Jim

It started with one simple question:

Why am I still doing this manually?

I was working on a spreadsheet, checking information row by row, sending emails, and updating statuses. The process was not difficult, but it was repetitive. One small mistake could mean sending incorrect information, overlooking a request, or forgetting to update a row.

I knew there had to be an easier way.

I wanted the spreadsheet to detect when a status changed to Sent, find the email address in the same row, send the correct message, and add a timestamp after the email was sent.

The idea sounded simple in my head. The problem was that I did not know how to build it. I was still learning how to code, so I asked AI for help.

My first prompt was something like:

Create a script that sends an email from Google Sheets.

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AI immediately generated a script. It looked impressive, but it did not work the way I expected. The script checked the wrong column, used information from the wrong cells, and failed when some required details were missing.

That was when I realized the problem was not only the code. My instructions were too vague.

I tried again, but this time I described the entire process:

When the status in Column G changes to "Sent," get the email
address from Column E and send a confirmation email.

After the email is sent successfully, add a timestamp to Column H.

Do not send the email if any required information is missing.

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The result was much closer to what I needed.

When I changed the status to Sent and received the email automatically, I felt genuinely excited. It was only a small automation, but it removed several manual steps from the process.

After building that first workflow, I started noticing repetitive tasks everywhere.

A form submission could automatically send a confirmation email. A spreadsheet could detect missing information before processing a request. A completed action could record the date and time. Reports could be organized without manually copying every row.

Tasks that previously took several minutes could happen in seconds.

AI helped me understand that many automations follow a simple structure:

Trigger → Condition → Action

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Something happens, the automation checks whether certain requirements are met, and then it completes the assigned task.

Thinking this way made automation easier for me to understand. Before writing any code, I could first map out what needed to happen.

Not every script worked on the first try. Some produced errors. Others failed when a cell was empty or when someone entered a status with extra spaces. There were also times when fixing one issue created another.

Those problems taught me that AI can help generate code, but it cannot replace understanding and testing.

Now, I ask questions like:

  • What could cause this script to fail?
  • What happens when a required cell is empty?
  • How can I prevent duplicate emails?
  • Can this handle multiple edited rows?
  • Can you explain each section in simple language?

These questions help me learn instead of simply copying and pasting code.

I also learned that not every task should be automated. Some tasks still require human judgment, personal communication, or careful review.

For me, automation makes the most sense when a task is repetitive, based on clear rules, completed frequently, time-consuming, or prone to manual errors.

The goal is not to remove people from the process. It is to reduce repetitive work so people can focus on tasks that require attention, creativity, and better decision-making.

I did not begin this journey with the goal of becoming an expert programmer. I simply wanted to solve one repetitive problem.

That problem encouraged me to learn Google Apps Script, understand basic JavaScript, and explore how AI could support my learning.

I am still learning, testing, and making mistakes. However, I now look at repetitive processes differently.

Whenever I find myself completing the same steps again and again, I stop and ask:

Can this be automated?

What repetitive task made you interested in automation?