AI glossary for businesses
Workflow automation
In short
Workflow automation means using software to carry out the repeatable steps of a business process, such as copying data, sending notifications or creating tasks, without manual work. Tools like Zapier and Make connect apps through simple "when this happens, do that" rules. Adding AI lets those workflows also read, sort and draft text.
How workflow automation works
Most automations follow one pattern. A trigger (a new order, a form submission, an email) starts one or more actions (add a row, send a message, create an invoice draft). The tools talk to each other through their APIs.
Picture a row of dominoes. You set them up once, then one push makes them fall in order every time. Classic automation is exactly that: fixed rules and predictable results. It works well when the input is structured, like a form with set fields.
AI adds a new kind of domino. It can handle messy input, such as a free-text email or a PDF, by reading it, deciding which category it belongs to and drafting a reply. That makes automation useful for many more tasks, but it also adds uncertainty, because AI can be wrong.
In practice for a small business
A small courier company in Thessaloniki gets dozens of emails a day: delivery changes, complaints and quote requests. With Make or Zapier, each new email goes to an AI model that tags it by type and urgency. Quote requests go to sales with the key details extracted. Complaints create a task for the support lead. A person still approves anything sent to a customer. The email triage and routing use case describes the steps.
Common tools: Zapier is easy to start with and connects thousands of apps, though costs can rise as task volume grows. Make gives more control over complex flows, but has a steeper learning curve. Microsoft Power Automate fits well if you already use Microsoft 365, though setup can feel technical.
Why it matters and what to watch out for
Automate a good process, not a broken one. Fix the steps first, then automate them.
Plan for failures. Apps change and connections break. Add alerts and check runs regularly.
Keep people on key decisions. Let AI sort and draft, and let people approve what goes to customers.
Data flows across tools. Each step may send personal data to another service. Under GDPR, check each vendor's terms and send only the data that is needed.
Know the difference from agents. An automation follows fixed steps. An AI agent decides its own next steps, which brings more flexibility and more risk.