Make.com AI Scenarios: 7 Practical Templates for Small Teams

By Alex Mercer · Last reviewed 2026-10-08

These make.com ai templates are seven scenario structures a small team can build in an afternoon, each with a use case, a rough credit count per run and a plan for when something breaks. Make's official template gallery shows you what is possible; this list explains when each pattern is worth using. It is written for non-developers who already have a Make account or are about to open one.

How this list was chosen

We picked jobs that small teams repeat every week and that need a bit of judgement, which is where an AI step earns its place: sorting, summarising, drafting and extracting. Each template uses common apps such as Gmail, Google Drive, Google Sheets, Slack and Airtable.

Credit estimates follow Make's help-center rules as of October 2026: a trigger uses one credit per run, and each action module uses one credit per bundle it processes. The estimates assume one bundle per step and your own OpenAI connection, where Make charges credits for the operation and OpenAI bills tokens separately. If you use Make's AI Provider instead, AI steps are priced by tokens and can use more than one credit. These are desk-research estimates, not measured runs.

The list: make.com ai templates

1. Inbox triage to Slack

Use case: a shared support or sales inbox gets mixed messages. Structure: new-email trigger (Gmail) → OpenAI step that labels the email (Sales, Support, Spam) and writes a one-line summary → Router → Slack message to the right channel. Credits per run: about 4. Failure handling: add an error handler on the OpenAI module that posts "needs manual review" to a fallback channel, so nothing disappears silently.

2. Lead enrichment into Airtable

Use case: website form leads arrive with messy free-text fields. Structure: form or webhook trigger → OpenAI step that extracts company, role and request type as JSON → a step that reads those JSON fields → new Airtable record. Credits per run: about 4. Failure handling: if parsing fails, write the raw text to a "Needs review" view in Airtable instead of dropping it.

3. Meeting notes summariser from Google Drive

Use case: someone saves a transcript after each call and nobody reads it. Structure: new-file trigger on a Google Drive folder → get the file's text → OpenAI step that summarises decisions and action items → Slack or email to attendees. Credits per run: about 4. Failure handling: long transcripts can exceed what you want to pay for; add a filter that skips files above a set size and flags them.

4. Draft replies to common questions

Use case: the same five customer questions come in every day. Structure: new-email trigger (Gmail) → OpenAI step with your FAQ text in the prompt → save the reply as a Gmail draft (not send). Credits per run: about 3. Failure handling: keep a human in the loop by only creating drafts. A person reviews and sends.

5. Social post repurposing

Use case: turn each new blog post into short social drafts. Structure: new-item trigger on an RSS feed → OpenAI step that writes three post variants → new Google Sheets row for review. Credits per run: about 3. Failure handling: store drafts in a sheet with a "status" column so nothing posts without approval.

6. Invoice and receipt data extraction

Use case: receipts land in a folder and someone types the totals into a spreadsheet. Structure: new-file trigger on a Google Drive folder → get the receipt's text → OpenAI step that returns vendor, date and total → new Google Sheets row. Check in Make's OpenAI app documentation whether the module you pick can take a file directly; if not, pass it extracted text. Credits per run: about 3, plus any download or conversion step. Failure handling: add a check that the total is a number; if not, mark the row for review. This is bookkeeping support, not financial advice, so a person should still check the figures.

7. Weekly digest of Slack channels

Use case: managers want a Friday summary without scrolling. Structure: scheduled trigger → fetch the week's messages from a Slack channel → combine them into one block of text → OpenAI summary → Slack post to a leadership channel. Credits per run: depends on bundles. Listing 200 messages returns 200 bundles, so any module that runs per message uses about 200 credits. Aggregating before the AI step keeps the AI call to one. Failure handling: cap the number of messages fetched and log when the cap is hit.

Comparison at a glance

Make

Each scenario runs inside Make's canvas. Routers and filters let one template handle several outcomes. On the Free plan you can only have two scenarios active, so choose your two most valuable templates first.

OpenAI

Make's OpenAI app needs an API key and an Organization ID from your OpenAI account, plus credit on that account. Use a small model for classification and short summaries; you can see what that costs per message in our AI automation cost breakdown. Ask the model for JSON when a later step needs to read specific fields.

Airtable

Airtable works well as the destination when you want views, filters and statuses for review. Google Sheets is simpler and free, so start there if your team already lives in Sheets.

How to pick the right one for you

Start with the template that removes the most manual copying each week. For most small teams that is inbox triage or lead enrichment.

Slack

If your team already talks in Slack, templates 1, 3 and 7 deliver results where people will see them. Keep AI messages in a dedicated channel at first so the team can judge quality.

Google Drive

If files are your pain point, templates 3 and 6 are the fit. Put the watched folder somewhere specific so unrelated files don't trigger runs and burn credits.

If you are still deciding whether Make is the right platform at all, our Zapier vs Make vs n8n comparison covers how each tool counts usage. Teams whose main bottleneck is booking rather than inbox work may get more from an AI scheduling assistant.

What to skip

• Auto-sending AI replies to customers without review. Drafts are safer until you trust the output.
• Running scenarios every minute when hourly is fine. Each scheduled check uses credits even when it finds nothing new.
• Feeding whole documents into the prompt when a short excerpt will do. Tokens cost money on the OpenAI side.
• Sending sensitive client data to an AI provider before checking your contracts and the provider's data terms.

FAQ

Is Make.com free?

Make has a Free plan with 1,000 credits a month, as of October 2026. It limits you to two active scenarios and a 15-minute minimum interval between scheduled runs, so it is best for testing.

How do I connect ChatGPT to Make?

Add the OpenAI app to a scenario and create a connection with an API key and Organization ID from your OpenAI account, which also needs prepaid credit. Make's Simple text prompt module can be tried without your own key.

What counts as an operation in Make?

An operation is one module run that processes or checks data, and operations consume credits, Make's billing unit. Most operations use one credit; a trigger uses one credit per run, and an action uses one credit per bundle it processes.

Conclusion

Build template 1 or 2 first, run it on the Free plan for a week, and count credits in the scenario history. If the AI output is reliable and the credit count fits, move to a paid plan and add the next template. Paid Make plans start at $9 a month for 10,000 credits with monthly billing, per Make's pricing page in October 2026.

Sources and methodology

Desk research of official Make and OpenAI documentation, last reviewed 7 October 2026. No hands-on tests.

• Make pricing: https://www.make.com/en/pricing
• Make help center, credits: https://help.make.com/credits
• Make help center, introducing credits: https://help.make.com/introducing-credits-new-billing-unit-live-in-make
• Make OpenAI app documentation: https://apps.make.com/openai-gpt-3