u/itsmakehq

Hello Makers 👋

Companies might be facing an AI direction problem because they lack a clear AI business strategy. Let us explain.

Employees are using AI tools every day, but very few of those habits connect to a documented AI business strategy that ties spend to measurable outcomes.

𝗧𝗵𝗲 𝗴𝗮𝗽 𝘂𝘀𝘂𝗮𝗹𝗹𝘆 𝗰𝗼𝗺𝗲𝘀 𝗱𝗼𝘄𝗻 𝘁𝗼 𝗮 𝗳𝗲𝘄 𝘁𝗵𝗶𝗻𝗴𝘀:
⚠️ No named owner when a pilot’s results start to slip
🕛 Teams reporting hours saved instead of revenue protected or risk reduced
🔍 Results getting compared to an imagined perfect version of AI instead of what the team was actually doing before it arrived, and more

The fix isn’t more AI tools. It’s a leadership-owned strategy that ties spending to outcomes, balances offense (new revenue) with defense (protecting what you have), and gets reassessed every quarter.

𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆, 𝘀𝘁𝗲𝗽 𝗯𝘆 𝘀𝘁𝗲𝗽: https://ma.ke/4xOjHOE

Hello Makers 👋

How long does one invoice take you to process manually? This scenario does it in seconds - automatically.

Our Business Automation and AI Lead, Francisco de Brito Fontes, shows in this tutorial how to build a workflow that monitors a designated Google Drive folder for newly uploaded supplier invoice PDF files, downloads them, extracts key data (invoice number, vendor details, dates, and amounts) using Make's native AI Extractors, and registers the structured information into a monday.com board.

To build this scenario, you will need access to:

✉️ A Google Drive account connected to Make.
🧾 A monday.com account with a Supplier Invoices board configured.
🟣 Access/Credits for Make AI Extractors.

Click the following link to apply the prebuilt scenario in your Make account: https://ma.ke/3U4hujp 

PS: don’t forget to test and validate your scenario, if all modules show a green checkmark, your automation is successful!

Hello Makers 👋

How long does one invoice take you to process manually? This scenario does it in seconds - automatically.

Our Business Automation and AI Lead, Francisco de Brito Fontes, shows in this tutorial how to build a workflow that monitors a designated Google Drive folder for newly uploaded supplier invoice PDF files, downloads them, extracts key data (invoice number, vendor details, dates, and amounts) using Make's native AI Extractors, and registers the structured information into a monday.com board.

To build this scenario, you will need access to:

✉️ A Google Drive account connected to Make.
🧾 A monday.com account with a Supplier Invoices board configured.
🟣 Access/Credits for Make AI Extractors.

Click the following link to apply the prebuilt scenario in your Make account: https://ma.ke/3U4hujp 

PS: don’t forget to test and validate your scenario, if all modules show a green checkmark, your automation is successful!

Hello Makers 👋

Is your company actually adopting AI, or just... using it?

There's a real difference, and it's bigger than it sounds. Recent research shows 37% of organizations are still using AI only at a surface level, with little or no change to existing processes, while just a third are using it to deeply transform how they work.

What is AI adoption and where to start:

AI adoption refers to the process of introducing AI tools and abilities into an organisation: identifying use cases, selecting models and platforms, connecting AI to source systems, training staff, and embedding oversight.

There are 5 maturity stages (per Gartner): 

1. Awareness: the organisation knows about AI but has not used it yet. No pilots or experiments are underway.
2. Active: AI appears in proofs of concept and informal experimentation.
3. Operational: AI is used in at least one workflow.
4. Systemic: AI is present across most workflows and operations, driving new business models.
5. Transformational: AI is built into the core of how the business runs.

Most companies are stuck somewhere between Active and Operational.

What actually moves you forward: in Make terms, it's the moment AI stops living in a chat tab and starts living in a scenario. A trigger passes structured data to a model call, a Router splits confident answers from cases that need human review, and the result gets written back to your systems. That's when "we use AI" becomes "AI does part of the work, reliably, with oversight."

What blocks it: almost never the technology. It's unclear ownership, fragmented data, weak change management, and inconsistent governance. 

The article also has a readiness checklist (data quality, governance, observability, skills coverage) if you want to score where your team sits before scaling anything.

Learn more about AI adoption here: https://ma.ke/4pP6m5S 

Curious, what stage you'd honestly put your organization at - and what's been the biggest blocker to moving past pilots?

Hello Makers 👋 

One of the most-wanted features has arrived in Make: You can now use dark mode 🌚

It covers the Make web platform and scenario builder, giving you a focused visual setup for long build sessions, low-light debugging, and late-night automation.

