Sep 24, 2026 | 8 minutes
7 AI automation examples you can build in Make (2026)
Seven real AI automation examples, each paired with a Make scenario template so you can clone the exact module chain instantly.
AI already completes small tasks for you every day: it drafts your emails, reminds you of meetings.
Meanwhile, most companies still haven’t implemented it, even in the most basic processes. Teams are losing time to handoffs and disconnected systems. McKinsey's 2026 State of AI survey found that only 44% of organizations report AI scaling across their enterprise.
AI automation can remove the inefficiencies easily: an LLM model reads input, decides what it means, and Make acts on it automatically.
This article gives you seven examples of AI automation you can act on straight away.
Key takeaways
The seven examples span customer support, sales, marketing, finance, customer feedback, IT/helpdesk, and project management.
Each example links to a real, clonable Make scenario template.
None of the templates need custom code, and only a couple lean on an app with its own paid tier.
AI automation works by having a model read unstructured input, like an email, PDF, or ticket, and having Make carry that decision straight into the next system, no manual handoff required.
What is AI automation?
AI automation is the practice of putting an AI model, like ChatGPT or Claude, directly inside an automated workflow so it can read unstructured input and decide what happens next, without a person or a fixed rule making that call.
A support ticket says "my order never showed up and I'm out $80." The model reads that sentence, works out that it's a refund request, and creates the right ticket in Freshdesk on its own.
That single step, reading messy text and turning it into a decision, is the core of AI automation. Three things make it work:
Unstructured input: the model reads free text, scanned PDFs, and voice transcripts, alongside clean spreadsheet rows.
Judgment: it weighs signals like urgency, sentiment, and intent before deciding what to do next.
Flexibility: the same scenario keeps working when the wording, the language, or the situation changes.
None of this needs a data scientist. The AI step lives inside a single module in the scenario, alongside the trigger and the action that carries its decision forward.
Make's own guide to AI automation walks you through how to set up AI workflows step by step, from picking the right AI module to structuring the scenario around it.
What are the benefits of AI automation?
Most AI tools can already read an invoice or a support ticket. The real value only shows up once a tool like Make takes that reading and acts on it, so nobody has to pick up where the AI left off.
Here are a few of many benefits AI automation brings on the table:
Faster response times: the decision and the action happen in the same run, with no queue in between.
Consistent handling: every ticket, lead, or invoice gets the same treatment, no matter who's on shift.
Judgment on messy input: Make can act on an email, a PDF, or a free-text ticket that rule-based automation can't parse.
No extra headcount: the scenario absorbs more volume without anyone new joining the team.
7 AI automation examples for your business
Each example below follows the same shape: a real department problem, a cloned Make scenario template, and the exact module chain behind it.
1. Automate customer support ticket triage and routing
A support inbox fills up fast, and most tickets are routine: password resets, refund requests, shipping questions.
Sorting them by hand means every ticket sits in a queue until someone reads it and decides who should own it.
This template reads each incoming Zendesk ticket, works out what it's about and how much expertise it needs, tags it accordingly, and routes it to the agent best suited to handle it.
Support teams cut the time tickets spend waiting for a human to triage them, and routing stays consistent no matter who's on shift or how busy the queue gets.
Clone this template: Categorize support tickets with ChatGPT
Step | Module | What it does |
1 | Trigger — Zendesk - Watch Tickets | Picks up every new support ticket |
2 | OpenAI - Generate a Completion | Categorizes the issue and the expertise it needs |
3 | Zendesk - Add, Replace or Remove Tags | Tags the ticket with that category |
4 | Router | Splits tickets by category |
5 | Zendesk - Create a Ticket Comment | Assigns the ticket to the agent with matching expertise |
2. Enrich and route sales leads automatically
A lead fills out a form, and by the time a rep gets to it, the enrichment step, the research, the CRM entry, has usually eaten up the lead's initial interest.
This template captures the submission the moment it comes in, uses AI to fill out the company's profile from what's already known about them, and creates the record in Salesforce automatically.
Sales gets notified on Slack the second a lead is ready, so reps start the conversation with context already in hand instead of doing the research themselves.
