Speed amplifies chaos.
I've watched it happen. A company automates their broken processes and wonders why everything got worse. They expected efficiency. They got faster failure.
Automation doesn't fix problems. It scales them.
The appeal of automation
When processes feel slow, automation seems like the obvious answer.
Manual data entry? Automate it. Repetitive approvals? Build a workflow. Report generation taking too long? Schedule it.
The logic is seductive. Machines are faster than humans. If we can remove the human bottleneck, everything will speed up.
This is true — but incomplete. It misses the part where the existing process was held together by human judgment, workarounds, and institutional knowledge that nobody documented.
What actually happens
I recently worked with a company that automated their lead qualification process. The old way: a sales rep manually reviewed each lead, scored it, and routed it to the right team. Slow, but it worked.
The new way: a workflow tool scored leads based on form data and automatically assigned them. Fast. Scalable. Modern.
The result: garbage leads flooded the sales queue. Good leads got lost. Conversion rates dropped 40%.
The old process wasn't just "manual data entry." It was a sales rep applying judgment — noticing that a lead from a certain industry should go to a specialist, recognizing that a particular company was a competitor doing research, catching data entry errors before they propagated.
The automation removed the human judgment but kept the broken inputs. Then it scaled the broken inputs at machine speed.
The pattern
Here's what I see over and over:
Step 1: A manual process works, but it's slow.
Step 2: Someone automates the process without fully understanding why it worked.
Step 3: The automation runs faster, but produces worse outcomes.
Step 4: Teams add exceptions, overrides, and manual review steps to catch the failures.
Step 5: The "automated" process is now more complex than the original, with all the old problems plus new ones.
The fundamental mistake: treating automation as a speed improvement when it's actually a structural change.
When automation fails
Automation fails when:
The inputs are unreliable. Garbage in, garbage out — but faster. If your data is messy, automating processes that depend on that data just creates messy outputs at scale.
The process requires judgment. Some decisions need context that's hard to encode. Automating them means encoding bad assumptions.
Nobody owns the outcome. When a manual process fails, someone notices. When an automated process fails, it might run for months before anyone realizes.
The edge cases are common. Automation handles the happy path well. If 30% of your cases are edge cases, you've just automated 70% of the work and created a mess for the rest.
What I do instead
Before automating anything, I ask:
What problem are we actually solving? "It's slow" isn't a problem statement. Why is it slow? What's the bottleneck? Sometimes the fix is simpler than automation.
What breaks if we automate this? Walk through failure modes. What happens when the data is wrong? What happens when the rules don't apply? What happens when no one is watching?
Who owns the automated process? Automation isn't set-and-forget. Someone needs to monitor it, update it, and handle exceptions. If the answer is "no one," don't automate.
Can we simplify first? Often the process is slow because it's unnecessarily complex. Simplify first, automate later. A simple manual process is easier to automate correctly.
The right order
The sequence matters:
- Understand what the current process actually does, including the hidden judgment calls.
- Clean the inputs. Fix data quality. Remove unnecessary steps. Standardize what can be standardized.
- Document the rules, including the exceptions.
- Simplify the process. Remove complexity that automation won't handle well.
- Automate the clean, simple, well-understood process.
- Monitor the outcomes. Build in feedback loops.
Most companies try to skip to step 5. They automate the mess and wonder why the mess got messier.
The leverage is in the structure
Automation is powerful, but it's not magic. It's leverage — it amplifies whatever's underneath.
Strong structure + automation = exponential improvement.
Weak structure + automation = exponential chaos.
The companies that get the most from automation are the ones that do the structural work first. They clean their data. They define clear processes. They establish ownership. They build systems that work manually before trying to make them work automatically.
It's slower to start. It's faster in the end.
The question to ask
Before any automation project, ask: "If this runs a thousand times faster, what happens?"
If the answer is "we process more leads" — great.
If the answer is "we create more mess" — fix the structure first.
Speed without structure just gets you to the wrong place faster.
For questions about automation and process design, reach out here.