🌔🌑 Switch between light and dark mode anytime from your personal settings, whichever suits you.

Learn more about it: https://ma.ke/4fDix0W 

Grateful for every piece of feedback that got us here. Thank you 🙏

Hello Makers 👋 

One of the most-wanted features has arrived in Make: You can now use dark mode 🌚

It covers the Make web platform and scenario builder, giving you a focused visual setup for long build sessions, low-light debugging, and late-night automation.

🌔🌑 Switch between light and dark mode anytime from your personal settings, whichever suits you.

Learn more about it: https://ma.ke/4fDix0W 

Grateful for every piece of feedback that got us here. Thank you 🙏

Hello Makers 👋

Are you still manually digging through your emails and database every day? 

Our Startup Program Specialist, Martin Hyravy, shows in this tutorial how to build an AI agent that watches your inbox, filters out the noise, categorizes what's left, and drafts a ready-to-send reply - pulling context from your own knowledge base and your live startup database. 

Every response gets logged for review, so you stay in the loop without doing the digging.

You'll need access to: 

• Gmail 
• Make AI Toolkit (connected to any LLM - Gemini 3.5 in this case) 
• AI Agent module 
• Google Sheets (or your source-of-truth database)

 

Pre-configured scenario: https://ma.ke/4wpgHrJ 

PS: don't forget to test and validate your scenario - if all modules show a green checkmark, your automation is successful!

Prompt:

You'll be my personal assistant and chief of staff and help me to answer the emails from startups that applied to our startup program. 

Before drafting the email, always first look into the previous responses in the Google Sheets repository to analyze my tone of voice, and check whether a similar email has not been solved. 

Before drafting the email, always look into the Startup Database in Google sheets for information about the application. Search with the email address, and look at the Application Status, Rejection Reason, or the coupon code sent.

Only after these two steps proceed with drafting the email, using the draft email tool. 

Make sure that the email responses are always relevant, respectful, and adhere to our team's goals and objectives outlined in the knowledge base files.

Hello Makers 👋

Are you still manually digging through your emails and database every day? 

Our Startup Program Specialist, Martin Hyravy, shows in this tutorial how to build an AI agent that watches your inbox, filters out the noise, categorizes what's left, and drafts a ready-to-send reply - pulling context from your own knowledge base and your live startup database. 

Every response gets logged for review, so you stay in the loop without doing the digging.

You'll need access to: 

• Gmail 
• Make AI Toolkit (connected to any LLM - Gemini 3.5 in this case) 
• AI Agent module 
• Google Sheets (or your source-of-truth database)

 

Pre-configured scenario: https://ma.ke/4wpgHrJ 

PS: don't forget to test and validate your scenario - if all modules show a green checkmark, your automation is successful!

Prompt:

You'll be my personal assistant and chief of staff and help me to answer the emails from startups that applied to our startup program. 

Before drafting the email, always first look into the previous responses in the Google Sheets repository to analyze my tone of voice, and check whether a similar email has not been solved. 

Before drafting the email, always look into the Startup Database in Google sheets for information about the application. Search with the email address, and look at the Application Status, Rejection Reason, or the coupon code sent.

Only after these two steps proceed with drafting the email, using the draft email tool. 

Make sure that the email responses are always relevant, respectful, and adhere to our team's goals and objectives outlined in the knowledge base files.

Hello Makers 👋 

We are super excited to introduce Make AI Sub-Agents!

🤖 Make AI Agents can now delegate tasks to other Make AI Sub-Agents.

That's agent orchestration: one orchestrator receives the request, calls the right agent specialist, and brings back one result.

Here’s what that means:

• Build focused AI agent specialists
• Let one agent choose the right specialist based on the request
• Test and tune each specialist without touching the whole workflow
• Reuse proven agents across different automations

What you can automate with Make AI Sub-Agents:

• Customer support flows: an orchestrator Make AI Agent reads the customer message, sends account questions to a lookup Make AI Agent, refund requests to a refund Make AI Agent, and edge cases to a human handoff agent.
• Finance operations: one Make AI Agent handles incoming requests, then routes document checks, invoice matching, and approval prep to separate specialist Make AI Agents before returning one clear result.
• Internal service desks: route employee requests across access checks, policy answers, and ticket creation, while keeping one AI point of contact for the whole exchange.

How to create Make AI Sub-Agents:

1. Add a Make AI Agent to your scenario → AI agent orchestrator.
2. Inside that Make AI Agent orchestrator, click Add tools and find Make AI Agents in the tools list.
3. Configure the Make AI Agent as a tool and click Add → It will link to your orchestrator as a Sub-Agent.
4. Repeat to add as many Sub-Agents as you need, each one a focused specialist.

