutsch taught me that:
Good explanations enable progress.
Bad explanations trap us.
Digital tools store data superbly, but explanations — the thing work actually runs on — get lost.
This revealed the epistemic backbone of InContextable:
If an organization loses the explanation behind a decision, it loses the decision.
⭐ 8. Dan Davies: People Do What Their Environment’s Narrative Makes Possible
Davies’ writing showed me that human behavior follows the story the system makes available.
Tools create narratives too — often accidental ones.
A CRM creates a narrative about customers
A ticketing system creates a narrative about problems
A forecasting tool creates a narrative about risk
When those narratives don’t match reality, organizations behave strangely. This is where context fractures into a thousand shards.
⭐ 9. McChrystal: Shared Context Is Faster Than Hierarchy
When hierarchy failed him, General McChrystal realized something critical:
Teams only move fast when they share context — not just information.
His special operations task force became a real-world proof of the central idea:
Information ≠ understanding
Data ≠ clarity
Reporting ≠ reality
Tools ≠ alignment
Only shared context allows distributed action.
This validated the entire thesis of InContextable.
⭐ 10. Bringing It Together: Why Information Systems Keep Failing Us
Across all these thinkers, a single insight emerges:
Our digital tools are built on the wrong model of information.
They assume information is:
stable
explicit
categorizable
easily transmitted
context-free
interpreted the same by everyone
But human information — the kind work depends on — is:
contextual
tacit
situational
narrative
ambiguous
relational
continuously interpreted
This mismatch explains:
rework
misalignment
the drift of asynchronous work
meeting overload
tool fatigue
over-documentation
the collapse of hierarchy
the rise of “shadow workflows”
staff burnout
decisions without rationale
clarity that evaporates in days
The problem isn’t modern work. It’s that our tools are built on assumptions that don’t match how information actually works.
That’s the real philosophical root.
⭐ Why InContextable Exists
InContextable is not about improving tools. It’s about improving the part of work that tools cannot hold:
meaning
narrative
intent
rationale
interpretation
shared truth
The tools and tips I publish are small but powerful interventions that restore context where systems strip it away.
They are ways of adding back the interpretive richness that digital systems flatten.
Because once context is restored:
work speeds up
ambiguity drops
decisions endure
meetings shrink
people stop guessing
misunderstandings evaporate
the organization starts to feel coherent again
Information systems can’t fix this gap. Only people can — with better habits of interpretation.
That is the philosophy behind InContextable.
Excellent synthesis. The Sowell ref about conflicting visions is perfect here becuase it explains why the same data gets different interpertations depending on underlying mental models. What you're describing is basically the epistemological gap between storage and understanding. Digital systems optimize for retreival when organizations actually need shared sense-making, and those are fundamnetally different operations.