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.