Build larger AI workflows without turning a single agent into a catch-all.

Available now on all Make plans, including free.

Try Make AI Agent orchestrator with sub-agents yourself: https://ma.ke/4wX8DOO 

Hello Makers 👋 

We are super excited to introduce Make AI Sub-Agents!

🤖 Make AI Agents can now delegate tasks to other Make AI Sub-Agents.

That's agent orchestration: one orchestrator receives the request, calls the right agent specialist, and brings back one result.

Here’s what that means:

• Build focused AI agent specialists
• Let one agent choose the right specialist based on the request
• Test and tune each specialist without touching the whole workflow
• Reuse proven agents across different automations

What you can automate with Make AI Sub-Agents:

• Customer support flows: an orchestrator Make AI Agent reads the customer message, sends account questions to a lookup Make AI Agent, refund requests to a refund Make AI Agent, and edge cases to a human handoff agent.
• Finance operations: one Make AI Agent handles incoming requests, then routes document checks, invoice matching, and approval prep to separate specialist Make AI Agents before returning one clear result.
• Internal service desks: route employee requests across access checks, policy answers, and ticket creation, while keeping one AI point of contact for the whole exchange.

How to create Make AI Sub-Agents:

1. Add a Make AI Agent to your scenario → AI agent orchestrator.
2. Inside that Make AI Agent orchestrator, click Add tools and find Make AI Agents in the tools list.
3. Configure the Make AI Agent as a tool and click Add → It will link to your orchestrator as a Sub-Agent.
4. Repeat to add as many Sub-Agents as you need, each one a focused specialist.

Build larger AI workflows without turning a single agent into a catch-all.

Available now on all Make plans, including free.

Try Make AI Agent orchestrator with sub-agents yourself: https://ma.ke/4wX8DOO 

Hello Makers 👋

Ever wanted to test a scenario without your whole team seeing your messy work-in-progress? That's exactly what Private spaces are for.

What they are: Every member of your org (except guests) gets their own personal workspace for scenarios and connections that teammates can't access. 

How it works:

• Admins enable it once from Organization settings → Private spaces, and every member automatically gets one
• Admins can set credit limits per space (monthly or yearly), or leave them unlimited so members draw from the org's overall credits
• Hit your limit? Your scenarios pause - you can keep building, but they won't run until the limit resets or an admin bumps it up

For Enterprise orgs: Admin visibility lets admins open any member's private space in read-only mode. They can see scenario names, modules used, and credit usage - but NOT module settings, run history, connections, or data stores. Your credentials stay yours.

One thing to know: disabling private spaces for the org permanently deletes all of them - scenarios, connections, data stores included. So heads up before flipping that toggle.

Discover more information about private spaces: https://ma.ke/4fi1NNP

Anyone already using their private space for experiments? Curious what you're building in there 👀

Hello Makers 👋 

You have now available the new Claude models in Make AI Provider! Here's what you get out of it:

• Zero setup: Pick a Claude model the same way you already pick OpenAI models, no Anthropic account or API key needed.
• One bill, one system: Claude usage runs on Make’s own Anthropic key and gets billed straight through your Make credits.
• The latest Claude models: Pick from Anthropic’s latest Claude models right inside Make, kept current as Anthropic ships new releases, no waiting on a separate integration update.
• Model choice, your call: Match the model to the task, strong reasoning, sharper writing, or steadier agent behavior, all without leaving the builder.
• Faster first use: Skip the account creation and key handling that used to stand between you and Claude-quality output.

Example: an agency ships Claude-powered workflows for every client, no separate Anthropic account per client needed.

If you’re new to configuring Make AI Provider, here’s how to set it up in Make AI Agent:

1. Add a Run an agent module (Make AI Agent) to your scenario.
2. Set the Connection to Make AI Provider: no separate Anthropic account or API key to create.
3. In the Model field, pick a Claude model from the dropdown.
4. Under Instructions, describe the agent’s job, for example: “Read incoming support tickets, identify the issue, and draft a helpful reply.”
5. Map your Input (the ticket text, email body, or whatever the agent should act on), then run the scenario.

Pick the model. Build the workflow. Put it to work.

Reminder: Claude joins the OpenAI GPT AI models already available in Make AI Provider, giving you more model choice without leaving the builder.

Try it out at: make.com 

Hello Makers 👋

Today we’re sharing a feature that will help you better understand what happens when your automation breaks.

