Automation is a Supercharger—Make Sure It’s Not Supercharging a Mess

Automation has been around for decades, but AI has pushed it into overdrive. Everywhere you look, systems are promising to “eliminate manual tasks” and “automate your workflow.”

Take HubSpot, for example—one of its biggest selling points is automation. And for good reason: it can streamline processes, improve efficiency, and help you scale faster.

Sounds great, right?

Well, yes and no.

Automation is easy to implement but hard to get right. If your processes, data, and workflows aren’t solid, automation won’t fix the problem—it will exasperate it.

The biggest mistake businesses make? Thinking automation is a magic switch.

Here’s what it takes to set up automation the right way—so that it delivers results, not headaches.

Automation Can Supercharge a Process—But It Can Also Supercharge a Mess

Automation doesn’t fix problems—it amplifies them.

If your process is clean and efficient, automation will make it better, faster, and more scalable. But if your process is broken, all you’re doing is automating inefficiencies, spreading mistakes faster, and creating bigger headaches.

Three Critical Questions Before You Automate

Automation should be a strategic decision, not a knee-jerk reaction.

Musk’s 5-Step Approach: Remove, Reduce, THEN Automate

Elon Musk knows how to design highly efficient processes. His efficiency algorithm is gold for automation, yet most businesses skip the first four steps and jump straight to automation. That’s why they fail.

The 5 Steps of Musk’s Efficiency Algorithm:

1. Question every requirement - Why does this process exist? Who created it? Does it still make sense?
2. Delete any part of the process you can - If you don’t end up adding at least 10% back later, you didn’t delete enough.
3. Simplify and optimise - Don’t waste time improving steps that shouldn’t exist in the first place.
4. Accelerate cycle time - Speed things up—but only after the process is optimised.
5. Automate - Only now do you automate. Automating before fixing the process is like putting a turbocharger on a car with no brakes.

Most companies ignore Steps 1–4 and jump straight to automation. That’s why so many automation projects fail or lead to unintended consequences.

Automation Runs on Data—And Bad Data Breaks Everything

We’ve all seen it. That cringe-worthy automated email that lands in your inbox:

"Dear [First Name], thanks for your interest in our product!"

Just one small mistake in how data is structured, and your automation instantly feels robotic, impersonal, and lazy.

Garbage In, Garbage Out

Automation doesn’t think. It just processes whatever data it’s given. If your data is messy, missing, or inconsistent, automation won’t fix it—it will just spread the problem faster.

Key Data Considerations for Automation:

Do you have the right data points?

• Example: Automating a welcome email sounds simple. But if your CRM doesn’t consistently capture first names, your automation is instantly broken.

Is your data mapped correctly?

• Where is it coming from? Where is it going?
• If your automation is pulling from the wrong source, it’s a ticking time bomb.

Can your data scale?

• Works for 100 customers, but will it work for 10,000?
• Have you accounted for exceptions, errors, and anomalies?

The bottom line? If your data isn’t structured correctly, your automation won’t work.

The Danger of Optimising a Single Step Without Looking at the Whole Process

It’s easy to automate one part of a system and call it a win. But if you don’t look at the big picture, automation can actually create new inefficiencies.

Example: The Over-Automated Factory

Let’s say a factory automates the production of bolts and nuts at record efficiency.

• But… there’s nowhere to store the inventory.
• The shipping process can’t keep up.
• Demand doesn’t support the increased output.

So instead of improving efficiency, they’ve created a bottleneck and wasted resources.

The same thing happens in businesses:

• You automate lead generation, but your sales team can’t handle the volume.
• You automate invoice processing, but your finance team can’t reconcile payments fast enough.

Automation should improve the whole system, not just one piece.

How to Get Automation Right

If you want automation that actually works, follow this structured approach:

1. Start by Clearly Mapping and Understanding the Process
2. Question Everything (Do we need this step? Can we remove it?)
3. Ensure Your Data is Clean & Mapped Correctly
4. Start Small, Then Scale
5. Choose the Right Tools
6. Monitor and Optimise Continuously

Final Thoughts: Automation is a Tool, Not a Shortcut

Automation isn’t a magic fix—it’s an amplifier. If your processes are solid, automation will make them faster and more efficient. If they’re broken, automation will multiply inefficiencies and problems.

So before you automate, ask yourself: Are you supercharging efficiency—or supercharging a mess?