7 AI automation examples you can copy in Make (2026)

The best AI automation examples show AI handling one specific business task end to end, from reading unstructured input to taking the next action inside a defined workflow.

Real examples of this kind of AI workflow automation can include almost anything these days with the pace of AI advancement. 

Each example below links to a cloned Make scenario template with the exact module chain, not just the concept.

AI automation examples are easy to describe and hard to build. Most guides explain the concept: an AI model reads something, decides something, then stops. 

Someone still has to move that output into the ticketing system, CRM, or accounting tool by hand, which cancels out the speed AI promised. 

McKinsey's 2026 State of AI survey found 44% of organizations now report AI scaling across their enterprise, up from 38% a year earlier. 

The seven examples below close that gap: each pairs the AI step with a Make scenario template, so you clone the module chain, not just the idea. 

Each example follows the same shape: a real department problem, a cloned Make scenario template, and the exact module chain behind it, not just a description of the concept.

1. How does AI automate customer support tickets?

Department: customer support. 

 Department: customer support.

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

2. How does AI qualify and route sales leads?

Department: sales.

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.

3. How does AI turn one blog post into a week of social content?

Department: marketing.

Publishing a blog post used to mean writing three or four more captions by hand, one per platform, in a slightly different tone each time.

This template watches for new WordPress posts and has AI rewrite each one into separate, platform-specific captions the moment it goes live.

Worth noting: this version queues the results in Airtable for a human to review and approve, it doesn't publish them automatically, that is where the second scenario comes in. Follow the instructions in the template follow along guide to conplete the AI automation.

Clone this template and the following scenario 2 (linked inside): Create social media posts from a blog using ChatGPT.

4. How does AI process invoices for accounts payable?

Department: finance.

Invoices arrive from dozens of vendors in dozens of formats, and someone on the finance team still keys most of the details in 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, and 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: Automate AI-powered invoice processing.

5. How does AI automate new-hire onboarding?

Department: HR.

A new hire accepts their offer, and someone on the HR team still has to remember to post a welcome message and loop in the right people on the right day.

This template watches an Airtable base and fires a Slack welcome message the moment a new hire's onboarding stage is set to "Welcome Message," so nobody has to remember to do it manually.

As it stands, this particular template doesn't include an AI step; add an OpenAI or Claude module before the Slack message to personalize the wording and generate a first-week checklist per hire.

New hires get a consistent welcome regardless of who on the HR team is busiest that week, and the base template still removes the manual trigger entirely.

Clone this template: Send Slack welcome message for new employees.

6. How does AI triage internal IT requests?

Department: IT/helpdesk.

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, and Freshdesk.

7. How does AI keep a project tracker up to date on its own?

Department: project management.

A Notion tracker is only useful if it's current, and keeping it current usually means someone 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: Send notion items to ChatGPT and update your database.

Which AI automation example will you try first?

Every example above starts the same way: a template you can clone, not a blank canvas. 

Pick the department where manual handoffs cost your team the most time. 

None of the seven examples above need custom code or an Enterprise-only app, so almost any team can clone one this week and watch the module chain run end to end.

Then browse Make's full template library, a workflow automation platform built for exactly this kind of AI-to-business-system handoff, for thousands more starting points.

Frequently asked questions

Q1: What is an AI automation example?

It's a real workflow where AI does the thinking, classifying, drafting, or scoring a piece of unstructured input, and an orchestration layer like Make carries that output into the next business system automatically, without anyone copying and pasting.

Q2: How is AI automation different from regular automation?

Regular automation follows fixed, pre-set rules and only handles inputs it already expects. AI automation adds judgment: it reads unstructured text, images, or audio and decides what should happen next on its own.

Q3: 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.

Q4: Which department should automate with AI first?

Start wherever unstructured requests pile up the fastest, usually customer support or IT. Both examples above classify, route, and reply automatically within seconds of a request arriving, so the backlog stops growing.

Q5: Can I use these Make templates on a free plan?

The module chains themselves run on any paid Make plan. Still, check each connected app's own plan requirement, such as a CRM seat or an OCR add-on's usage tier, before you clone the scenario.

Q6: How long does it take to build an AI automation example like these?

Cloning a template and connecting your own accounts usually takes under an hour for someone comfortable in Make. Tuning the AI prompt to match your own data and tone typically takes a bit longer.