Adding More Information Tools Is the Right Answer to the Wrong Problem
The problem isn’t that people lack information. It’s that none of it adds up.
Every time work feels chaotic, the same reflex kicks in:
We need a better tool.
A new dashboard.
A unified platform.
A smarter AI assistant.
A single source of truth.
And to be fair, this reflex isn’t stupid. It’s rational. If things are confusing, surely the fix is better information.
Except it almost never works.
Not because the tools are bad—but because they are answering the wrong question.
The Mistake We Keep Making
When organizations struggle, they usually frame the problem like this:
People don’t have enough information, visibility, or access.
So they invest in tools that promise:
more data
more transparency
more reporting
more real-time insight
more automation
But what people are actually struggling with is something else entirely:
They don’t know what the information means.
This is the core category error of the digital age.
We keep trying to fix a meaning problem with information tools.
Information Is Not Understanding
Here’s the uncomfortable truth:
You can have perfect access and still be confused.
You can have real-time dashboards and still make bad decisions.
You can have AI summaries and still not agree on what’s happening.
That’s because information doesn’t coordinate action.
Meaning does.
Or, as David Deutsch famously argued:
Knowledge is not information. Knowledge is the capacity to generate explanations.
Most enterprise tools are brilliant at storing and moving information.
Almost none are designed to preserve or transmit explanations.
Why More Tools Make Things Worse
Adding tools feels productive, but it often increases friction. Here’s why.
1. Tools Fragment Context
Each system captures a slice of reality:
CRM → sales reality
Jira → delivery reality
Finance system → cost reality
Support system → customer pain
Each slice is locally accurate.
Together, they rarely add up to a coherent picture.
People aren’t disagreeing because they’re wrong.
They’re disagreeing because they’re solving different problems based on different contexts.
2. Tools Preserve Artifacts, Not Reasoning
Most systems remember:
what was decided
what was built
what the metric is
They forget:
why the decision was made
what alternatives were rejected
what assumptions were in play
what tradeoffs were accepted
So six months later, the artifact remains—but the meaning is gone.
Teams inherit outcomes without inheriting understanding.
That’s how organizations end up undoing their own work.
3. AI Accelerates the Wrong Layer
AI didn’t introduce this problem—it exposed it.
When you ask AI to summarize meetings, strategies, or roadmaps:
it compresses language
removes nuance
invents coherence where context is missing
The result isn’t stupidity.
It’s contextless competence.
AI makes the fragmentation faster, smoother, and more convincing.
The Real Problem Isn’t Information Scarcity
We like to say we’re overloaded with information.
That’s not quite right.
If volume were the issue, Google wouldn’t work.
The real problem is this:
We scaled information faster than we scaled shared understanding.
In smaller, co-located organizations, meaning was ambient:
you overheard conversations
you knew the backstory
you shared history
context traveled socially
Now context is scattered across tools, documents, messages, dashboards, and people’s heads.
Meaning evaporates at every handoff.
Why “Better Tools” Keep Failing
When leaders see confusion, they respond with:
more dashboards
more reporting
more alignment rituals
more shared platforms
But these operate at the artifact layer.
The failure is at the interpretation layer.
You can standardize templates forever.
You cannot standardize understanding without shared models of reality.
This is why organizations can be:
rational locally
irrational globally
Everyone is doing the right thing—
just not in the same world.
What Actually Needs to Change
The fix is not fewer tools or better tools.
The fix is treating context as first-class infrastructure.
That means designing systems that:
preserve why, not just what
keep reasoning attached to decisions
make assumptions visible
allow meaning to evolve without resetting history
help humans and AI interpret the same reality
In other words:
Stop optimizing information flow.
Start architecting meaning flow.
The Line That Matters
Adding more information tools feels like progress because it produces activity.
But activity is not coherence.
Until organizations stop confusing information with understanding, they’ll keep buying better answers to the wrong problem—and wondering why work still doesn’t make sense.