Clone this template: Enrich lead's company data with ChatGPT
Step | Module | What it does |
1 | Trigger — Webhooks - Custom webhook | Captures the lead the instant they submit |
2 | OpenAI - Generate a Completion | Enriches the company profile |
3 | JSON - Parse JSON | Structures the enriched data |
4 | Salesforce - Create a Record | Creates the lead in Salesforce |
5 | Slack - Send a Message | Notifies sales the enriched lead is ready |
3. Repurpose blog content into social media posts
Publishing one blog post used to mean writing three or four more posts by hand, one per social platform, each in a slightly different tone.
This template watches for new WordPress posts and has AI turn each one into a week's worth of platform-specific social media posts the moment it goes live, one for X, one for LinkedIn, one for Facebook, and so on.
Worth noting: this version queues the posts in Airtable for a human to review and approve rather than publishing them automatically.
The second scenario in this pair picks up the approved rows from Airtable and posts them straight to X, LinkedIn, and Facebook Pages; follow the instructions in the template's guide to connect the two.
Clone this template and the following scenario 2 (linked inside): Create social media posts from a blog using ChatGPT
Step | Module | What it does |
1 | Trigger — WordPress - Watch Posts | Catches every new blog post |
2 | OpenAI - Generate a Completion | Rewrites the post into platform-specific captions |
3 | Text Parser - Match Pattern | Splits the AI output into separate captions |
4 | Router | Branches by platform |
5 | Airtable - Create a Record | Queues each caption in Airtable for review |
Once a post is marked approved in Airtable, the second scenario in the pair takes over.
A webhook receives the approved record, and a router sends it down the right path for X/Twitter, LinkedIn, or Facebook Pages, each with its own publishing and record-update steps.
Step | Module | What it does |
1 | Trigger — Gateway - Custom Webhook | Receives the blog post, platform, image, article URL, excerpt, and Airtable record ID |
2 | Router | Routes the request based on the selected platform |
3 | X/Twitter - Create a Tweet | In case you chose to publish on X/Twitter, this module publishes the post on that platform |
4 | Airtable - Update a Record | Saves the X/Twitter post ID and marks the record as published |
5 | HTTP - Get a File | In case you chose to publish on LinkedIn, this module first downloads an image you want to use in the post |
6 | LinkedIn - Create a Company Image Post | Publishes the image and post content on LinkedIn |
7 | Airtable - Update a Record | Saves the LinkedIn post ID and marks the record as published |
8 | Facebook Pages - Create a Post | In case you chose to publish on LinkedIn, this module publishes the article link and post content on Facebook . |
9 | Airtable - Update a Record | Saves the Facebook post ID and marks the record as published |
Router filters: the Twitter route runs when Platform equals Twitter, the LinkedIn route runs when Platform equals LinkedIn, and the Facebook route runs when Platform equals Facebook.
Once both scenarios are connected, one approval in Airtable is enough to get a post live on X, LinkedIn, and Facebook Pages without opening any of the three platforms directly.
4. Automate invoice processing for accounts payable
Invoices arrive from dozens of vendors in dozens of formats, and someone on the finance team still keys most of the details by hand, line by line.
This template reads the PDF the moment it lands in the inbox, pulls out the vendor, amount, due date, and line items with OCR.
It then checks the results against the purchase order before anything moves forward.
Clean invoices move straight to approval, only the genuine mismatches land on a reviewer's desk, and every invoice gets logged automatically for the audit trail.
Clone this template:
Step | Module | What it does |
1 | Trigger — Gmail - Watch Emails | Catches every incoming invoice attachment |
2 | PDF.co - Document Parser (OCR) | Pulls vendor, amount, due date, line items |
3 | OpenAI - Generate a Completion | Flags mismatches against the purchase order |
4 | Router | Sends clean invoices to auto-approval, flagged ones to a reviewer |
5 | Data Store - Add/Replace a Record | Logs every invoice for the audit trail |
5. Analyze customer feedback sentiment automatically
Feedback piles into a spreadsheet faster than anyone can read it, so most of it just sits there until someone runs a manual review weeks later.