Now in Make, when a workflow hits an error, one click turns the technical message (HTTP codes, API messages, raw JSON) into a plain-language explanation of what happened and exactly what to fix.

No documentation search. No pasting error text into another AI tool. Just click, and know what to do next.

In early testing, this got people back to a working automation about 20% more often after an error.

Live now for every Make user, on every plan. Learn more: https://ma.ke/3RINDwa

Hello Makers 👋

GPT-5.6 is now in Make, with Sol, Terra, and Luna built for routing your automations by job:

☀️ Sol: review long contracts, plan multi-step work, support coding tasks
🌍 Terra: summarize docs, reason across notes, return clean JSON or XML
🌙 Luna: classify tickets, extract IDs, validate inputs

Use Luna for high-volume steps, Terra for structured business logic, and Sol when the context gets big.

Available now to all Make users in Make AI Agents, Make AI Toolkit, and the OpenAI ChatGPT app.

Not every AI step needs the same brain 🧠

Read more on what's new with GPT-5.6 in Make: https://ma.ke/4f8oVyk 

Hello Makers 👋

GPT-5.6 is now in Make, with Sol, Terra, and Luna built for routing your automations by job:

☀️ Sol: review long contracts, plan multi-step work, support coding tasks
🌍 Terra: summarize docs, reason across notes, return clean JSON or XML
🌙 Luna: classify tickets, extract IDs, validate inputs

Use Luna for high-volume steps, Terra for structured business logic, and Sol when the context gets big.

Available now to all Make users in Make AI Agents, Make AI Toolkit, and the OpenAI ChatGPT app.

Not every AI step needs the same brain 🧠

Read more on what's new with GPT-5.6 in Make: https://ma.ke/4f8oVyk 

If you rely on the platform, we know how disruptive that will be. You’ve built real business logic, and rebuilding it isn't a small thing.

𝗧𝗵𝗮𝘁'𝘀 𝘄𝗵𝘆 𝘄𝗲'𝗿𝗲 𝗼𝗳𝗳𝗲𝗿𝗶𝗻𝗴 𝗥𝗲𝗹𝗮𝘆.𝗮𝗽𝗽 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗲𝘅𝘁𝗿𝗮 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝘁𝗼 𝗺𝗼𝘃𝗲 𝘁𝗼 𝗠𝗮𝗸𝗲:
→ Free white-glove migration for eligible accounts - our team helps rebuild your workflows
→ Priority support to get you to production fast
→ 3 live Office Hours sessions with our Customer Care team

Learn why Make is a strong alternative, and how to start your transition: https://ma.ke/4vAEe7O

Hello Makers 👋

Take a look at some of our latest features, improvements, and updates that our team has been shipping this past month:

Enhancements and Updates:

• MCP Tools to Make AI Agents: Make AI Agents can now access external MCP server tools alongside native Make modules in the same run. 
• Execute an action with AI module for MCP: choose Make AI Provider (OpenAI or Claude) or your own OpenAI account for the MCP Client's 'Execute an action with AI' module.
• New functions on Make: four new functions - arrayDiff, arrayIntersect, set, escapeJSON - for cleaner, lighter expressions.
• Claude Opus 4.8: Anthropic's latest model is now in Make. Anthropic says early testers saw steadier judgment and more reliable behavior during agentic tasks, especially when the work kept going.
• Unified Help Search in Make: one help search across Make. Cmd+K to access it anywhere.
• Star apps and app modules in App Search: star your go-to apps and modules so the builder feels like your workspace, not a directory. The goal is simple: spend more time building logic and less time digging through lists.
• Google Drive with your personal Gmail account: Google Drive connections now work with @ gmail.com accounts, not just Workspace.
• Configuring disconnected modules: run tests without hardcoded data, explore apps, and overall build with more control.
• Webhook data structure: a new status card shows if your webhook is listening, has received data, or still needs a test request.
• Copy notes when cloning a scenario: cloned scenarios now carry over notes too, matching export/import behavior.

New Verified Apps

Air, Arbox v3, BackgroundErase, BoldDesk, ChatterFox, CoinGecko, CPV Lab, CSV Box, Databricks, Daytona, DocuPotion, Eden AI, Figuro Engage, HEU, Houdiny AI, Mihu AI, MillionVerifier, NextLead CRM, Pingram, Postproxy, SMSlink Pacific, Tiiny Host, Trading Automation For Alpaca, Watchflow

New Modules

GitHub

• Make an API Call

Monday

• Chat Completion
• Run Platform Agent

Wix

• Watch Events

Discover more about our latest releases: https://ma.ke/4yqy6S4