Automating tasks with AI means building a workflow where AI handles the decisions that used to require a human in the loop. In practice, that looks like this: a trigger fires when something happens (a form submission, a new email, a Slack message), AI reads the input and decides what to do, then the workflow takes action automatically. On aidowith.me, the Make Automation route has 12 steps that walk you through building exactly this kind of workflow. You pick a repetitive task, map the trigger and action, connect your apps in Make, and add an AI module that processes incoming data. The route covers webhook setup, prompt engineering inside Make scenarios, error handling, and testing. No code required. The full route takes about 2 hours and ends with a running automation that saves you time every week. Most people pick a task that was taking 3 to 5 hours a week and get it down to under 15 minutes of review.
Last updated: April 2026
The Problem and the Fix
Without a route
- You spend 3 hours a week on repetitive tasks that follow the same pattern every time
- You've looked at Make or Zapier but don't know where to add AI or how to prompt it
- Every automation tutorial shows simple button-clicks but breaks when you add real data
With aidowith.me
- 12-step route that builds a real AI-powered workflow from trigger to action in one session
- Specific guidance on where to add AI inside Make and how to prompt it for consistent output
- A running automation that handles a real task at the end of the route, not just a demo
Who Builds This With AI
Ops & Analysts
Summaries, process docs, and structured output from messy inputs.
Managers & Leads
Reports, presentations, and team comms handled faster.
Marketers
Content, campaigns, and briefs done in hours instead of days.
How It Works
Pick your task and map the workflow
Choose a repetitive task with a clear trigger and outcome. Map the inputs, the AI decision point, and the action. This design step prevents rework later.
Build and connect modules in Make
Connect your trigger app, add an AI module with your prompt, and connect the output to your action app. The route walks through each connector and setting.
Test, debug, and activate
Run the scenario with real data, handle edge cases, and activate. You finish with a live workflow running automatically on a schedule or trigger.
Build your first AI automation in Make
12 steps. About 2 hours. A running workflow that handles a real task automatically.
Start This Skill →What You Walk Away With
Pick your task and map the workflow
Build and connect modules in Make
Test, debug, and activate
A running automation that handles a real task at the end of the route, not just a demo
"I automated my lead intake process in one afternoon. Every new form submission now gets categorized, summarized, and routed by AI before I even see it."- Operations manager, digital agency
Questions
Make (formerly Integromat) lets you build visual workflows that connect apps and add AI processing without writing code. You set a trigger, add an AI module with a prompt, and define the action. The aidowith.me Make Automation route covers every step: choosing a workflow, prompting the AI module, handling errors, and testing with real data. The 12-step route takes about 2 hours and ends with a working automation. No coding background needed.
Any task where AI can read an input and decide what to do next. Common examples include: categorizing and routing incoming emails, summarizing customer feedback and tagging by topic, generating first-draft responses to support tickets, extracting data from documents and updating a spreadsheet, and creating social media drafts from new blog posts. The key is that there's a repeatable pattern. If you do the same type of decision 20 times a week, it's a good automation candidate. The route helps you pick the right task and build the workflow.
Following the 12-step route on aidowith.me, most people finish a running automation in about 2 hours on their first attempt. That includes picking the task, connecting apps, setting up the AI prompt, testing with real data, and activating the scenario. Subsequent automations go faster because you reuse the same pattern. The first one takes longer because you're also getting familiar with how Make scenarios work and how to structure AI prompts inside a workflow.