This template watches a Google Sheet for new feedback responses and has OpenAI label each one positive, negative, or neutral the moment it lands, then writes that label straight back into the row.
The team can filter and sort by sentiment immediately instead of reading every open-ended response, so a spike in negative feedback gets caught the same day instead of at the next review cycle.
Clone this template: Analyze sentiment in customer feedback on Google Sheets using ChatGPT
Step | Module | What it does |
1 | Trigger - Google Sheets - Watch New Rows | Fires when a new feedback response is added to the sheet |
2 | OpenAI - Generate a Completion | Classifies the response as positive, negative, or neutral |
3 | Google Sheets - Update a Row | Writes the sentiment label back into the row |
6. Classify and route IT support requests automatically
An inbox fills up with requests, some urgent, some routine, and someone has to read every single one before deciding where it goes.
This template reads each inbound email, classifies its sentiment and category with AI, and opens a Freshdesk ticket already sorted into the right queue, no manual reading required first.
IT and support teams spend less time triaging by hand, and tickets reach the right queue in seconds instead of sitting in a shared inbox waiting to be read.
Clone this template: AI email classification with Gmail, ChatGPT & Freshdesk
Step | Module | What it does |
1 | Trigger — Gmail - Watch Emails | Picks up every inbound email |
2 | OpenAI - Generate a Completion | Classifies sentiment and category |
3 | JSON - Parse JSON | Structures the classification result |
4 | Router | Splits by category |
5 | Freshdesk - Create a Ticket | Opens the ticket in the right queue |
7. Keep project trackers updated automatically with AI
A Notion tracker is only useful if it's up-to-date, and keeping it that way usually means someone in the team manually reviewing and updating every item.
This template watches a Notion database for new or updated items, sends each one to AI to summarize the status, flag risk, or suggest a next step, and writes that result straight back into the same record.
The tracker stays current without anyone opening it just to update a status field, and the team can trust what they see in Notion is actually up to date.
Clone this template:
Step | Module | What it does |
1 | Trigger — Notion - Watch Database Items | Fires on a new or updated project item |
2 | OpenAI - Generate a Completion | Summarizes status, flags risk, or suggests next steps |
3 | Notion - Update a Database Item | Writes the AI's output back into the tracker |
Start with the example that fits your team
Pick the department losing the most time to manual handoffs and clone that example first.
None of these seven need custom code or an enterprise subscription, so almost any team can get one running this week.
From there, browse Make's template library for more use cases, or get started for free and build your own.
Frequently asked questions
Q1: What is an example of AI automation?
One example from this list: a support ticket lands in Zendesk, AI reads it, decides which team it needs, tags it, and routes it to the right agent automatically. Each of the seven examples above follows that same pattern with a different department and a different template to clone.
Q2: What are the four types of automation?
Most guides group automation into basic scripted tasks, robotic process automation that mimics clicks on a screen, workflow automation that connects apps through fixed rules, and AI automation, which adds judgment on top. AI automation is the only one of the four that can read unstructured input like an email or a PDF and decide what happens next, which is what every example above does.
Q3: What are AI automation systems?
An AI automation system needs three pieces: a trigger that starts the run, an AI step that reads the input and makes a decision, and an action that carries that decision into another app. In Make, that's a trigger module, an OpenAI, Anthropic (or similar) module, and one or more action modules chained together in a single scenario.
Q4: What is the best AI automation tool?
There's no single best tool. It depends on whether the job touches one app or several.
Make fits when a workflow needs to move between multiple systems and make an AI-driven decision along the way, all without custom code. A few connected apps carry their own separate plan requirements, so it's worth checking those before you clone a template.
Q5: Can I use these Make templates on a free plan?
No. The module chains themselves need a paid Make plan to run, and you should also check each connected app's own plan requirement, such as a CRM seat or an OCR add-on's usage tier, before cloning the scenario.
Q6: Do I need to code to build one of these examples?
No. Every template above is built visually in Make's Scenario Builder using drag-and-drop modules, and each one can be cloned, renamed, and edited without writing a line of code.
Ready to make the automation revolution happen?
