用 AI 自動化你的重複工作:從 Zapier 到 AI Agent 實戰

At 9 PM, the owner of a small online women's clothing store was still at work. She was doing what she did every day: manually copying and pasting orders from Shopee and her official website into a Google spreadsheet, and then notifying her shipping colleagues via LINE. 40 orders, 40 times of copying and pasting. She had been doing this for two years and never thought it could be done differently.

Until she handed this process over to a machine, and suddenly had an extra 30 minutes every evening to spend with her child.

This article will tell you how to outsource "repetitive, brainless, but necessary" tasks - from the simplest tool automation to AI agents that can think for themselves. If you're not sure what an agent is, you can start by reading our introductory article What is an AI Agent.

First Layer: Traditional Automation, Connecting "Dead Processes"

Many repetitive tasks are "fixed processes" - as long as A happens, do B and then C, without needing to make judgments. These tasks are most suitable for traditional automation tools, which don't require AI.

There are three representative tools, each with its own characteristics:

• Zapier: The easiest to use, with a user-friendly interface and the most connected services. Suitable for those who "just want to quickly connect two apps".
• Make: Uses visual flowcharts to connect processes, can handle more complex multi-step processes, suitable for those who like to see the big picture.
• n8n: Can be self-hosted on a company server, data doesn't leave the premises, loved by engineers, and suitable for industries sensitive to privacy.

Taking the owner of the clothing store as an example, her first version of automation looked like this:

1. Trigger: New orders on Shopee or the official website.
2. Action 1: Automatically write order data into a Google spreadsheet.
3. Action 2: Automatically send a LINE message to notify shipping colleagues.

These three steps were fully automated without AI, simply "connecting dead processes". Just doing this, she saved 40 copying and pasting actions every day.

Second Layer: Adding AI, Making Processes "Judgmental"

The ceiling of traditional automation is obvious - it doesn't think. Once a process requires "judgment", it gets stuck. This is where AI comes in.

Still taking the owner of the clothing store as an example. She found that customers would write various messages in the order notes: "I want to change my address", "Can you help me gift wrap?", "Ship next week". Traditional automation would simply copy these words into the spreadsheet, and she would still have to check each one.

After upgrading her process:

1. Trigger: New order arrives.
2. AI Judgment: Use ChatGPT or Gemini to categorize the notes - is it "change address", "special packaging", or "delayed shipping"?
3. Branching: Automatically mark "change address" in red, schedule "delayed shipping" in the calendar, and handle normal orders as usual.
4. Notification: Only notify her of orders that require human handling.

What's the difference? Before, she had to check every order; now, the machine filters them first, and she only checks the ones that "really need human attention". AI acts as a "judgmental force" in the process.

Third Layer: Handing Over to AI Agents, Letting Them Run the Whole Process

In the first two layers, you still need to design the process flow and connect each line. The third layer takes it further: you only describe the goal, and let the AI agent think of the steps and execute them.

Let's take a real scenario. A freelance designer, every time they take on a new project, has to do a series of tedious tasks: create a project folder, copy and fill in the contract template with client data, send a welcome email, and open a project card in the project management tool.

Using an agent, they only need to say: "I've taken on Mr. Wang's website design project, quoted 120,000, please run the project initiation process". The rest is left to a general-purpose agent like Manus or an enterprise-level agent like Dust to break down and execute. It will decide which tool to use for each step, rather than waiting for you to connect each line.

Note that the third layer is the most flexible but also the least controllable - it may send the email to the wrong person. So, the more you use this kind of hands-off automation, the more you need to set up a "check with me before sending" barrier at critical steps (sending emails, making payments).

How to Start: Choosing the Right First Automation

Don't try to do the most impressive thing at first. I suggest you follow this order to choose your first automation:

1. Find the most annoying task: Something you do more than three times a week, and doesn't require thinking. Organizing orders, forwarding emails, and data aggregation are all good targets.
2. Determine if it needs "judgment": If not, use Zapier or Make; if it needs classification or writing, add an AI layer; if you want to hand over the whole process, try an agent.
3. Start with the minimum viable version: The first version just needs to work, don't aim for perfection. Once it's running, you can add more features.
4. Set up safety valves: For any actions that "go outside" (sending emails, deducting payments, sending documents), always set up a human confirmation step.

To find more ideas on "which tasks can be automated", our AI Task List and Prompt Templates can be used directly.

TheAI Academy's Suggestions

I've seen too many people get stuck in "researching which tool is the strongest", but haven't actually created any automation. My honest advice is: the difference between tools isn't as big as you think, the real difference lies in "whether you've actually taken action to create your first process".

A practical suggestion for Taiwanese SMEs: if your team doesn't have an engineer, start with Zapier, which has the most Chinese resources and tutorials, and is the least prone to errors. If you're concerned about data not leaving the premises (e.g., medical, legal, accounting), look directly at n8n and host it on your own server. Once you've created three to five automations and developed the intuition to "think about automation first", you can move on to more advanced AI agents. First, aim to have something, then aim for perfection.

If your automation starts to involve "writing a small program" to complete, it's time to look at our series' third article Coding Agent and Vibe Coding Introduction.

Back to the owner of the clothing store. She didn't become an engineer, and she didn't spend a dime hiring someone. She simply handed over those 40 copying and pasting actions, and the customers' varied notes, to the machine. The extra 30 minutes she gained, she spent with her child. The most fascinating thing about automation is not saving a few minutes, but that it silently takes on those tedious tasks that steal your life.