Automation is often seen as the fastest way to improve efficiency.

When something feels slow, manual, or error-prone, the instinct is clear:
“Let’s automate it.”

In reality, automation doesn’t fix broken processes.
It amplifies whatever structure already exists, including confusion.

Automation is not a starting point

Automation is a multiplier.

If your process is:

  • unclear,

  • fragmented,

  • full of exceptions,

  • dependent on individual heroics,

automation won’t simplify it. It will make it faster, harder to understand, and more expensive to change.

This is why many automation projects deliver disappointing results despite significant investment.

What “automating chaos” looks like in practice

I’ve seen this pattern repeatedly:

  • A process is poorly defined, but “everyone kind of knows how it works”

  • Edge cases are handled informally

  • Responsibility shifts depending on availability

  • Data lives in multiple places

Instead of addressing these issues, automation is introduced to:

  • reduce manual work,

  • speed up execution,

  • increase control.

The result?

  • faster propagation of errors,

  • more rigid mistakes,

  • and systems no one fully understands.

Chaos, but automated.

Why automation often feels urgent

Automation becomes attractive when:

  • teams are overloaded,

  • delays are visible,

  • errors are costly,

  • leadership wants quick relief.

The pressure is real.
But urgency should not replace understanding.

Skipping clarity may feel faster in the short term, but it almost always slows things down later.

What clarity actually means

Clarity doesn’t require perfect documentation or complex diagrams.

It means being able to answer simple questions consistently:

  • What triggers this process?

  • Who owns each step?

  • What defines completion?

  • What happens when something goes wrong?

If different people give different answers, automation is premature.

The right sequence: clarity → structure → automation

Successful automation follows a clear order:

  1. Clarify reality
    Understand how work actually happens today.

  2. Design structure
    Define ownership, rules, and handoffs.

  3. Stabilize behavior
    Ensure the process works without heroics.

  4. Automate deliberately
    Only then introduce tools and automation.

This sequence reduces risk and increases return.

Automation should reduce thinking, not replace it

Good automation:

  • removes repetitive effort,

  • enforces clear rules,

  • supports decision-making.

Bad automation:

  • hides complexity,

  • locks in poor assumptions,

  • removes flexibility where judgment is still needed.

The difference lies in whether clarity existed beforehand.

When automation works best

Automation delivers real value when:

  • processes are understood,

  • exceptions are known,

  • responsibilities are clear,

  • and goals are aligned.

In those conditions, automation doesn’t just save time - it creates space for better work.