What you should not automate in the beginning

## What you should not automate in the beginning

**Automation in the beginning often hinders than helps. It creates a sense of progress, but it takes away the main thing - the understanding of what is happening at all. There are things that are important to live with your hands first, even if the AI can do them faster.

## Where does the desire to automate everything come from

When you first start working with AI, the idea comes very quickly: since it can write code, you can immediately automate everything. Setting up the project, logic, checks, deploy, structure. I want to press the button and get the “ready system”.

That wish is understandable. Automation looks like a sign of maturity and the right approach. It seems that if something is done automatically, then you did everything right. Especially if you are not from a technical environment and want to rely on AI as a crutch.

## Why Automation Gives You a False Feeling of Control

The problem is that automation hides the process. It removes the steps that are important to you right now. When something happens automatically, you don’t notice the order in which the action takes place, where errors occur, and what the outcome depends on.

In the end, you seem to “manage the system”, but in fact you just look at the result, not understanding how it turned out. As long as it's working, it doesn't feel like it. Once something breaks down, automation becomes a black box.

## Why you should not automate the logic of the task

The most common mistake is to automate the business logic or basic behavior of the program. You ask the AI to do the whole thing, to set up connections, to process data, to react to events, and you get the mechanism ready.

The problem is that you don’t know where decisions are made. Logic becomes smeared in code, and any change becomes a risk. In the beginning, it is important to see exactly where the selection, calculation or verification takes place, even if it seems primitive.

## Why is it dangerous to automate repair

Another catch is to automate error correction. When things don’t work, it’s easy to ask the AI to “fix” over and over again without figuring out what’s broken. Formally, the problem disappears, but there is no understanding.

Over time, you lose the connection between cause and effect. An error ceases to be a signal, but becomes just a noise to be silenced. In the beginning, it is much more important to at least try to understand what happened, and only then use AI as an assistant, not as a wizard.

## Why You Should Not Automate Results

Paradoxically, an automatic check in the beginning can also hurt. If you immediately rely on AI-generated tests, logs, and checks, you begin to take their word for it.

Until you know what exactly counts as the right outcome, any automatic test is just another layer of magic. In the beginning, it is more useful to manually look at the result and ask yourself a simple question: this is actually what I wanted to get.

## Where Automation Begins to Be Useful

Automation works for you when you already understand the process. When you know what steps are repeated, where you often make mistakes, and what you want to speed up. At this point, she stops hiding the meaning and begins to save time.

Until then, automation is almost always premature. It makes the work look neat, but internally empty.