Automation has been around for a long time: when a form is submitted, add a row to a spreadsheet; when an invoice is paid, send a receipt. What is new is that AI can now handle the steps in between that used to require someone to read, understand and decide.
Traditional automation and AI automation
Traditional automation follows fixed rules. It works well with structured data, such as a number in a field or a choice from a list, and it breaks when the input varies. A customer email that says the same thing in a different way is enough to stop it.
AI automation adds steps that can work with ordinary language. An AI step can read a free-text message, work out what it is about, pull out the details that matter and draft a response. The fixed rules still do the moving of data; the AI handles the reading and writing.
How a workflow is built
Every workflow has the same three parts: a trigger that starts it, a series of steps, and an output. Here is a simple example for incoming customer email:
- Trigger: a new email arrives in the support inbox.
- Step 1: AI classifies the topic and the urgency.
- Step 2: AI extracts the order number and the customer’s request.
- Step 3: a ticket is created in the right queue.
- Output: a draft reply is prepared for a person to review and send.
What makes a good candidate
- The task happens often.
- It follows roughly the same pattern each time.
- The input is mostly text: emails, forms, documents, messages.
- Mistakes are easy to notice and easy to fix.
Where to be careful
Do not hand over decisions with serious consequences, such as refunds above a limit, contract terms or anything involving personal data, without a person in the loop. A good starting pattern is “draft and approve”: the workflow prepares the work and a person confirms it.
Start with one workflow, watch the results for a few weeks, and expand only when you trust what it produces.