The Hidden Cognitive Complexity Tax
Why “Everything Works — Mostly” Is Wearing Organizations Down
Here’s the most accurate description of life inside most organizations:
Everything works.
Mostly.
And that’s the problem.
Not outages.
Not broken systems.
Just systems that work well enough to keep going — while quietly exhausting the people who rely on them.
This isn’t a software failure
Software today is powerful, flexible, and highly reliable.
It lets organizations:
Configure instead of clarify
Add options instead of revisit intent
Encode decisions instead of re-deciding them
Optimize execution without preserving understanding
Technically, this is progress.
Cognitively, it’s a tax.
“Mostly” is where the cost hides
When systems fail clearly, organizations fix them.
When systems fail occasionally, people adapt.
“Mostly working” systems don’t get repaired — they get worked around.
That’s how you end up with:
Slack messages explaining what a field really means
Meetings to interpret dashboards everyone already saw
Exceptions layered on top of exceptions
Tribal knowledge acting as infrastructure
Humans quietly becoming the integration layer
Nothing breaks badly enough to stop.
Everything breaks slowly enough to tolerate.
Each new feature is a multi-defect opportunity
The comforting assumption is this:
Add a feature → add a little complexity
That’s not how human systems behave.
Every new feature, option, rule, or configuration creates multiple opportunities for failure:
Misinterpretation
Partial adoption
Conflicting assumptions
Forgotten intent
Edge cases no one owns
The feature usually works perfectly.
The shared meaning around it does not.
So complexity doesn’t add.
It multiplies.
Efficiency thinking is the accelerant
Most organizations are still governed by industrial-era efficiency thinking:
Optimize throughput
Standardize decisions
Automate variance
Measure success through utilization and adoption
Those ideas worked when work was stable, repeatable, and slow to change.
Software removed the natural limits.
Now efficiency logic scales faster than human understanding — and does.
The mercenaries of complexity thrive here
Much of the IT industry profits from this gap.
They don’t sell clarity.
They sell optionality.
The promise is always the same:
“You can customize this”
“It adapts to your workflow”
“It scales with your complexity”
What’s never priced is the interpretive burden those options impose.
So complexity is exported downstream.
The vendor books revenue.
The organization pays the tax.
The real constraint isn’t speed — it’s coherence
Organizations don’t stall because data moves too slowly.
They stall because:
Meaning diverges
Context fragments
Decisions lose lineage
No one remembers why things are the way they are
Efficiency optimizes execution.
It does not preserve understanding.
Once understanding erodes, optimization accelerates confusion.
AI doesn’t fix this — it exposes it
AI assumes stable meaning and shared intent.
Instead, it inherits:
Custom fields with folklore definitions
Workflows nobody remembers designing
Systems that work mostly — except when they don’t
AI isn’t the problem.
It’s the stress test.
A better question
Before adding a feature, tool, or integration, ask:
What new cognitive work does this create — especially when it only works most of the time?
If no one can answer clearly, the cost hasn’t been counted.
The conclusion most teams avoid
The biggest cost of software isn’t licensing.
It’s the cognitive effort required to make “mostly working” systems usable.
The most dangerous systems aren’t broken systems.
They’re systems where everything works — mostly.